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UNIVERSITÉ
DE GENÈVE

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UNIVERSITÉ
DE GENÈVE
FACULTÉ DE MÉDECINE

Section de *médecine clinique*
Département d'anesthésiologie,
pharmacologie, soins intensifs et urgences
Service d'anesthésiologie

Thèse préparée sous la direction du Professeur Eduardo Schiffer

" LA SURVEILLANCE CONTINUE DE LA GLYCÉMIE
EN PÉRIODE PÉRIOPÉRATOIRE
EN CHIRURGIE NON CARDIAQUE :
UNE REVUE SYSTÉMATIQUE "

Thèse
présentée à la Faculté de Médecine
de l'Université de Genève
pour obtenir le grade de Docteur en médecine
par

Alessandro PUTZU
de
Italie

Thèse n° 11315

Rédaction de la thèse à partir d'une publication originale.

Article publié dans l'European Journal of Anaesthesiology (éditeur : Wolters Kluwer),
en ligne le 7 novembre 2024 et en version imprimée le 1er février 2025.

Reference complète : Putzu A, Grange E, Schorer R, Schiffer E, Gariani K. Continuous peri-operative glucose monitoring in noncardiac surgery: A systematic review. Eur J Anaesthesiol. 2025 Feb 1;42(2):162-171. doi: 10.1097/EJA.00000000000002095. Epub 2024 Nov 7.
# DOCTORAT EN MEDECINE

Thèse de :

Alessandro PUTZU

originaire d'Italie

Intitulée :

**La surveillance continue de la glycémie en période périopératoire en chirurgie non cardiaque : une revue systématique**

La Faculté de médecine, sur le préavis du Comité directeur des thèses, autorise l'impression
de la présente thèse, sans prétendre par-là émettre d'opinion sur les propositions qui y sont
énoncées.

Genève, le 1er décembre 2025

Thèse n° 11315

Antoine Geissbühler
Doyen

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TABLE DES MATIÈRES

<table><tr><td>1. RÉSUMÉ</td><td>4</td></tr><tr><td>2. INTRODUCTION</td><td>5</td></tr><tr><td>2.1. Importance du contrôle glycémique périopératoire</td><td>5</td></tr><tr><td>2.2. Prévalence de l'hyperglycémie périopératoire</td><td>7</td></tr><tr><td>2.3. Prévalence périopératoire du diabète</td><td>8</td></tr><tr><td>2.4. Surveillance et prise en charge glycémique en période périopératoire</td><td>9</td></tr><tr><td>2.5. Prise en charge glycémique en période périopératoire</td><td>9</td></tr><tr><td>2.6. Surveillance continue de la glycémie (CGM)</td><td>11</td></tr><tr><td>2.7. Principaux indicateurs glycémiques dérivés des CGM</td><td>13</td></tr><tr><td>2.8. Conflits d'intérêts financiers et CGM</td><td>14</td></tr><tr><td>2.9. Utilisation des CGM en contexte périopératoire</td><td>14</td></tr><tr><td>2.10. Défis pratiques de l'utilisation des CGM en période périopératoire</td><td>16</td></tr><tr><td>2.11. Portée de la revue systématique</td><td>17</td></tr><tr><td>3. MANUSCRIT</td><td>18</td></tr><tr><td>3.1 Texte principal</td><td>18</td></tr><tr><td>3.2. Matériel supplémentaire</td><td>29</td></tr><tr><td>4. DISCUSSION</td><td>47</td></tr><tr><td>5. CONCLUSIONS</td><td>49</td></tr><tr><td>6. BIBLIOGRAPHIE</td><td>50</td></tr><tr><td>REMERCIEMENTS</td><td>57</td></tr></table>
GLOSSAIRE

CGM - Continuous glucose monitoring

CV - Coefficient of Variation

ERC - Essai Randomisé et Contrôlé

MINS - Myocardial Injury After Non-cardiac Surgery

POC – Point-Of-Care

TAR – Time Above Range

TBR – Time Below Range

TIR – Time In Range
# 1. RÉSUMÉ

La gestion de la glycémie constitue un enjeu majeur de la médecine périopératoire moderne.

La dysglycémie périopératoire après une chirurgie non cardiaque est fréquente, tant chez les patient-es diabétiques que non diabétiques, et est associée à des issues cliniques défavorables.

La surveillance glycémique en continu (CGM, *Continuous Glucose Monitoring*) est de plus en plus utilisée en ambulatoire chez les patient-es atteint-es de diabète de type 1 et 2. Les professionnels de santé sont de plus en plus amenés à se confronter à ces dispositifs dans leur pratique, et leur utilisation a été proposée dans le contexte périopératoire. Toutefois, l'utilité de la CGM en chirurgie non cardiaque reste incertaine.

Cette thèse présente une revue systématique de la littérature sur l'utilisation des CGM en période périopératoire dans le cadre de la chirurgie non cardiaque. Ont été incluses toutes les études rapportant l'utilisation d'un dispositif CGM en peropératoire ou en postopératoire. Les critères de jugement principaux concernaient les paramètres de contrôle glycémique périopératoire obtenus via les CGM. Les critères secondaires incluaient les événements cliniques majeurs postopératoires, et les critères tertiaires portaient sur les effets indésirables liés au dispositif ainsi que sur les dysfonctionnements techniques.

Vingt-six études, regroupant un total de 1016 patient-es, ont été incluses. Parmi elles, 24 étaient non randomisées et six comportaient un groupe contrôle. Les profils glycémiques rapportés variaient largement selon le type de chirurgie et le statut diabétique des patient-es. En raison du nombre très limité d'études contrôlées, il n'a pas été possible d'évaluer l'impact des CGM sur le contrôle glycémique postopératoire ni sur le taux de complications comparativement aux stratégies alternatives, telles que la glycémie capillaire au point de soin.

Les événements indésirables liés aux CGM étaient rares et généralement bénins, tandis que des dysfonctionnements techniques du dispositif ont été observés dans approximativement un cas sur dix.
À notre connaissance, au moment de la réalisation de cette étude, il s'agissait de la première revue systématique consacrée aux CGM en chirurgie non cardiaque. La grande variabilité des profils glycémiques postopératoires, associée à un taux non négligeable de dysfonctionnements des dispositifs, suggère que la CGM mérite une évaluation approfondie dans des populations chirurgicales sélectionnées. Compte tenu de la rareté des essais contrôlés, son efficacité comparée à la surveillance glycémique classique au point de soin sur la gestion glycémique postopératoire et les complications reste à déterminer.

## 2. INTRODUCTION

### 2.1. Importance du contrôle glycémique périopératoire

La gestion optimale de la glycémie constitue un élément clé de la prise en charge périopératoire. De nombreuses données mettent en évidence l'association entre la dysglycémie périopératoire – incluant l'hyperglycémie, l'hypoglycémie et une variabilité glycémique excessive – et une morbi-mortalité accrue dans cette période critique.

Durant la période périopératoire, l'hyperglycémie est fréquente chez les patient-es diabétiques, mais également en cas d'hyperglycémie de stress chez les patient-es sans antécédents connus de diabète. La sévérité de l'hyperglycémie périopératoire dépend de plusieurs facteurs tels que le type de chirurgie, le contrôle glycémique antérieur, ou la survenue de complications (1). De nombreuses études ont démontré une association entre hyperglycémie périopératoire et événements indésirables : prolongation de la durée d'hospitalisation, infections, complications cardiovasculaires et augmentation de la mortalité, aussi bien chez les patient-es diabétiques que non diabétiques (2–5).
Une large étude observationnelle prospective a montré que une glycémie préopératoire élevée, en particulier la glycémie plasmatique aléatoire, prédisait les complications cardiovasculaires postopératoires chez des patient-es sans diabète (5). Cette étude incluait 11 954 patient-es, dont 23,5 % étaient diabétiques ; 6,8 % ont présenté une lésion myocardique postopératoire après une chirurgie non cardiaque (MINS, *Myocardial Injury after Non-cardiac Surgery*) et 2,1 % sont décédé-es dans les 30 jours. Bien que l'incidence de MINS et de décès ait été plus élevée chez les patient-es diabétiques, les glycémies aléatoires ont prédit la survenue de MINS dans toute la cohorte, et la mortalité à 30 jours uniquement chez les non-diabétiques. Chez les patient-es non diabétiques, une glycémie aléatoire > 6,86 mmol/L et une glycémie à jeun > 6,41 mmol/L étaient des facteurs prédictifs indépendants de MINS ; en revanche, chez les patient-es diabétiques, seule une glycémie aléatoire > 7,92 mmol/L avait une valeur prédictive (5). Une autre étude de cohorte a montré que les patient-es non diabétiques présentaient de moins bons résultats que les diabétiques à niveaux similaires d'hyperglycémie (6). Toutefois, ces résultats doivent être interprétés avec prudence, car une part non négligeable de la population hyperglycémique pourrait inclure des patient-es présentant un diabète non diagnostiqué.

L'hypoglycémie est associée à des déficits cognitifs, des événements cardiovasculaires, voire à une mortalité accrue (7). Bien que l'hypoglycémie périopératoire ait également été impliquée dans des issues postopératoires défavorables (8, 9), les données restent principalement limitées à la chirurgie cardiaque et aux patient-es en soins intensifs (10-12). Plusieurs facteurs de risque de survenue d'hypoglycémie périopératoire ont été identifiés : jeûne prolongé, glycémie à jeun basse en préopératoire, diabète évoluant depuis plus de 10 ans, indice de masse corporelle faible, utilisation de sulfamides hypoglycémiants, antécédents d'hypoglycémie, âge avancé, administration préopératoire d'insuline sous-cutanée et durée opératoire prolongée (13-15).
Une variabilité glycémique accrue, définie par des fluctuations importantes de la glycémie, est également associée à un risque accru de complications périopératoires (16, 17). Ce lien pourrait s'expliquer par l'activation d'un stress oxydatif plus marqué en cas de fluctuations extrêmes que dans le cadre d'une hyperglycémie chronique et soutenue (18).

De manière générale, les données issues de différentes populations indiquent que la dysglycémie est associée à un pronostic clinique défavorable, aussi bien chez les patient-es diabétiques que non diabétiques (2–4, 9). Le suivi glycémique périopératoire constitue donc un paramètre essentiel, non seulement chez les patient-es diabétiques, mais également chez les sujets sans diabète mais à risque accru de complications postopératoires.

## 2.2. Prévalence de l'hyperglycémie périopératoire

La fréquence de l'hyperglycémie périopératoire dépend principalement de facteurs liés au patient, au type d'intervention chirurgicale et à la stratégie de dépistage mise en place. Dans une large cohorte de patient-es diabétiques et non diabétiques ayant bénéficié d'interventions générales, bariatriques, vasculaires ou rachidiennes, seulement 47 % ont bénéficié d'un contrôle glycémique périopératoire – 84 % des diabétiques et 38 % des non-diabétiques.

Parmi celles et ceux qui ont été testé-es, 18 % ont présenté une hyperglycémie périopératoire (glycémie > 10 mmol/L) : 40 % des patient-es diabétiques et 6 % des non-diabétiques (19).

D'autres études ont également souligné la fréquence élevée d'hyperglycémie chez les patient-es sans diagnostic préalable de diabète. Une étude portant sur 1 971 patient-es non diabétiques opéré-es en chirurgie générale a montré que 10 % développaient une hyperglycémie de stress (glycémie > 10 mmol/L) dans les 48 premières heures postopératoires (20). La prévalence varie toutefois en fonction de la spécialité chirurgicale, du contexte clinique et des comorbidités. Par exemple, une hyperglycémie de stress
(glycémie > 7 mmol/L) a été observée chez jusqu'à 38 % des patient-es hospitalisé-es pour fracture de hanche (21).

Une large étude de cohorte rétrospective monocentrique a mis en évidence une variation substantielle de l'incidence de l'hyperglycémie et du recours à l'insulinothérapie chez les patient-es diabétiques et non diabétiques ayant subi une chirurgie non cardiaque. Parmi les non-diabétiques, 23 % ont développé une hyperglycémie (glycémie > 10 mmol/L), et 61 % d'entre eux ont reçu de l'insuline selon le protocole institutionnel. En comparaison, 74 % des patient-es diabétiques ont développé une hyperglycémie et 91 % ont été traité-es par insuline (6).

## 2.3. Prévalence périopératoire du diabète

Le diabète constitue un enjeu croissant de santé publique, avec une estimation de 536 millions d'adultes vivant avec cette pathologie dans le monde en 2019, soit une prévalence globale de 10,5 % (22). La prévalence du diabète est plus élevée chez les patient-es chirurgicaux que dans la population générale, ce qui reflète un recours accru à la chirurgie chez les personnes présentant des complications liées au diabète. Cette prévalence varie également selon les spécialités chirurgicales. Une méta-analyse de 90 études portant sur plus de 800 000 interventions a rapporté une prévalence globale du diabète de 17 %, les taux les plus élevés étant observés en chirurgie cardiovasculaire, suivie de la chirurgie orthopédique (23). Un domaine encore peu exploré est la prévalence du diabète non diagnostiqué, estimée à 7,4 % chez les patient-es en période périopératoire (24), bien que ces taux soient probablement variables selon les populations étudiées, les pays et les systèmes de santé.
## 2.4. Surveillance et prise en charge glycémique en période périopératoire

La gestion glycémique périopératoire en chirurgie non cardiaque s'appuie essentiellement sur des mesures intermittentes en point de soin (POC, Point-Of-Care), effectuées par prélèvements capillaires digitaux, ainsi que sur des protocoles standardisés d'insulinothérapie. Bien que ces approches aient permis de réduire l'incidence des dysglycémies sévères et leurs complications, tout en restant largement accessibles à faible coût, elles présentent d'importantes limites pratiques et cliniques (25). Par exemple, la surveillance fréquente représente une charge de travail considérable : chaque test POC peut mobiliser jusqu'à cinq minutes (26). En outre, la répétition des prélèvements capillaires par piqûre au doigt ou des ponctions veineuses peut s'avérer inconfortable pour les patient-es. Dans certains contextes périopératoires tels que un œdème périphérique ou une hypotension sévère, la fiabilité des dispositifs peut être altérée (27-31). Chez les patient-es porteurs de cathéters artériels ou centraux, ou lorsque les mesures capillaires sont jugées peu fiables, des échantillons artériels ou veineux peuvent être analysés via des analyseurs de gaz du sang au lit du patient. La glycémie plasmatique mesurée au laboratoire central reste la référence pour confirmer des valeurs inattendues ou critiques – par exemple en cas de suspicion de dysglycémie sévère ou d'état de choc – mais le délai d'analyse limite son utilité pour le suivi fréquent peropératoire ou en phase postopératoire immédiate (25).

## 2.5. Prise en charge glycémique en période périopératoire

Il n'existe actuellement aucun consensus international sur les objectifs glycémiques à atteindre en période périopératoire. Certaines recommandations professionnelles préconisent de maintenir la glycémie en dessous de 10,0 mmol/L, d'autres en dessous de 12,0 mmol/L, tandis que certaines ne précisent aucun seuil cible (32-35). Toutefois, une plage glycémique optimale d'environ 6,0 à 10,0 mmol/L est largement acceptée.
Certaines études ont évalué des cibles glycémiques plus strictes (36). Chez les patient-es ayant subi une chirurgie cardiaque, un traitement intensif par insuline n'a pas permis de réduire la mortalité ni la morbidité périopératoire. Au contraire, les taux plus élevés de décès et d'AVC observés dans le groupe recevant un traitement intensif ont soulevé des doutes quant à la sécurité de cette approche (37). De plus, un contrôle glycémique strict augmente le risque d'hypoglycémies modérées ou sévères, toutes deux associées à une mortalité accrue, en particulier en contexte de soins critiques (36, 38).

Une surveillance régulière de la glycémie le jour de l'intervention et pendant la période peropératoire favorise une gestion proactive de la dysglycémie. La surveillance doit débuter dès l'admission en zone de préparation préopératoire et se poursuivre jusqu'à la sortie de la salle de surveillance post-interventionnelle. Le suivi et le traitement concernent principalement les patient-es diabétiques, en particulier celles et ceux recevant de l'insuline sous-cutanée ou intraveineuse. Une prise en charge hospitalière de qualité du diabète repose sur des standards clairs, applicables, transmis via des ordonnances structurées et intégrés dans une démarche d'amélioration continue de la qualité (39).

Le sous-groupe de patient-es non diabétiques nécessitant une surveillance glycémique rapprochée reste à définir (40). Il est donc essentiel d'identifier les patient-es à risque et de mettre en œuvre une approche proactive et individualisée.

La prise en charge glycémique en périopératoire implique un équilibre délicat entre le risque d'hyperglycémie et celui d'hypoglycémie. La correction glycémique peut être obtenue par administration sous-cutanée d'analogues d'insuline rapide ou par perfusion intraveineuse d'insuline rapide. Le choix de la voie d'administration dépend de la complexité de l'intervention et de l'état du patient. Les procédures courtes, avec stabilité hémodynamique attendue et sans variations liquidiennes majeures, peuvent être prises en charge par des bolus
sous-cutanés correcteurs toutes les deux à quatre heures. En revanche, les chirurgies majeures, associées à des fluctuations hémodynamiques, à des transferts hydriques importants ou à une réponse inflammatoire marquée, peuvent nécessiter une prise en charge spécifique (32).

## 2.6. Surveillance continue de la glycémie (CGM)

Les avancées technologiques en diabétologie ont conduit au développement de dispositifs portables, en particulier les systèmes de surveillance continue de la glycémie (CGM, *Continuous Glucose Monitoring*) (Figure 1). L'introduction de la CGM a transformé la prise en charge du diabète (41). Ces dispositifs visent à prévenir l'hyperglycémie et l'hypoglycémie, tout en ciblant intrinsèquement un contrôle glycémique optimal.

<figure><img src="image_4.png" /><figcaption>**Figure 1** – Exemple de dispositif CGM (FreeStyle Libre, Abbott). Source : Sjö, BGM_twonpart.JPG, Wikimedia Commons, licence CC BY-SA 4.0, consulté le 14 octobre 2025.</figcaption></figure>

Les systèmes CGM permettent de dépasser les limites de la surveillance capillaire intermittente en fournissant des mesures quasi continues du glucose interstitiel. Plusieurs modèles sont actuellement commercialisés, et le développement technologique dans ce
domaine reste très actif (Tableau 1). De nombreuses études ont montré l'avantage des CGM par rapport à l'autosurveillance glycémique, principalement dans le diabète de type 1, mais aussi chez certain-es patient-es atteints de diabète de type 2 (42, 43).

<table><caption>Tableau 1 – Sélection de dispositifs CGM commercialisés. Adapté de (44).</caption><thead><tr><td>Dispositif</td><td>Fabricant</td><td>Temps de calibration</td><td>Fréquence des lectures</td><td>Durée de port</td></tr></thead><tbody><tr><td>IPro2</td><td>Medtronic<br>Minimed</td><td>60 minutes</td><td>Toutes les 5 minutes</td><td>6 jours</td></tr><tr><td>Dexcom G7</td><td>Dexcom Inc.</td><td>30 minutes</td><td>Toutes les 5 minutes</td><td>10 jours</td></tr><tr><td>Freestyle<br>Libre 3</td><td>Abbott Diabetes Care<br>Inc.</td><td>60 minutes</td><td>Toutes les 5 minutes</td><td>14 jours</td></tr><tr><td>Eversense E3</td><td>Senseonics Inc.</td><td>2 glycémies<br>capillaires/jour</td><td>Toutes les 5 minutes</td><td>180 jours (implant)</td></tr></tbody></table>

L'intérêt pour l'utilisation des CGM en dehors du cadre ambulatoire est croissant. Leur précision, comparée aux tests en point of care, s'est révélée satisfaisante chez les patient-es hospitalisé-es, y compris en soins critiques (45, 46). Diverses stratégies d'implémentation ont été proposées en unité de soins intensifs (46), bien que l'évolution de la gravité clinique impose une réévaluation constante des performances des capteurs (31, 47).

Une large revue de portée sur 96 études évaluant la précision des CGM en soins intensifs a mis en évidence une hétérogénéité considérable dans les critères de précision rapportés (48). La précision variait en fonction du système CGM et de la méthode de référence utilisée ; les CGM intravasculaires montraient généralement de meilleures performances que les dispositifs sous-cutanés, et leur fiabilité semblait plus faible en situation d'hypoglycémie (48). Cependant, ces études, souvent anciennes, ont été réalisées auprès de patient-es en état critique, dans un cadre clinique difficilement transposable au contexte périopératoire.
## 2.7. Principaux indicateurs glycémiques dérivés des CGM

Les indicateurs standardisés issus des CGM permettent une évaluation cohérente à la fois du contrôle glycémique moyen et de la variabilité glycémique (Tableau 2). Le pourcentage de valeurs dans la cible (TIR, *Time In Range*) reflète le contrôle global, tandis que les pourcentages au-dessus (TAR, *Time Above Range*) et en dessous (TBR, *Time Below Range*) quantifient respectivement l'exposition à l'hyperglycémie et à l'hypoglycémie.

Le coefficient de variation (CV, *Coefficient of Variation*) fournit une mesure sans unité de la variabilité glycémique, indépendante de la glycémie moyenne. Le consensus international de 2019 recommande, chez les adultes non chirurgicaux atteints de diabète de type 1 ou 2, les cibles suivantes : TIR > 70 % (3,9–10 mmol/L), TAR < 25 % (> 10 mmol/L), TBR < 4 % (< 3,9 mmol/L), et CV < 36 %, afin de minimiser à la fois les excursions glycémiques et le risque d'hypoglycémie (49, 50). Ces objectifs sont adaptés pour les patient-es à haut risque (50). De nombreuses données issues de populations non chirurgicales ont démontré que ces indicateurs sont prédictifs de résultats cliniquement significatifs, tels que les événements cardiovasculaires ou la mortalité. En revanche, les données équivalentes chez les patient-es en période périopératoire demeurent limitées, soulignant la nécessité urgente d'études bien conçues pour valider ces paramètres dans un contexte chirurgical.

<table><thead><tr><td>Indicateur glycémique dérivé des CGM</td><td>Définition</td></tr></thead><tbody><tr><td>Time In Range (TIR)</td><td>Pourcentage de mesures CGM et de temps (heures/minutes) pendant une journée de 24 h avec une glycémie dans l'intervalle cible de 3,9 à 10 mmol/L</td></tr><tr><td>Time Above Range (TAR)</td><td>Pourcentage de mesures CGM et de temps (heures/minutes) pendant une journée de 24 h avec une glycémie > 10 mmol/L</td></tr></tbody></table>
<table><caption>Tableau 2 – Indicateurs glycémiques standardisés issus de la CGM. Adapté de (50).</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Time Below Range (TBR)</td><td>Pourcentage de mesures CGM et de temps (heures/minutes) pendant une journée de 24 h avec une glycémie &lt; 3,9 mmol/L</td></tr><tr><td>Mean glucose</td><td>Moyenne calculée des glycémies mesurées quotidiennement</td></tr><tr><td>Coefficient of Variation (CV)</td><td>Mesure de la variabilité glycémique intrajournalière dérivée des CGM. Le CV est exprimé en pourcentage : écart-type divisé par la glycémie moyenne</td></tr></tbody></table>

## 2.8. Conflits d'intérêts financiers et CGM

Les conflits d'intérêts financiers et le coût des dispositifs représentent des enjeux majeurs dans le domaine des CGM. Plusieurs études ont montré que les études sponsorisées par l'industrie ou en situation de conflit d'intérêts rapportaient des conclusions plus favorables (51, 52). Certains revues systématiques ont spécifiquement évalué la présence de financements issus de l'industrie des CGM ou de conflits d'intérêts (53, 54). Notamment, une large revue de portée incluant 129 études a révélé que 58 % d'entre elles étaient au moins partiellement financées par des fabricants de CGM (55). Le soutien financier de l'industrie soulève des préoccupations concernant le biais potentiel dans la conception des études, leur réalisation et l'interprétation des résultats. Par ailleurs, le coût élevé des dispositifs CGM – atteignant souvent plusieurs milliers de dollars par an – constitue une barrière majeure à leur accessibilité et à leur adoption à large échelle dans la pratique clinique.

## 2.9. Utilisation des CGM en contexte périopératoire

Récemment, l'utilisation des CGM a été explorée chez les patient-es en période périopératoire. Bien que aucune étude de validation à large échelle n'ait encore été réalisée dans la population chirurgicale non cardiaque, plusieurs études de plus petite largeur ont
montré une bonne concordance entre les mesures de glycémie obtenues par CGM et les valeurs POC dans différents contextes de chirurgie non cardiaque (56-61).

Les capteurs interstitiels mesurent la glycémie toutes les une à cinq minutes, produisant ainsi des profils glycémiques dynamiques susceptibles de révéler des excursions hyperglycémiques ou hypoglycémiques rapides souvent absentes du suivi POC horaire standard (62, 63). Les CGM offrent un potentiel important pour transformer la prise en charge glycémique périopératoire en fournissant des tendances glycémiques en temps réel plutôt que des valeurs isolées, ce qui permet une approche plus proactive et nuancée du contrôle glycémique. Cette capacité peut s'avérer particulièrement précieuse chez des groupes à risque élevé de dysglycémie, notamment les patient-es diabétiques (62, 63) ou les patient-es subi-es à une chirurgie bariatrique (64), pancréatique (65), de transplantation ou d'autres interventions majeures (62, 63).

Le profil glycémique en chirurgie non cardiaque varie selon le type d'intervention. La chirurgie bariatrique constitue une situation particulière, bien connue pour ses effets hypoglycémiants, et représente le domaine où le plus grand nombre d'études sur la CGM périopératoire a été publié (64). Au cours des premiers jours postopératoires, le protocole spécifique de réalimentation et les modifications de la physiologie gastro-intestinale se traduisent par un profil CGM distinctif.

En chirurgie non bariatrique majeure, où le stress chirurgical est important, la dysglycémie est également fréquente. Une étude incluant 70 patient-es diabétiques ou non, pris-es en charge pour une chirurgie majeure, a montré que la CGM permettait de détecter des durées significatives d'hyperglycémie et d'hypoglycémie malgré une gestion périopératoire du diabète standardisée (63). Cela suggère que un pourcentage élevé de patient-es présente des épisodes prolongés de dysglycémie. Certaines chirurgies majeures comme la pancréatectomie
ou la transplantation exposent à un risque encore plus élevé, en raison d'un état d'insulinopénie induite ou de l'administration de fortes doses de corticoïdes.

## 2.10. Défis pratiques de l'utilisation des CGM en période périopératoire

L'intégration des CGM dans la période périopératoire soulève des défis spécifiques, notamment en peropératoire. Les capteurs sont généralement positionnés au bras, en région sous-claviculaire ou abdominale — des zones qui doivent rester accessibles malgré les incisions chirurgicales, les champs opératoires et la position du patient. L'électrocoagulation peropératoire et les plaques de mise à la terre d'électrochirurgie sont impliquées dans jusqu'à 15 % des défaillances de capteurs, tandis que le délai de préchauffage requis limite leur utilité lorsqu'ils sont posés juste avant l'induction anesthésique (62). La précision des CGM diminue de manière significative lorsque les valeurs glycémiques sortent de l'intervalle validé des dispositifs récents (2,2 à 22,2 mmol/L) (66). En outre, comme les CGM mesurent le glucose dans le liquide interstitiel et non dans le sang, un décalage physiologique d'environ 10 à 15 minutes est inévitable, ce qui peut compliquer l'interprétation en cas de fluctuations glycémiques rapides. Néanmoins, les excursions rapides et extrêmes restent rares dans la pratique clinique.

Chez les patient-es en état critique, la précision des CGM varie selon le modèle et la méthode de référence, les dispositifs intravasculaires montrant généralement de meilleures performances que les dispositifs sous-cutanés, avec une fiabilité réduite en cas d'hypoglycémie (48). Cependant, plusieurs études menées chez l'adulte ont démontré que les CGM restaient précis et sûrs, y compris chez les patient-es en état de choc nécessitant un soutien par noradrénaline (31, 67, 68). Une surveillance continue du fonctionnement du capteur reste toutefois indispensable dans les situations cliniques où des facteurs comme l'œdème tissulaire, la compression du dispositif ou une hypoperfusion sévère peuvent
compromettre la fiabilité de la mesure (31). Il est important de noter que des études menées en réanimation ont rapporté des taux de défaillance des dispositifs avoisinant 12 %, et des taux d'arrachement accidentel pouvant atteindre 33 % (67, 68).

## 2.11. Portée de la revue systématique

Le rôle des CGM chez les patient-es bénéficiant d'une chirurgie non cardiaque reste mal défini et, au moment du déroulement de l'étude, n'avait pas encore fait l'objet d'une revue systématique. Afin de combler cette lacune, nous avons réalisé une revue systématique des données disponibles concernant l'utilisation des CGM pour la surveillance des profils glycémiques en période périopératoire. Nous avons évalué leur impact potentiel sur le contrôle glycémique périopératoire ainsi que leur association avec les issues cliniques majeures. En complément, nous avons recensé de manière systématique les dysfonctionnements rapportés des dispositifs et les événements indésirables. Enfin, nous avons analysé les sources de financement déclarées et les éventuels conflits d'intérêts pour chaque étude.

Les résultats de cette revue sont présentés dans l'article publié dans le *European Journal of Anaesthesiology*, qui constitue la seconde partie du présent travail de thèse (69).
## 3. MANUSCRIT

Article publié dans l'European Journal of Anaesthesiology (éditeur : Wolters Kluwer), en ligne le 7 novembre 2024 et en version imprimée le 1er février 2025.

Reference complète : Putzu A, Grange E, Schorer R, Schiffer E, Gariani K. Continuous peri-operative glucose monitoring in noncardiac surgery: A systematic review. Eur J Anaesthesiol. 2025 Feb 1;42(2):162-171. doi: 10.1097/EJA.0000000000002095. Epub 2024 Nov 7.

### 3.1 Texte principal
OPEN

REVIEW ARTICLE

# Continuous peri-operative glucose monitoring in noncardiac surgery

## A systematic review

Alessandro Putzu, Elliot Grange, Raoul Schorer, Eduardo Schiffer and Karim Gariani

**BACKGROUND** Glucose management is an important component of peri-operative care. The usefulness of continuous glucose monitoring (CGM) in noncardiac surgery is uncertain.

**OBJECTIVE** To systematically assess the glycaemic profile and clinical outcome of patients equipped with a CGM device during the peri-operative period in noncardiac surgery.

**DESIGN** Systematic review.

**DATA SOURCES** Electronic databases were systematically searched up to July 2024.

**ELIGIBILITY CRITERIA** Any studies performed in the peri-operative setting using a CGM device were included. Closed-loop systems also administering insulin were excluded. Analyses were stratified according to diabetes mellitus status and covered intra-operative and postoperative data. Outcomes included glycaemic profile (normal range 3.9 to 10.0 mmol l⁻¹), complications, adverse events, and device dysfunction.

**RESULTS** Twenty-six studies (1016 patients) were included. Twenty-four studies were not randomised, and six used a control arm for comparison. In bariatric surgery, diabetes

mellitus patients had a mean ± SD glucose of 5.6 ± 0.5 mmol l⁻¹, with 15.4 ± 8.6% time below range, 75.3 ± 5.5% in range and 9.6 ± 6.7% above range. During major surgery, diabetes mellitus patients showed a mean glucose of 9.6 ± 1.1 mmol l⁻¹, with 9.5 ± 9.1% of time below range, 56.3 ± 13.5% in range and 30.6 ± 13.9% above range. In comparison, nondiabetes mellitus patients had a mean glucose of 6.4 ± 0.6 mmol l⁻¹, with 6.7 ± 8.4% time below range, 84.6 ± 15.5% in range and 11.2 ± 4.9% above range. Peri-operative complications were reported in only one comparative study and were similar in CGM and control groups. Device-related adverse events were rare and underreported. In 9.21% of cases, the devices experienced dysfunctions such as accidental removal and issues with sensors or readers.

**CONCLUSION** Due to the limited number of controlled studies, the impact of CGM on postoperative glycaemic control and complications compared with point-of-care testing remains unknown. Variability in postoperative glycaemic profiles and a device dysfunction rate of 1 in 10 suggest CGM should be investigated in a targeted surgical group.

Published online 7 November 2024

KEY POINTS

* This systematic review of the use of continuous glucose monitoring (CGM) during the peri-operative period included 26 studies and 1016 patients.

* CGM detected significant variability in glucose levels across various surgical specialties that depended on diabetic status.

* Device-related adverse events were rare, although device dysfunction occurred in approximately 10% of cases.

From the Division of Anaesthesiology, Department of Anaesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals (AP, EG, RS, ES), Faculty of Medicine, University of Geneva (ES) and Division of Endocrinology, Diabetes, Nutrition and Therapeutic Patient Education, Department of Medical Specialties, Geneva University Hospitals, Geneva, Switzerland (KG)

Correspondence to Dr Alessandro Putzu, Division of Anaesthesiology, Geneva University Hospitals, 4 Rue Gabrielle Perret-Gentil, 1206 Geneva, Switzerland.
Tel: +41 22 372 74 02; fax: +41 22 372 76 90; e-mail: Alessandro.Putzu@hcuge.ch
0265-0215 Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society of Anaesthesiology and Intensive Care.

DOI:10.1097/EJA.0000000000002095
* Due to the limited number of controlled studies, the impact of CGM on postoperative glycaemic control and complications, compared with point-of-care testing, remains unclear.

## Introduction

Effective glucose management is an important element of peri-operative care. There is a large body of evidence indicating that peri-operative hyperglycaemia is associated with adverse outcomes such as prolonged hospital stay, postoperative infection, readmission or mortality in patients with and without diabetes.¹⁻³

In the peri-operative period, hyperglycaemia in diabetic patients and stress hyperglycaemia in individuals not previously known for diabetes are commonly encountered. The magnitude of peri-operative hyperglycaemia will depend on several factors, such as the type of surgery, previous diabetes control, infection and other complications.⁴

Hypoglycaemia is associated with cognitive deficit, adverse cardiovascular events and potential death.⁵ Various risk factors have been associated with the development of peri-operative hypoglycaemia. These include fasting, low preoperative fasting glucose, diabetes for more than 10 years, low body mass index (BMI), use of sulfonylureas and other insulin secretagogues, previous episodes of hypoglycaemia, advanced age, preoperative administration of subcutaneous insulin and prolonged surgery.⁶⁻⁸

Peri-operative blood glucose monitoring, therefore, remains an important issue not only for diabetic patients but also for nondiabetic patient groups at increased risk of postoperative complications.

There are several tools available for peri-operative glycaemic monitoring, the most frequently used being point-of-care (POC) glucometers using capillary blood, which are widely available and low-cost. However, some peri-operative factors such as peripheral oedema or severe hypotension may affect device reliability and iterative testing can be time-consuming.⁹⁻¹³ Furthermore, repetitive blood sampling using a needle or fingerprick can be uncomfortable for some patients. Arterial blood gas analysis and traditional laboratory testing are alternatives to the monitoring of blood glucose.¹⁰

The advent of continuous glucose monitoring (CGM) has revolutionised the field of diabetology.¹⁴ Several studies have shown an advantage of CGMs compared with self-monitoring of blood glucose, mainly in type 1 diabetes but also for some patients with type 2 diabetes.¹⁵,¹⁶

There is growing interest in the benefits of CGMs outside of the usual ambulatory setting. The accuracy of CGM compared with POC testing has been satisfactory in inpatients and in the critically ill.¹⁷,¹⁸ Furthermore, various CGM implementation strategies have been suggested in

the intensive care unit,¹⁸ but changes in illness acuity necessitate ongoing assessment of sensor function.¹³,¹⁹ Conversely, the potential role of CGM in noncardiac surgery remains unclear. Therefore, we aimed to systematically review the available evidence to describe the role of CGM on glycaemic control and its potential associations with significant clinical outcomes in the peri-operative period for noncardiac surgery patients.

## Materials and methods

This systematic review followed a pre-established protocol published in the PROSPERO database (CRD42023448990) using the Cochrane methodology.²⁰ Reporting was aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (PRISMA 2020 checklist, Supplementary Table S1, http://links.lww.com/EJA/B56).²¹

### Systematic search

Two investigators (AP and EG) independently searched MEDLINE, EMBASE and the Cochrane Central Register of Clinical Trials for appropriate articles from inception to 6 July 2024. The search strategies are reported in the Supplementary Methods S1, http://links.lww.com/EJA/B56. For unpublished trials, we searched the National Institute of Health Clinical Trials Register (ClinicalTrials.gov). Bibliographies of retrieved studies and of relevant reviews were also screened for additional publications. No language restriction was enforced, and studies published only as abstracts were also included.

### Study selection

Studies were identified through examination of abstracts by two authors (AP and EG) and collected as full-text articles if potentially relevant. Eligible studies met the following PICOS criteria: patient group: adults undergoing noncardiac surgery; intervention: use of any CGM device; comparison intervention: any control intervention or lack of control group; outcome: see below; study design: observational and interventional studies.

We excluded case reports, studies with less than 10 patients, studies with overlapping patient groups already included in a previously selected article, and studies employing a closed-loop system (artificial pancreas) considered as co-intervention. Two authors (AP and EG) independently assessed selected studies for the final analysis. Any disagreements were resolved by consensus between the two authors.

### Data abstraction

Data extraction and entry was performed by one author and a second author verified the data, with divergences resolved by consensus (AP and EG). Sources of significant clinical heterogeneity were identified and recorded. These included study design, patient characteristics, CGM device type, intervention and control regimen. Corresponding authors were not contacted in case of missing outcome data.
We transformed results reported as interquartile or range into standard deviation using the formula recommended by the *Cochrane Handbook*.²⁰ Medians were converted into means using a formula suggested for skewed outcomes.²⁰,²² We excluded results with insufficient data to allow calculation of mean or standard deviation.

## Outcomes

The CGM glycaemic profile was characterised using some key metrics: time below range (TBR), time in range (TIR), time above range (TAR) and the coefficient of variation. TBR reflects the duration spent with blood glucose levels below the target range, indicating an increased risk of hypoglycaemia. TIR represents the percentage of time spent within the optimal glycaemic range. TAR highlights periods when blood glucose levels exceed the recommended range. Coefficient of variation is calculated by dividing the glucose standard deviation by the mean glucose.

The primary outcomes were mean glucose level, TBR, TAR, TIR and coefficient of variation. The outcome definitions were as reported from each study and were listed in the supplementary material (Supplementary Table S2, http://links.lww.com/EJA/B56). The secondary outcomes included various clinical endpoints comprising postoperative mortality, myocardial infarction, acute kidney injury and stroke. Post hoc tertiary outcomes were device-related adverse events (DRAE) and device dysfunction. Adverse events reported to be related to the CGM device were included in DRAE. Device dysfunction was defined as any event necessitating the replacement of the device (excluding replacement for MRI) or any event causing relevant missing data. Outcomes reporting by each study are listed in Supplementary Table S3, http://links.lww.com/EJA/B56. Study-reported outcomes are listed in Supplementary Table S3, http://links.lww.com/EJA/B56.

## Methodological quality

Two authors (AP and EG) independently assessed the risk of bias of each study and consensus was reached in case of divergences. For the nonrandomised studies, the methodological index for nonrandomised studies (MINORS) tool was used (Supplementary Methods S2, http://links.lww.com/EJA/B56).²³ The overall judgement was categorised as good, fair or poor.

For randomised controlled trials (RCTs), the risk of bias was assessed by using the Cochrane Risk of Bias 2 tool. The assessment was performed at the primary outcome level. The overall risk-of-bias judgement was categorised as low risk of bias, some concerns and high risk of bias.

## Conflict of interest

Two authors (EG and AP) assessed potential financial and nonfinancial conflicts of interest. The methodology is reported in the supplement (Supplementary Methods S3,

http://links.lww.com/EJA/B56). Each study was categorised according to the following categories: 'notable concern about conflict of interest', 'no notable concern about conflict of interest' or 'unclear concern about conflict of interests'.

## Statistical analysis

The data from intra-operative and postoperative periods were stratified according to diabetes status: diabetes mellitus, nondiabetic/prediabetes (non-DM), and mixed. The analysis was further detailed regarding major surgical specialties: bariatric surgery, nonbariatric major surgery, pancreatic surgery (excluding pancreas transplantation) and transplantation surgery. Preoperative data were excluded from the analysis whenever possible. The planned meta-analysis and head-to-head comparisons were not performed because of the scarcity of comparative studies. The weighted percentage means of the primary outcomes were computed. The weights were the sample size of each study normalised to sum to one. The standard deviation of each study mean was computed and considered as an index of dispersion between studies. The analysis was performed using R version 4.3.2 using package 'stats' and 'dplyr' (R Core Team (2023). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria (https://www.R-project.org/). A qualitative description of secondary and tertiary outcomes was performed due to underreporting and heterogeneity. Protocol deviations are reported in Supplementary Table S4, http://links.lww.com/EJA/B56.

## Results

### Systematic search

The systematic search produced 1076 potential titles and abstracts from online databases and hand search (Fig. 1). Ninety-four articles were identified for review, and after exclusion of inadequate reports (Supplementary Table S4, http://links.lww.com/EJA/B56), we included 29 reports of 26 studies with a total of 1016 patients.²⁴⁻⁵²

### Study characteristics

The characteristics of the included studies are shown in Table 1 and Supplementary Table S6, http://links.lww.com/EJA/B56. Twenty-four were nonrandomised trials of which four were comparative studies. Nineteen studies were prospective. Two studies were RCTs. All studies but one were single centre. Thirteen studies were performed in Europe, 10 in Asia, 2 in Oceania and 1 in the United States.

The mean age ranged between 35 and 70 years old and the proportion of women ranged between 14 and 82%. The types of procedures were varied, including seven studies of patients benefiting from major abdominal surgery, six from bariatric surgery (66% sleeve gastrectomy, 28% laparoscopic Roux-en-Y gastric bypass), three
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Flow diagram for the selection of studies.</figcaption></figure>

from pancreatic surgery, four from orthopaedic surgery, three from transplantation surgery (one pancreas transplantation),³⁸ and three from miscellaneous surgery.

Diabetes prevalence ranged between 0 and 100% and mean BMI ranged between 21.2 and 47.5 kg m⁻². Eight studies reported the use of a standardised peri-operative insulin protocol.

In terms of CGM device types, Freestyle Libre and Dexcom G6 were used in nine and seven studies respectively, iPro2 in four studies, and MiniMed in two studies. The Guardian REAL-Time, Guardian Connect, Micro-Tech AiDEX G7 and a nonspecified Medtronic device, were each used in one study. The estimated duration of CGM monitoring ranged between 1 and 28 days.

The most common position of the device was the dorsal side of the upper arm (414 patients), followed by the infraclavicular region (140 patients) and the abdomen (87 patients). The position of the devices was not clearly reported in 10 studies.

## Methodological quality

Twenty-four studies were nonrandomised and were evaluated with MINORS. Overall, 5 studies were evaluated to have fair methodological quality, whereas 19 had poor methodological quality (Supplementary Table S6, http://links.lww.com/EJA/B56).

Two studies were randomised: one RCT was judged to be at low risk of bias,³⁶ and its sub-analysis was considered at
<table><caption>**Table 1** Study characteristics</caption><thead><tr><th>Study</th><th>Country</th><th>Sample size</th><th>Surgery type</th><th>Diabetic status</th><th>Diabetes prevalence</th><th>CGM device</th><th>CGM monitoring</th></tr></thead><tbody><tr><td>Carlsson 2023</td><td>Denmark</td><td>70</td><td>Major surgery</td><td>Mixed</td><td>71%</td><td>Dexcom G6</td><td>Intra- and postoperative</td></tr><tr><td>Fagher 2023</td><td>Sweden</td><td>100</td><td>Pancreatoduodenectomy</td><td>Mixed</td><td>23%</td><td>FreeStyle Libre 1</td><td>Postoperative</td></tr><tr><td>Farmanov 2024</td><td>Russia</td><td>26</td><td>Total duodenopancreatectomy</td><td>Mixed</td><td>46%</td><td>Medtronic device</td><td>Postoperative</td></tr><tr><td>Hagerf 2023</td><td>Czech Republic</td><td>14</td><td>Major abdominal surgery needing ICU</td><td>Mixed</td><td>50%</td><td>Dexcom G6</td><td>Postoperative</td></tr><tr><td>Hagerf 2024</td><td>Czech Republic</td><td>65</td><td>Major abdominal surgery or transplantation needing ICU stay</td><td>Mixed</td><td>51%</td><td>Dexcom G6</td><td>Postoperative</td></tr><tr><td>Herzig 2022</td><td>Switzerland</td><td>44</td><td>Elective surgery</td><td>DM2</td><td>100%</td><td>Dexcom G6</td><td>Intra- and postoperative</td></tr><tr><td>Jabor 2023</td><td>Czech Republic</td><td>61</td><td>Major abdominal surgery needing ICU</td><td>NR</td><td>NR</td><td>Dexcom G6</td><td>Postoperative</td></tr><tr><td>Jo 2022</td><td>South Korea</td><td>25</td><td>Kidney transplantation surgery</td><td>Mixed</td><td>24%</td><td>NR</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Kim 2022</td><td>South Korea</td><td>20</td><td>Metabolic surgery (LSG/DJB 50%, LSG 30%, LRYGB 15%, LBPD 5%)</td><td>DM2</td><td>100%</td><td>FreeStyle Libre</td><td>Pre- and postoperative</td></tr><tr><td>Leung 2023</td><td>China</td><td>83</td><td>Total knee arthroplasty</td><td>Mixed</td><td>27%</td><td>FreeStyle Libre</td><td>Postoperative</td></tr><tr><td>Maeda 2019</td><td>Japan</td><td>20</td><td>Total knee or hip arthroplasty</td><td>DM2 or pre-diabetes</td><td>60%</td><td>iPro 2</td><td>Postoperative</td></tr><tr><td>Mao 2021</td><td>China</td><td>18</td><td>Pancreatic or palliative abdominal surgery</td><td>Non-diabetes</td><td>0%</td><td>FreeStyle Libre</td><td>Postoperative</td></tr><tr><td>Mittal 2015</td><td>UK</td><td>26</td><td>Pancreas transplantation surgery with or without kidney transplantation</td><td>Mixed</td><td>NR</td><td>iPro 2</td><td>Postoperative</td></tr><tr><td>Munekage 2016</td><td>Japan</td><td>15</td><td>Pancreatoduodenectomy or hepatectomy needing ICU</td><td>NR</td><td>NR</td><td>iPro 2</td><td>Postoperative</td></tr><tr><td>Poljakova 2013</td><td>Czech Republic</td><td>17</td><td>Lower extremity vascular or orthopaedic surgery</td><td>DM2 or pre-diabetes</td><td>94%</td><td>Guardian REAL-Time</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Price 2023</td><td>USA</td><td>76</td><td>Surgery under general anaesthesia of at least 3 h expected duration</td><td>DM1 or DM2</td><td>100%</td><td>Dexcom G6 or FreeStyle Libre 2.0</td><td>Intra- and postoperative</td></tr><tr><td>Shaban 2023</td><td>Australia</td><td>21</td><td>Kidney transplantation surgery</td><td>Mixed</td><td>19%</td><td>FreeStyle Libre</td><td>Postoperative</td></tr><tr><td>Tripyla 2020</td><td>Switzerland</td><td>20</td><td>Major abdominal surgery</td><td>Diabetes or pre-diabetes</td><td>75%</td><td>Dexcom G6</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Turquetil 2021</td><td>France</td><td>31</td><td>Metabolic surgery (LRYGB 77%, LSG 23%)</td><td>DM2</td><td>100%</td><td>FreeStyle Libre</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Wang 2021</td><td>China</td><td>49</td><td>Metabolic surgery (LSG 100%)</td><td>Mixed</td><td>31%</td><td>MiniMed</td><td>Pre- and postoperative</td></tr><tr><td>Wysocki 2019</td><td>Poland</td><td>32</td><td>Metabolic surgery (LSG 56%, LRYGB 44%)</td><td>Mixed</td><td>50%</td><td>FreeStyle Libre</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Wysocki 2024</td><td>Poland</td><td>33</td><td>Metabolic surgery (LSG 100%)</td><td>DM2</td><td>100%</td><td>FreeStyle Libre</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Yin 2022</td><td>China</td><td>55</td><td>Amputation surgery</td><td>Mixed</td><td>87%</td><td>Medtronic device</td><td>Pre-, intra-, and postoperative</td></tr><tr><td>Yip 2014</td><td>New Zealand</td><td>21</td><td>Metabolic surgery (LRYGB 55%, LSG 45%)</td><td>DM2</td><td>100%</td><td>MiniMed MMT-7102</td><td>Pre- and postoperative</td></tr><tr><td>Yong Jin 2019</td><td>South Korea</td><td>31</td><td>Kidney or liver transplantation surgery</td><td>Mixed</td><td>19%</td><td>iPro 2</td><td>Postoperative</td></tr><tr><td>Zhang 2024</td><td>China</td><td>31</td><td>Esophagectomy</td><td>Non-diabetes</td><td>0%</td><td>MicroTech ADEX model G7</td><td>Pre-, intra-, and postoperative</td></tr></tbody></table>

CGM, continuous glucose monitoring; DM, diabetes mellitus; ICU, intensive care unit; LSG/DJB, laparoscopic sleeve gastrectomy with duodenojejunal bypass; LSG, laparoscopic sleeve gastrectomy; LRYGB, laparoscopic Roux-en-Y gastric bypass; LBPD, laparoscopic bilio pancreatic diversion; NR, not reported.
high risk of bias due to the methodology not being defined a priori.43 The second RCT was judged to be at high risk of bias (Supplementary Figure S1, http://links.lww.com/EJA/B56).51

## Conflict of interest

Information on the funding sources and author financial conflicts of interest are reported in Supplementary Table S7, http://links.lww.com/EJA/B56. Overall, 16 studies were reported as having no notable concern about conflict of interest, 7 studies had a notable concern,32,33,36,40,43,45,47,51 and 3 studies were reported as having an unclear concern.28,29,34

Three studies reported financial support from a company producing CGM devices.36,40,45 In two cases, it was clearly reported that the company had no role in the study's design, conduct, analysis, or report.36,45

Seven studies had at least one author reporting a potential financial conflict of interest with companies related to the CGM industry.32,33,36,40,45,47,51 In total, 15 out of 77 authors (19%) of these studies had a conflict of interest. In two cases, it was the corresponding author.33,47

Seven studies were found to have nonfinancial conflicts of interest, with the reasons being multiple publications or acknowledged expertise within CGM, diabetes, or peri-operative monitoring.24,28,33,36,38,47,52

## Glycaemic profile

The results are detailed in Table 2. Five studies reported that CGM readings were not blinded during the study, potentially influencing peri-operative care. These studies are excluded from the aggregate analysis.31,37,39,44,49

The mean glucose levels were higher in patients undergoing major surgery (diabetes mellitus 9.6 ± 1.1; nondiabetes mellitus 6.4 ± 0.6 mmol l⁻¹) vs. bariatric surgery

(diabetes mellitus 5.6 ± 0; nondiabetes mellitus 4.6 ± 0.1 mmol l⁻¹).

TBR, defined in most studies as less than 3.9 mmol l⁻¹ (70 mg dl⁻¹), was higher in bariatric surgery (diabetes mellitus 15.4 ± 8.6%; nondiabetes mellitus 26.7 ± 1.0%) vs. major surgery (diabetes mellitus 9.5 ± 9.1; nondiabetes mellitus 6.7 ± 8.4).

The TIR was defined in most studies as glycaemic values between 3.9 mmol l⁻¹ (70 mg dl⁻¹) and 10.0 mmol l⁻¹ (180 mg dl⁻¹). It was higher for diabetic patients undergoing bariatric surgery (75.4 ± 5.5%) vs. major surgery (56.3 ± 13.5%). In nondiabetic patients, TIR was over 80% for major surgery as well as transplantation and pancreatic procedures vs. 63.4 ± 0.2% for bariatric surgery.

TAR, defined in most studies as values greater than 10 mmol l⁻¹ (180 mg dl⁻¹), was higher in diabetic patients undergoing major surgery (30.6 ± 13.9%) and pancreatic surgery (45.6 ± 8.5%). In the nondiabetic or prediabetic group, TAR was increased in bariatric surgery (7.6 ± 0.9%), pancreatic surgery (8.6 ± 6.2%), major surgery (12.4 ± 3.6%), and particularly transplantation surgery (21.7 ± 7.5%). The coefficient of variation was rarely reported and varied from 20.1 to 35.5 ± 1.0.

## Postoperative complications

No comparative study reported clinical outcomes predefined by our meta-analysis. One RCT of the use of CGM vs. closed-loop insulin delivery in mixed elective surgery reported similar rates of Clavien-Dindo complications in both groups: Clavien-Dindo II or higher: 12 of 22 vs. 14 of 22, Clavien-Dindo III or higher: 4 of 22 vs. 6 of 22.36

## Device-related adverse events

Eight studies reported DRAEs, but no study reported a definition for DRAE (Supplementary Table S8, http://links.lww.com/EJA/B56). Five studies reported not encountering

<table><caption>Table 2 Primary outcomes in different surgical specialties</caption><thead><tr><th>Surgery</th><th>Characteristics</th><th>Studies (patients)</th><th>Glucose level (mmol l<sup>-1</sup>) mean ± SD</th><th>Studies (patients)</th><th>TBR (%)</th><th>Studies (patients)</th><th>TIR (%)</th><th>Studies (patients)</th><th>TAR (%)</th><th>Studies (patients)</th><th>CV (%)</th></tr></thead><tbody><tr><td rowspan="3">Bariatric surgery</td><td>DM</td><td>6 (136)</td><td>5.6 ± 0.5</td><td>5 (109)</td><td>15.4 ± 8.6</td><td>6 (130)</td><td>75.3 ± 5.5</td><td>5 (109)</td><td>9.6 ± 6.7</td><td>1 (20)</td><td>20.1</td></tr><tr><td>Non/pre-DM</td><td>1 (16)</td><td>4.6 ± 0.1</td><td>1 (16)</td><td>26.7 ± 1.0</td><td>1 (16)</td><td>63.4 ± 0.2</td><td>1 (16)</td><td>7.6 ± 1.0</td><td>NR</td><td>-</td></tr><tr><td>Mixed</td><td>6 (183)</td><td>5.3 ± 0.6</td><td>5 (156)</td><td>18.1 ± 8.4</td><td>6 (177)</td><td>72.6 ± 6.8</td><td>5 (156)</td><td>8.8 ± 5.8</td><td>1 (20)</td><td>20.1</td></tr><tr><td rowspan="2">Major surgery</td><td>DM</td><td>2 (71)</td><td>9.6 ± 1.1</td><td>2 (71)</td><td>9.5 ± 9.1</td><td>3 (75)</td><td>56.3 ± 13.5</td><td>2 (71)</td><td>30.6 ± 13.9</td><td>1 (21)</td><td>26.2 ± 6.1</td></tr><tr><td>Non/pre-DM</td><td>3 (69)</td><td>6.4 ± 0.6</td><td>3 (69)</td><td>6.7 ± 8.4</td><td>4 (91)</td><td>84.6 ± 15.5</td><td>2 (49)</td><td>11.2 ± 4.9</td><td>2 (49)</td><td>23.9 ± 9.9</td></tr><tr><td rowspan="2">Pancreatic surgery</td><td>Mixed</td><td>11 (373)</td><td>8.3 ± 1.3</td><td>6 (179)</td><td>7.5 ± 8.1</td><td>8 (220)</td><td>73.4 ± 18.2</td><td>7 (267)</td><td>21.2 ± 11.6</td><td>5 (203)</td><td>29.1 ± 7.0</td></tr><tr><td>DM</td><td>1 (21)</td><td>10.7 ± 0.6</td><td>1 (21)</td><td>0.3 ± 0.2</td><td>1 (21)</td><td>46.6 ± 8.8</td><td>1 (21)</td><td>45.6 ± 8.5</td><td>1 (21)</td><td>26.2 ± 6.2</td></tr><tr><td rowspan="3">Transplant surgery</td><td>Non/pre-DM</td><td>1 (15)</td><td>6.5 ± 0.7</td><td>1 (15)</td><td>10.9 ± 4.1</td><td>1 (15)</td><td>80.4 ± 3.0</td><td>1 (15)</td><td>8.6 ± 6.4</td><td>1 (15)</td><td>36.8 ± 3.9</td></tr><tr><td>Mixed</td><td>4 (54)</td><td>8.8 ± 1.8</td><td>2 (36)</td><td>4.7 ± 5.9</td><td>3 (48)</td><td>66.8 ± 19.1</td><td>3 (42)</td><td>29.2 ± 18.6</td><td>3 (42)</td><td>30.4 ± 6.9</td></tr><tr><td>DM</td><td>NR</td><td>-</td><td>NR</td><td>-</td><td>1 (4)</td><td>32.5</td><td>NR</td><td>-</td><td>NR</td><td>-</td></tr><tr><td rowspan="2"></td><td>Non/pre-DM</td><td>NR</td><td>-</td><td>NR</td><td>-</td><td>1 (22)</td><td>83.2 ± 29.9</td><td>NR</td><td>-</td><td>NR</td><td>-</td></tr><tr><td>Mixed</td><td>3 (97)</td><td>8.5 ± 1.0</td><td>1 (25)</td><td>8.0</td><td>1 (26)</td><td>75.4 ± 33.1</td><td>2 (66)</td><td>21.7 ± 7.5</td><td>2 (66)</td><td>35.5 ± 1.0</td></tr></tbody></table>

Data reported as mean ± standard deviation. CV, coefficient of variation; DM, diabetes mellitus; NR, not reported; TBR, time below range; TIR, time in range; TAR, time above range.
any DRAE (0 of 227 patients).36,37,45–47 One study reported self-limited bleeding, mild pruritus and skin irritation re-solving spontaneously in 2 of 20 patients.40 Another study found that 2 of 65 patients had subcutaneous haematomas at the sensor insertion site, with degraded sensor function.51 A third study reported that 2 of 42 patients were unwilling to reimplant the sensor after pressure damage.50 We found no reports of serious or life-threatening DRAEs.

## Device dysfunction

Thirteen studies reported CGM dysfunction data, and no study defined CGM dysfunction (Supplementary Table S8, http://links.lww.com/EJA/B56). The devices were subject to accidental removal and sensor or reader dysfunction. Two studies (37 patients) reported the absence of any device dysfunction, whereas 11 studies reported the presence of dysfunctions. Overall, about 56 of 608 patients (9.21%) experienced device dysfunction.

Troubleshooting involved either sensor replacement or exclusion of the data. In the studies where replacement was used and reported, 13 sensors were replaced in 165 patients. Thirty-four patients were withdrawn from the studies because of dysfunction issues.

The possibility of electromagnetic interference from surgical cauterisation was mentioned in five studies. Two of those studies placed the devices preoperatively, and some of these devices failed to function after warm-up, potentially because of electric interference.40,45 Two studies placed the devices before surgery and did not report any electromagnetic interference (0 of 40 devices).37,46 One study placed the devices in the postanaesthesia care unit to avoid this problem.42

## Discussion

This systematic review examines the role of CGM in noncardiac surgery. Due to the limited number of controlled studies, we were unable to evaluate the impact on postoperative glycaemic control and complications when comparing CGM with alternative strategies like POC testing. Notably, glycaemic profiles varied significantly across different procedures and in patients with or without diabetes. The results highlight that while adverse events are uncommon and typically mild, the chance of device dysfunction is up to 1 in 10.

The duration of CGM monitoring varied from 1 to 28 days, demonstrating its applicability for both short and extended postoperative periods. The types of CGM devices used, mainly Freestyle Libre and Dexcom G6, were implanted on the dorsal side of the upper arm, as well as the infraclavicular region and the abdomen.

## Significance of the findings

Adverse events and device dysfunctions were rarely reported and not uniformly defined across studies. About 10% of the devices experienced some dysfunction,

including accidental removal and sensor or reader issues. The potential interference of surgical electrocautery with CGM devices highlights a specific concern for their reliability in the operating room,53 but this issue remains mostly undocumented in actual noncardiac surgery studies.

The glycaemic profile of noncardiac surgery patients varied across different types of procedures. Bariatric surgery is well known for its glucose-lowering effects and is the surgical setting for most published studies of peri-operative CGM.54 Approximately one-third of these patients have diabetes, and as many as half can achieve remission from type 2 diabetes a few years after the surgery, with CGM recordings suggested as one of the predictors.33 In the first few days after surgery, the specific re-alimentation protocol and changes in gastrointestinal physiology can be challenging, and this is reflected by a unique CGM pattern. The mean glucose level was below 6 mmol l⁻¹, with TBR about 15% in diabetic patients and 25% in nondiabetic patients, indicating hypoglycaemia to be common in these patients.

Major nonbariatric surgery is a setting where significant surgical stress is expected, leading to dysglycaemia. Our analysis found that in nondiabetic patients, mean glucose values averaged 6.3 mmol l⁻¹ with about 80% of samples within the normal glycaemic range. In patients with diabetes mellitus, mean glucose values were 9 mmol l⁻¹, with approximately 60% of samples from people with diabetes mellitus within the normal glycaemic range.47 This suggests that significant durations of both hypoglycaemia and hyperglycaemia are detected in a high proportion of patients. Some major procedures like pancreatectomy or transplants are at higher risk of dysglycaemia, where an insulin-depleted status is induced, or higher doses of steroids are administered.

This variability emphasises the necessity of individualised glycaemic control and the potential of CGM in peri-operative glucose management in a targeted surgical group.

## Peri-operative glucose monitoring and continuous glucose monitoring

Monitoring and treatment of blood sugar levels in the peri-operative period is primarily aimed at diabetic patients, particularly in those patients receiving subcutaneous or intravenous insulin. Beyond the specific peri-operative use of CGMs, it remains to be determined which nondiabetic patients should require close monitoring of blood glucose levels.55 However, the evaluation of hyperglycaemia and hypoglycaemia in different patient groups indicates that both conditions are significantly associated with worse clinical outcome.1–3,56

Close blood glucose monitoring can be a substantial and time-consuming work.
Measuring blood sugar conventionally with a POC gluc-
ometer can take up to 5 min per sample.⁵⁷ In this context,
the use of CGM could prove useful to reduce the work-
load of operating room personnel. CGM may display
other advantages, as it can identify hyperglycaemia or
hypoglycaemia episodes earlier and allow rapid interven-
tion to avoid dysglycaemic episodes, reducing peri-oper-
ative glucose variability.⁴ It has also been suggested that
CGM may predict mortality: in critically ill patients
monitored with CGM, the risk of in-hospital mortality
increased incrementally with every 10% increase in TAR
with a glucose threshold of 10.5 mmol l⁻¹,⁵⁸

The use of CGMs presents certain limitations, particu-
larly during the peri-operative period. First of all, the
precision of CGM is very significantly reduced when
glycaemic values fall outside the range of 2.2 to
22.2 mmol l⁻¹ (40 to 400 mg dl⁻¹) for the most recent
devices.⁵⁹ However, an excursion of blood glucose values
outside of this interval remains a rare event in the surgical
context, especially elective surgery and therapeutic inter-
ventions should be initiated before reaching those values.
In addition, some drugs such as paracetamol and ascorbic
acid could interfere with sensor readings and may pro-
duce falsely elevated or decreased glucose values.¹³

In critically ill adults, CGM has been reported to be accurate and safe in multiple studies, even in those with shock requiring norepinephrine.13,60,61 However, ongoing assessment of sensor function is crucial in settings where acute clinical changes may affect device reliability. These changes include oedema, device compression and severe hypoperfusion.13 Furthermore, some studies have reported a CGM device failure rate of around 12% and accidental removal rates of up to 33% in a critical care setting.60,61

Even though a large comprehensive validation study
is missing in noncardiac surgery, some studies have
suggested good accuracy of CGM glucose readings
in comparison to POC in various noncardiac surgery
settings.²⁵,³²,³⁷,⁴⁰,⁵⁰,⁵¹

Conflict of interest and the cost of CGM devices are also
critical considerations. Two Cochrane systematic reviews
found that conflicts of interest or industry-sponsored
studies had more favourable conclusions.⁶²,⁶³ Financial
support from CGM companies in some studies raises
questions about potential biases, while the high cost of
these devices could reduce their accessibility and wide-
spread adoption in clinical practice.

Finally, the distinction between intensive glucose moni-
toring and intensive glucose management is crucial.
Although CGM provides detailed glucose profiles, it
does not inherently equate stringent glucose control,
emphasising the need for comprehensive management
strategies that extend beyond mere monitoring. This
is the main reason we excluded studies that did not

blind the CGM readings from our aggregate analysis.
Currently, there is still a lack of global consensus on peri-
operative glycaemic targets. Some guidelines recom-
mend a level less than 10.0 mmol l⁻¹, some less than
12.0 mmol l⁻¹ and others do not even recommend a
specific target, such as the American and Canadian
Diabetes Associations.⁶⁴⁻⁶⁶

Strengths and limitations of the study

Our systematic review on CGM in the peri-operative
period was comprehensive. This study followed both a
prepublished protocol and the Cochrane methodology.
However, there are several limitations. First, there is a
lack of RCTs and controlled studies in general. This did
not allow any head-to-head comparison between CGM
and alternative strategies, such as POC testing. The in-
cluded studies suffered limitations specific to the obser-
vational design, such as a greater risk of bias and the
presence of unadjusted confounders. Observational stud-
ies remain very common because of several advantages
such as feasibility, fewer ethical concerns and larger study
cohorts.⁶⁷ Heterogeneity was observed across studies in
terms of type of surgery, patients and CGM devices used.
This limited the feasibility of quantitative analysis, to-
gether with the underreporting of certain outcomes. Sec-
ond, the absence of individual patient data prevents an
unbiased quantitative analysis. A few studies reported the
use of obsolete older sensors and displayed reduced accu-
racy compared with more recent devices. Subgroup data
based on the occurrence of diabetes and its type were
rarely available, thereby reducing the ability to determine
whether the use of CGM is more useful in diabetic or
nondiabetic patients. Third, a majority of studies did not
assess or report safety outcomes. These limitations lead to
a very low certainty in the current findings.

Conclusions

This study highlights the use of CGM devices across diverse
surgical procedures and patient groups, with notable differ-
ences in observed glycaemic profiles. Device-related ad-
verse events are uncommon and mild, whereas CGM
dysfunction is relatively frequent in this specific group.
Standardisation of the reporting of glucose ranges and the
systematic assessment of adverse events and device dys-
function is recommended in future studies. Due to the
limited number of controlled studies, the impact of CGM on
postoperative glycaemic control and complications com-
pared with POC testing remains unknown. This under-
scores the need for further research, including RCTs and
case-control studies, to better understand the effectiveness
and safety of CGM in peri-operative care.

Acknowledgements relating to this article

Assistance with the article: none.

Financial support and sponsorship: none.

Conflicts of interest: none.
Presentation: none.

This manuscript was handled by Claire Roger.

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## 3.2. Matériel supplémentaire

La document est disponible sur : https://links.lww.com/EJA/B56

### Supplementary Table S1 - PRISMA 2020 checklist

#### Abstract checklist

<table><thead><tr><th>Section and Topic</th><th>Item #</th><th>Checklist Item</th><th>Registered (Yes/No)</th></tr></thead><tbody><tr><td>TITLE</td><td>1</td><td>Identify the report as a systematic review.</td><td>Yes</td></tr><tr><td>BACKGROUND</td><td>2</td><td>Provide an explicit statement of the main objective(s) or question(s) the review addresses.</td><td>Yes</td></tr><tr><td>Objectives</td><td>2</td><td>Specify the inclusion and exclusion criteria for the review.</td><td>Partially (words limit)</td></tr><tr><td>METHODS</td><td>3</td><td>Specify the information sources (e.g. databases, registers) used to identify studies and the date when each was last searched.</td><td>Partially (words limit)</td></tr><tr><td>Eligibility criteria</td><td>4</td><td>Specify the methods used to assess risk of bias in the included studies.</td><td>No (words limit)</td></tr><tr><td>Information sources</td><td>5</td><td>Specify the methods used to present and synthesise results.</td><td>No (words limit)</td></tr><tr><td>Risk of bias</td><td>6</td><td>Give the total number of included studies and participants and summarise relevant characteristics of studies.</td><td>Yes</td></tr><tr><td>Synthesis of results</td><td>7</td><td>Give the total number of included studies and participants and summarise relevant characteristics of studies.</td><td>Yes</td></tr><tr><td>RESULTS</td><td>8</td><td>Present results for main outcomes, preferably indicating the number of included studies and participants for each. If meta-analysis was done, report the summary estimate and confidence/credible interval. If comparing groups, indicate the direction of the effect (i.e. which group is favoured).</td><td>Yes</td></tr><tr><td>DISCUSSION</td><td>9</td><td>Provide a brief summary of the limitations of the evidence included in the review (e.g. study risk of bias, inconsistency and imprecision).</td><td>Yes</td></tr><tr><td>Limitations of evidence</td><td>10</td><td>Provide a general interpretation of the results and important implications.</td><td>Yes</td></tr><tr><td>Interpretation</td><td>10</td><td>Provide a general interpretation of the results and important implications.</td><td>Yes</td></tr><tr><td>OTHER</td><td></td><td></td><td></td></tr><tr><td>Funding</td><td>11</td><td>Specify the primary source of funding for the review.</td><td>No (words limit)</td></tr><tr><td>Registration</td><td>12</td><td>Provide the register name and registration number.</td><td>Yes</td></tr></tbody></table>

From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/
<table><thead><tr><th>Section and Topic</th><th>Item #</th><th>Checklist item</th><th>Location where item is reported</th></tr></thead><tbody><tr><td>TITLE</td><td></td><td></td><td></td></tr><tr><td>Title</td><td>1</td><td>Identify the report as a systematic review.</td><td>1</td></tr><tr><td>ABSTRACT</td><td></td><td></td><td></td></tr><tr><td>Abstract</td><td>2</td><td>See the PRISMA 2020 for Abstracts checklist.</td><td>Supplement</td></tr><tr><td>INTRODUCTION</td><td></td><td></td><td></td></tr><tr><td>Rationale</td><td>3</td><td>Describe the rationale for the review in the context of existing knowledge.</td><td>3-4</td></tr><tr><td>Objectives</td><td>4</td><td>Provide an explicit statement of the objective(s) or question(s) the review addresses.</td><td>4</td></tr><tr><td>METHODS</td><td></td><td></td><td></td></tr><tr><td>Eligibility criteria</td><td>5</td><td>Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.</td><td>5</td></tr><tr><td>Information sources</td><td>6</td><td>Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.</td><td>5</td></tr><tr><td>Search strategy</td><td>7</td><td>Present the full search strategies for all databases, registers and websites, including any filters and limits used.</td><td>5, supplement</td></tr><tr><td>Selection process</td><td>8</td><td>Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.</td><td>5-6</td></tr><tr><td>Data collection process</td><td>9</td><td>Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.</td><td>6</td></tr><tr><td>Data items</td><td>10a</td><td>List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect.</td><td>6, supplement</td></tr><tr><td></td><td>10b</td><td>List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.</td><td>6, supplement</td></tr><tr><td>Study risk of bias assessment</td><td>11</td><td>Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.</td><td>7, supplement</td></tr><tr><td>Effect measures</td><td>12</td><td>Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results.</td><td>NA</td></tr><tr><td rowspan="5">Synthesis methods</td><td>13a</td><td>Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).</td><td>7</td></tr><tr><td>13b</td><td>Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.</td><td>6</td></tr><tr><td>13c</td><td>Describe any methods used to tabulate or visually display results of individual studies and syntheses.</td><td>NA</td></tr><tr><td>13d</td><td>Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.</td><td>NA</td></tr><tr><td>13e</td><td>Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression).</td><td>7, supplement</td></tr><tr><td>Reporting bias assessment</td><td>14</td><td>Describe any sensitivity analyses conducted to assess robustness of the synthesized results.</td><td>Supplement</td></tr><tr><td>Certainty assessment</td><td>15</td><td>Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).</td><td>NA</td></tr><tr><td></td><td></td><td>Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.</td><td>NA</td></tr><tr><td>RESULTS</td><td></td><td></td><td></td></tr><tr><td>Study selection</td><td>16a</td><td>Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.</td><td>8</td></tr><tr><td></td><td>16b</td><td>Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded.</td><td>8, supplement, Figure 1</td></tr><tr><td>Study characteristics</td><td>17</td><td>Cite each included study and present its characteristics.</td><td>8</td></tr><tr><td>Risk of bias in studies</td><td>18</td><td>Present assessments of risk of bias for each included study.</td><td>9, supplement</td></tr></tbody></table>
<table><thead><tr><th>Section and Topic</th><th>Item #</th><th>Checklist item</th><th>Location where item is reported</th></tr></thead><tbody><tr><td>Results of individual studies</td><td>19</td><td>For all outcomes, present, for each study (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots.</td><td>9.11, Table 2, supplement</td></tr><tr><td rowspan="4">Results of syntheses</td><td>20a</td><td>For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.</td><td>9.11, Table 2, supplement</td></tr><tr><td>20b</td><td>Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.</td><td>9.11, Table 2, supplement</td></tr><tr><td>20c</td><td>Present results of all investigations of possible causes of heterogeneity among study results.</td><td>NA</td></tr><tr><td>20d</td><td>Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results.</td><td>NA</td></tr><tr><td>Reporting biases</td><td>21</td><td>Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.</td><td>NA</td></tr><tr><td>Certainty of evidence</td><td>22</td><td>Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.</td><td>NA</td></tr><tr><td colspan="4"><strong>DISCUSSION</strong></td></tr><tr><td rowspan="4">Discussion</td><td>23a</td><td>Provide a general interpretation of the results in the context of other evidence.</td><td>12-15</td></tr><tr><td>23b</td><td>Discuss any limitations of the evidence included in the review.</td><td>16</td></tr><tr><td>23c</td><td>Discuss any limitations of the review processes used.</td><td>16</td></tr><tr><td>23d</td><td>Discuss implications of the results for practice, policy, and future research.</td><td>12-16</td></tr><tr><td colspan="4"><strong>OTHER INFORMATION</strong></td></tr><tr><td rowspan="4">Registration and protocol</td><td>24a</td><td>Provide registration information for the review, including register name and registration number, or state that the review was not registered.</td><td>5</td></tr><tr><td>24b</td><td>Indicate where the review protocol can be accessed, or state that a protocol was not prepared.</td><td>5</td></tr><tr><td>24c</td><td>Describe and explain any amendments to information provided at registration or in the protocol.</td><td>Supplement</td></tr><tr><td>25</td><td>Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.</td><td>5</td></tr><tr><td>Competing interests</td><td>26</td><td>Declare any competing interests of review authors.</td><td>5</td></tr><tr><td>Availability of data, code and other materials</td><td>27</td><td>Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.</td><td>18</td></tr></tbody></table>

NA, not applicable

From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/
# Supplementary Methods S1 – Search Strategies

## PubMed

("CGM"[tiab] OR ("continuos glucose"[tiab] AND (monitor*[tiab] OR "sensing"[tiab] OR sensor*[tiab])) OR "artificial pancreas" OR "closed loop insulin"[tiab:~5]) AND (surgery[tiab] OR surgical[tiab] OR surg*[tiab] OR operative[tiab] OR perioperative[tiab] OR preoperative[tiab] OR postoperative[tiab] OR intraoperative[tiab] OR anesthesia[tiab] OR anaesthesia[tiab])

## EMBASE

("CGM":ab,ti OR ("continuos glucose":ab,ti AND (monitor*:ab,ti OR "sensing":ab,ti OR sensor*:ab,ti)) OR "artificial pancreas":ab,ti OR ("closed loop":ab,ti AND insulin:ab,ti)) AND (surgery:ab,ti OR surgical:ab,ti OR surg*:ab,ti OR operative:ab,ti OR perioperative:ab,ti OR preoperative:ab,ti OR postoperative:ab,ti OR intraoperative:ab,ti OR anesthesia:ab,ti OR anaesthesia:ab,ti)

## CENTRAL

("CGM" OR ("continuos glucose" AND (monitor* OR "sensing" OR sensor*)) OR "artificial pancreas" OR ("closed loop" AND insulin)) AND (surgery OR surgical OR surg* OR operative OR perioperative OR preoperative OR postoperative OR intraoperative OR anesthesia OR anaesthesia)

# Supplementary Table S2 – Outcomes definitions of included studies

<table><thead><tr><td>Study</td><td>TIR</td><td>TBR</td><td>TAR</td></tr></thead><tbody><tr><td>Carlsson 2023</td><td>3.9–10.0 mmol/L (70–180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>NA</td></tr><tr><td>Fagher 2023</td><td>4.0–10.0 mmol/l (72–180 mg/dl)</td><td>&lt; 4 mmol/L (72 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Farmanov 2024</td><td>4.3–11.6 mmol/L (77–209 mg/dL)</td><td>NA</td><td>NA</td></tr><tr><td>Hagerf 2023</td><td>3.9–10.0 mmol/L (70–180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>10–13.9 mmol/l + &gt;13.9 mmol/l</td></tr><tr><td>Hagerf 2024</td><td>3.9–10.0 mmol/L (70–180 mg/dL)</td><td>NA</td><td>NA</td></tr><tr><td>Herzig 2022</td><td>3.9–10.0 mmol/L (70–180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr></tbody></table>
<table><tr><td>Jabor 2023</td><td>NA</td><td>NA</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Jo 2022</td><td>NA</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>10-13.9 mmol/l (181-250 mg/dL)</td></tr><tr><td>Kim 2022</td><td>3.9-10.0 mmol/L (70-180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Krutkyte 2023</td><td>5.6-10.0 mmol/L (101-180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Leung 2023</td><td>NA</td><td>NA</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Maeda 2019</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Mao 2021</td><td>3.9-10.0 mmol/L (70-180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Mittal 2015</td><td>3.9-7.8 mmol/L (70-140 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 7.8 mmol/l (140 mg/dL)</td></tr><tr><td>Munekage 2016</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Shaban 2023</td><td>4 and 11 mmol/L (72-198 mg/dL)</td><td>NA</td><td>NA</td></tr><tr><td>Turquetil 2021</td><td>3.9-7.8 mmol/L (70-140 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 7.8 mmol/l (140 mg/dL)</td></tr><tr><td>Wang 2021</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Wysocki 2019</td><td>3.9-10.0 mmol/L (70-180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 6.7 mmol/l (121 mg/dL)</td></tr><tr><td>Wysocki 2024</td><td>3.9-6.7 mmol/L (70-120 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 6.7 mmol/l (120 mg/dL)</td></tr><tr><td>Yin 2022</td><td>3.9-10.0 mmol/L (70-180 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 10 mmol/l (180 mg/dL)</td></tr><tr><td>Yip 2014</td><td>4-7 mmol/L</td><td>NA</td><td>NA</td></tr><tr><td>Yong Jin 2019</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Zhang 2024</td><td>3.9-7.8 mmol/L (70-140 mg/dL)</td><td>&lt; 3.9 mmol/L (70 mg/dL)</td><td>&gt; 7.8 mmol/l (140 mg/dL)</td></tr></table>

NA, not applicable or not reported; TIR, time in range; TBR, time below range; TAR, time above range.
Supplementary Table S3 – Outcomes reported by included studies

Any outcome

<table><thead><tr><td>Study</td><td>Any glycemic outcome</td><td>DRAE</td><td>Device dysfunction</td><td>Comparative study</td><td>Any predefined clinical outcome</td></tr></thead><tbody><tr><td>Carlsson 2023</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Fagher 2023</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Farmanov 2024</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Hagerf 2023</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Hagerf 2024</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Herzig 2022</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Jabor 2023</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Jo 2022</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Kim 2022</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Leung 2023</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Maeda 2019</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Mao 2021</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Mittal 2015</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Munekage 2016</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Poljakova 2013</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Price 2023</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Shaban 2023</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Tripyla 2020</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Turquetil 2021</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>NA</td></tr><tr><td>Wang 2021</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Wysocki 2019</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Wysocki 2024</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Yin 2022</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Yip 2014</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Yong Jin 2019</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>NA</td></tr><tr><td>Zhang 2024</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>NA</td></tr></tbody></table>

NA, not applicable
Glycamic outcomes

<table><thead><tr><th>Surgery</th><th>Population</th><th>Mean glucose level</th><th>Time Below Range</th><th>Time In Range</th><th>Time Above Range</th><th>Coefficient of Variation</th></tr></thead><tbody><tr><td rowspan="3">Bariatric surgery</td><td>DM</td><td>Kim 2022, Turquetil 2021, Wang 2021, Wysocki 2019, Wysocki 2024, Yip 2014</td><td>Kim 2022, Turquetil 2021, Wang 2021, Wysocki 2019, Wysocki 2024</td><td>Kim 2022, Turquetil 2021, Wang 2021, Wysocki 2019, Wysocki 2024, Yip 2014</td><td>Kim 2022, Turquetil 2021, Wang 2021, Wysocki 2019, Wysocki 2024</td><td>Kim 2022</td></tr><tr><td>Non/pre-DM</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>NR</td></tr><tr><td>Mixed</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>Wysocki 2019</td><td>Wysocki 2019</td></tr><tr><td rowspan="3">Major surgery</td><td>DM</td><td>Carlsson 2023</td><td>Carlsson 2023</td><td>Carlsson 2023</td><td>Carlsson 2023</td><td>Carlsson 2023</td></tr><tr><td>Non/pre-DM</td><td>Carlsson 2023, Mao 2021, Zhang 2024</td><td>Carlsson 2023, Mao 2021, Zhang 2024</td><td>Carlsson 2023, Mao 2021, Zhang 2024</td><td>Mao 2021, Zhang 2024</td><td>Mao 2021, Zhang 2024</td></tr><tr><td>Mixed</td><td>Hagerf 2023, Hagerf 2024, Jabor 2023, Munekage 2016</td><td>Hagerf 2023</td><td>Hagerf 2023, Hagerf 2024</td><td>Hagerf 2023, Jabor 2023</td><td>Jabor 2023</td></tr><tr><td rowspan="3">Pancreatic surgery</td><td>DM</td><td>Krutkyte 2023</td><td>Krutkyte 2023</td><td>Krutkyte 2023</td><td>Krutkyte 2023</td><td>Krutkyte 2023</td></tr><tr><td>Non/pre-DM</td><td>Mao 2021</td><td>Mao 2021</td><td>Mao 2021</td><td>Mao 2021</td><td>Mao 2021</td></tr><tr><td>Mixed</td><td>Farmanov 2024, Jabor 2023</td><td>Farmanov 2024</td><td>Jabor 2023</td><td>Jabor 2023</td><td>Farmanov 2024, Jabor 2023</td></tr><tr><td rowspan="3">Transplant surgery</td><td>DM</td><td>NR</td><td>NR</td><td>Shaban 2023</td><td>NR</td><td>NR</td></tr><tr><td>Non/pre-DM</td><td>NR</td><td>NR</td><td>Shaban 2023</td><td>NR</td><td>NR</td></tr><tr><td>Mixed</td><td>Jabor 2023, Jo 2022, Yong Jin 2019</td><td>Jo 2022</td><td>Shaban 2023</td><td>Jabor 2023, Jo 2022</td><td>Jabor 2023, Jo 2022</td></tr></tbody></table>

NR, not reported
# Supplementary Methods S2 - MINORS: Methodological items for non-randomized studies

The score included 12 items, the first eight being specifically for non-comparative studies (see below). The items were scored 0 if not reported; 1 when reported but inadequate; and 2 when reported and adequate.

Blinding was considered non-critical for an unbiased assessment of the CGM, given the intervention's origin and the difficulty in implementation, resulting in a score of 2 when no other concerns were present. The overall methodological quality was evaluated as good for a score of 16 for non-comparative studies and 24 for comparative studies, as fair with scores 12-15 for non-comparative studies or 18-23 for comparative studies, and poor for 0-11 for non-comparative studies or 0-17 for comparative studies.

From: Karem Slim, Emile Nini, Damien Forestier, Fabrice Kwiatkowski, Yves Panis and Jacques Chipponi,

<table><thead><tr><th>Methodological items for non-randomized studies</th><th>Score†</th></tr></thead><tbody><tr><td>1. <strong>A clearly stated aim:</strong> the question addressed should be precise and relevant in the light of available literature</td><td></td></tr><tr><td>2. <strong>Inclusion of consecutive patients:</strong> all patients potentially fit for inclusion (satisfying the criteria for inclusion) have been included in the study during the study period (no exclusion or details about the reasons for exclusion)</td><td></td></tr><tr><td>3. <strong>Prospective collection of data:</strong> data were collected according to a protocol established before the beginning of the study</td><td></td></tr><tr><td>4. <strong>Endpoints appropriate to the aim of the study:</strong> unambiguous explanation of the criteria used to evaluate the main outcome which should be in accordance with the question addressed by the study. Also, the endpoints should be assessed on an intention-to-treat basis.</td><td></td></tr><tr><td>5. <strong>Unbiased assessment of the study endpoint:</strong> blind evaluation of objective endpoints and double-blind evaluation of subjective endpoints. Otherwise the reasons for not blinding should be stated.</td><td></td></tr><tr><td>6. <strong>Follow-up period appropriate to the aim of the study:</strong> the follow-up should be sufficiently long to allow the assessment of the main endpoint and possible adverse events</td><td></td></tr><tr><td>7. <strong>Loss to follow up less than 5%:</strong> all patients should be included in the follow up. Otherwise, the proportion lost to follow up should not exceed the proportion experiencing the major endpoint</td><td></td></tr><tr><td>8. <strong>Prospective calculation of the study size:</strong> information of the size of detectable difference of interest with a calculation of 95% confidence interval, according to the expected incidence of the outcome event, and information about the level for statistical significance and estimates of power when comparing the outcomes</td><td></td></tr><tr><td><strong>Additional criteria in the case of comparative study</strong></td><td></td></tr><tr><td>9. <strong>An adequate control group:</strong> having a gold standard diagnostic test or therapeutic intervention recognized as the optimal intervention according to the available published data</td><td></td></tr><tr><td>10. <strong>Contemporary groups:</strong> control and studied group should be managed during the same time period (no historical comparison)</td><td></td></tr><tr><td>11. <strong>Baseline equivalence of groups:</strong> the groups should be similar regarding the criteria other than the studied endpoints. Absence of confounding factors that could bias the interpretation of the results</td><td></td></tr><tr><td>12. <strong>Adequate statistical analyses:</strong> whether the statistics were in accordance with the type of study with calculation of confidence intervals or relative risk</td><td></td></tr></tbody></table>

Methodological Index For Non-Randomized Studies (MINORS): Development and validation of a new instrument, ANZ J. Surg. 2003; 73: 712-716

# Supplementary Methods S3 - Financial and non-financial conflict of interest.

Possible financial conflict of interest, suggested by significant commercial funding of the study or author financial (direct) conflict of interest, were assessed by two authors (AP and EG). We categorized each study as: "notable concern about conflict of interest", "no notable concern about conflict of interest", or "unclear concern about conflict of interests". In case of unclear or not reported conflict of interest, we tried to resolve the item assessing other papers published by the study authors. We reported details on authors having conflicts of interest (e.g., lead or corresponding authors, other authors) and the stage of the trial to which they contributed (design, conduct, analysis, reporting). We reported details on funding and sponsorship of the trial and whether the role of the funding body was reported for design, conduct, analysis, and reporting.¹

<footnote>†The items are scored 0 (not reported), 1 (reported but inadequate) or 2 (reported and adequate). The global ideal score being 16 for non-comparative studies and 24 for comparative studies.</footnote>
We also evaluated non-financial (indirect) conflict of interest for each author.¹,² We assessed various items including multiple publications, acknowledged expert, holding a position in or consulting for a relevant committee/board/group, and obvious personal belief in relation to continuous glucose monitoring, perioperative monitoring.

References:

1. Higgins J Thomas J, Chandler J, Cumpston M, Li T, Paige MJ, Welch VA. Cochrane Handbook for Systematic Reviews of Intervention version 6.2. Cochrane 2021;

2. Vandvik PO, Alhazzani W, Møller MH. Understanding conflicts of interest. Intensive Care Medicine Intensive Care Med; 2018; 44: 1738–40

<table><caption>**Supplementary Table S4 – Deviations from study protocol**</caption><thead><tr><td>Nature of the deviation</td><td>Description of the deviation</td><td>Reason for the deviation</td></tr></thead><tbody><tr><td>Type of data to be included</td><td>Type of data to be included</td><td>We decided to exclude preoperative data whenever possible to reduce heterogeneity, particularly due to the absence of systemic stress in response to surgery.</td></tr><tr><td>Type of data to be included</td><td>Type of data to be included in the aggregate analysis on glycaemic profile</td><td>During the review process, it was deemed appropriate to exclude from the aggregate analysis on glycemia profile studies that explicitly report non-blinding of the CGM values. This could influence perioperative glucose management compared to studies where CGM values were blinded. Furthermore, studies on pancreas transplantation were excluded due to their unique pathophysiology and perioperative glucose management.</td></tr><tr><td>Primary outcomes</td><td>Exclusion of 2 primary outcomes: the number of hypoglycaemia events and</td><td>We decided to exclude these two outcomes due to issues of underreporting and heterogeneity in the definition of outcomes,</td></tr></tbody></table>
<table><caption>Supplementary Table S5 - Major exclusion with reason</caption><tr><td></td><td>the number of hyperglycaemia events</td><td>which render the pooling and external interpretation of the results unfeasible.</td></tr><tr><td>Non-primary outcomes</td><td>New tertiary outcomes: device related adverse events and device dysfunction</td><td>We have opted to incorporate these two tertiary outcomes to evaluate safety and assess any device malfunctions.</td></tr><tr><td>Risk of bias</td><td>Use of MINORS tool for non-randomized trials</td><td>We decided to use this tool since most of the studies lacked a comparative arm.</td></tr><tr><td>Subgroup analyses</td><td>Removal of subgroup analysis</td><td>Owing to the scarcity of comparative studies, the heterogeneity, and the limited number of studies available, we conducted separate analyses for diabetic and non-diabetic patients, further stratifying them according to some surgical specialties. No analyses comparing these groups were performed.</td></tr></table>

<table><thead><tr><td>Trial</td><td>Format</td><td>Reference</td><td>Reason</td></tr></thead><tbody><tr><td>Aglialoro 2011</td><td>Abstract</td><td>Diabetes Technol Ther. 2011;13(2):200</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Amikishieva 2023</td><td>Abstract</td><td>Diabetes Technol Ther. 2023;25(0):A234</td><td>No outcomes of interest</td></tr><tr><td>Baba 2023</td><td>Full-text</td><td>Sci Rep. 2023 Mar 15;13(1):4299</td><td>Not in perioperative period</td></tr><tr><td>Bally 2018</td><td>Abstract</td><td>Diabetes 2018;67(suppl 1):350</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Beauharnais 2012</td><td>Abstract</td><td>Diabetes. 2012;61(0):A222</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Bjerkan 2024</td><td>Full-text</td><td>Obes Res Clin Pract. 2024 Jan-Feb;18(1):9-14</td><td>Not in perioperative period</td></tr><tr><td>Bochicchio 2017</td><td>Full-text</td><td>J Trauma Acute Care Surg. 2017 Jun;82(6):1049-1054.</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Bonner 2016</td><td>Abstract</td><td>Am J Transplant. 2016; 16(0):422</td><td>Not in perioperative period</td></tr><tr><td>Bublik 2023</td><td>Abstract</td><td>Diabetes Technol Ther. 2023;25(0):A57-A58</td><td>Not in perioperative period</td></tr><tr><td>Cummings 2023</td><td>Full-text</td><td>Diabetes Obes Metab. 2023 Aug;25(8):2191-2202</td><td>Not in perioperative period</td></tr></tbody></table>
<table><tr><td>Davis 2021</td><td>Full-text</td><td>Diabetes Care. 2021 Jul;44(7):1641-1646</td><td>Mixed population (surgical and medical)</td></tr><tr><td>del Rio 2014</td><td>Full-text</td><td>J Diabetes Sci Technol. 2014 Nov;8(6):1097-104</td><td>Not in perioperative period</td></tr><tr><td>Dorcely 2024</td><td>Full-text</td><td>Obes Sci Pract. 2024 Jan 4;10(1):e729</td><td>Not in perioperative period</td></tr><tr><td>Duan 2023</td><td>Full-text</td><td>Trials. 2023 Feb 1;24(1):72.</td><td>Study protocol</td></tr><tr><td>El Bouyousfi 2019</td><td>Abstract</td><td>Obes Surg. 2019;29(5):969</td><td>Sample size &lt; 10 patients</td></tr><tr><td>Elder 2017</td><td>Full-text</td><td>Pediatr Transplant. 2017 Nov;21(7)</td><td>Pediatric study</td></tr><tr><td>Fagher 2022</td><td>Abstract</td><td>Diabetes Technol Ther. 2022;24(0):A189</td><td>Overlapping population</td></tr><tr><td>Farmanov 2023</td><td>Abstract</td><td>Diabetes technology & therapeutics., 2023, Vol.25(S2), p.A-270-A-284</td><td>Overlapping population</td></tr><tr><td>Farmanov 2024</td><td>Abstract</td><td>Diabetes Res Clin Pract. 2024; 209(S1)</td><td>Overlapping population</td></tr><tr><td>Ferreira 2023</td><td>Full-text</td><td>Diabetes Technol Ther. 2023 Jul;25(7):467-475</td><td>Not in perioperative period</td></tr><tr><td>Fujino 2013</td><td>Full-text</td><td>Masui. 2013 Feb;62(2):140-6.</td><td>Sample size &lt; 10 patients</td></tr><tr><td>Galindo 2020</td><td>Full-text</td><td>Diabetes Care. 2020 Nov;43(11):2730-2735.</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Hagerf 2024</td><td>Abstract</td><td>Diabetes 2024;73(Supplement_1):1918-LB</td><td>Overlapping population</td></tr><tr><td>Hanaire 2011</td><td>Full-text</td><td>Diabetes Technol Ther. 2011 Jun;13(6):625-30.</td><td>Not in perioperative period</td></tr><tr><td>Jin 2019 LT</td><td>Full-text</td><td>J Diabetes Res. 2019 Nov 27;2019:1757182</td><td>Not in perioperative period</td></tr><tr><td>Joseph 2014</td><td>Abstract</td><td>Crit Care. 2014;18: P439</td><td>Mixed population (cardiac and non-cardiac surgery)</td></tr><tr><td>Kim 2023</td><td>Full-text</td><td>Paediatr Anaesth. 2023 Oct;33(10):862-867</td><td>Pediatric study</td></tr><tr><td>Kubota 2020</td><td>Full-text</td><td>Gastric Cancer. 2020 Jul;23(4):699-706. doi: 10.1007/s10120-019-01036-5</td><td>Not in perioperative period</td></tr><tr><td>Lampert 2018</td><td>Abstract</td><td>Pancreatology. 2018;18:S1-188</td><td>No outcomes of interest</td></tr><tr><td>Lorencio 2010</td><td>Abstract</td><td>Intensive Care Med. 2010;36(0):S217</td><td>No outcomes of interest</td></tr><tr><td>Lupoli 2022</td><td>Full-text</td><td>Biomedicines. 2022 Apr 16;10(4):916</td><td>Not in perioperative period</td></tr><tr><td>McCune 2023</td><td>Full-text</td><td>Clin Transplant. 2023 Apr;37(4):e14920</td><td>Not in perioperative period</td></tr><tr><td>McEachron 2021</td><td>Full-text</td><td>Transpl Int. 2021 Jan;34(1):87-96. doi: 10.1111/tri.13762</td><td>Not in perioperative period</td></tr><tr><td>Migdal 2020</td><td>Full-text</td><td>J Diabetes Sci Technol. 2020 Nov;14(6):1135-1136</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Nair 2017</td><td>Abstract</td><td>Diabetes. 2017;66(1):A229-A398</td><td>Sample size &lt; 10 patients</td></tr><tr><td>Nilsen 2024</td><td>Full-text</td><td>Surg Obes Relat Dis. 2024 Jan;20(1):10-16.</td><td>Not in perioperative period</td></tr><tr><td>Pasquel 2020</td><td>Full-text</td><td>Diabetes Care. 2020 Jun;43(6):1242-1248.</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Poljakova 2014</td><td>Abstract</td><td>Diabetes Technology & Therapeutics. 2014 Feb;A1-A162</td><td>Overlapping population</td></tr></table>
<table><tr><td>Prendin 2022</td><td>Full-text</td><td>Diabetes Obes Metab. 2022 Oct;24(10):2061-2065.</td><td>Not in perioperative period</td></tr><tr><td>Ramos-Levi 2023</td><td>Full-text</td><td>J Clin Med. 2023 Jun 27;12(13):4295.</td><td>Not in perioperative period</td></tr><tr><td>Ri 2021</td><td>Full-text</td><td>J Gastric Cancer. 2021 Dec;21(4):325-334</td><td>Not in perioperative period</td></tr><tr><td>Rilo 2013</td><td>Abstract</td><td>Transplantation. 2013;96(0):S8</td><td>Not in perioperative period</td></tr><tr><td>Russell 2012</td><td>Abstract</td><td>J Diabetes Sci Technol. 2012;6(1):A31</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Segev 2021</td><td>Full-text</td><td>J Clin Med. 2021 Apr 27;10(9):1893</td><td>Pediatric study</td></tr><tr><td>Segev 2021</td><td>Full-text</td><td>J Clin Med. 2021;10:1893.</td><td>Pediatric study</td></tr><tr><td>Shaban 2021</td><td>Abstract</td><td>https://anzsnasm.com/12845 [accessed on 28 Sept 2023]</td><td>Overlapping population</td></tr><tr><td>Sheehan 2022</td><td>Full-text</td><td>Diabetes Obes Metab. 2022 Jun;24(6):1021-1028. doi: 10.1111/dom.14665</td><td>Not in perioperative period</td></tr><tr><td>Shibamoto 2022</td><td>Full-text</td><td>Surg Today. 2022 Nov;52(11):1634-1644</td><td>Not in perioperative period</td></tr><tr><td>Shibamoto 2023</td><td>Full-text</td><td>Anticancer Res. 2023 Feb;43(2):857-864</td><td>Not in perioperative period</td></tr><tr><td>Shoda 2022</td><td>Full-text</td><td>Surg Today. 2022 Jun;52(6):889-895</td><td>Not in perioperative period</td></tr><tr><td>Somani 2023</td><td>Abstract</td><td>Diabetes 2023;72(S1):242</td><td>Not in perioperative period</td></tr><tr><td>Spanakis 2022</td><td>Full-text</td><td>Diabetes Care 2022;45:2369-2375</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Tiberi 2023</td><td>Full-text</td><td>J Pediatr Endocrinol Metab. 2023 May 31;36(8):803-807.</td><td>Sample size < 10 patients</td></tr><tr><td>Tripyla 2020</td><td>Abstract</td><td>Diabetes. 2020;69(0)</td><td>Overlapping population</td></tr><tr><td>Umpierrez 2022</td><td>Abstract</td><td>Diabetologia. 2022;65:S53</td><td>Overlapping population</td></tr><tr><td>Varshney 2019</td><td>Abstract</td><td>HPB. 2019;21:S432</td><td>Sample size < 10 patients</td></tr><tr><td>Vonderau 2021</td><td>Full-text</td><td>Clin Transplant. 2021 Nov;35(11):e14450</td><td>Not in perioperative period</td></tr><tr><td>Vonderau 2021</td><td>Full-text</td><td>Clin Transplant. 2021 Nov;35(11):e14450.</td><td>Not in perioperative period</td></tr><tr><td>Wasiq 2023</td><td>Full-text</td><td>Eur J Pediatr. 2023 Mar;182(3):1083-1087.</td><td>Pediatric study</td></tr><tr><td>Watanabe 2012</td><td>Abstract</td><td>Clin Nutr Suppl. 2012;7(1):132-133</td><td>Pediatric study</td></tr><tr><td>Wollersheim 2016</td><td>Full-text</td><td>Ann Intensive Care. 2016 Dec;6(1):70</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Yamashita 2008</td><td>Full-text</td><td>Acta Anaesthesiol Scand 2009; 53: 66-71</td><td>Use of an artificial pancreas device</td></tr><tr><td>Yang 2019</td><td>Abstract</td><td>HPB. 2019; 21(S2):S482</td><td>Not in perioperative period</td></tr><tr><td>Yatabe 2011</td><td>Full-text</td><td>Crit Care Med. 2011 Mar;39(3):575-8</td><td>Mixed population (surgical and medical)</td></tr><tr><td>Zaimoku 2017</td><td>Abstract</td><td>J Hepato-Biliary-Pancreatic Sci. 2017; 24(0):A261</td><td>Sample size < 10 patients</td></tr></table>
<table><caption>Supplementary Table S6 - Additional study characteristics</caption><thead><tr><th>Study</th><th>Study timeline</th><th>Age (years)</th><th>Female (%)</th><th>BMI</th><th>ASA score</th><th>Estimated duration of CGM monitoring (days)</th><th>Use of a perioperative insulin protocol</th><th>Blinding of CGM data</th></tr></thead><tbody><tr><td>Carlsson 2023</td><td>2020-2021</td><td>68.3 ± 10.6</td><td>34%</td><td>27 ± 5.4</td><td>ASA 2: 41; ASA 3: 57; ASA 4: 2</td><td>4</td><td>Yes</td><td>Yes</td></tr><tr><td>Fagher 2023</td><td>2017-2019</td><td>69.7 ± 9.8</td><td>47%</td><td>NR</td><td>NR</td><td>5</td><td>Yes</td><td>No</td></tr><tr><td>Farmanov 2024</td><td>2020-2022</td><td>65</td><td>62%</td><td>NR</td><td>NR</td><td>Early postoperative period (before enteral nutrition)</td><td>Yes</td><td>No</td></tr><tr><td>Hagerf 2023</td><td>2023 or before</td><td>64.5 ± 18.8</td><td>14%</td><td>24.35 ± 7.3</td><td>NR</td><td>9</td><td>Yes</td><td>NR</td></tr><tr><td>Hagerf 2024</td><td>2022-</td><td>60 ± 16.7</td><td>38%</td><td>26.9 ± 4.5</td><td>NR</td><td>7 (length of ICU stay)</td><td>Yes</td><td>NA</td></tr><tr><td>Herzig 2022</td><td>2020-2021</td><td>68.45 ± 12.3</td><td>34%</td><td>30.1 ± 5.8</td><td>NR</td><td>9</td><td>No</td><td>Yes</td></tr><tr><td>J labor 2023</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>9</td><td>NR</td><td>NR</td></tr><tr><td>Jo 2022</td><td>2021-2022</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>28</td><td>NR</td><td>NR</td></tr><tr><td>Kim 2022</td><td>2020-2021</td><td>47.2 ± 9.1</td><td>75%</td><td>37.2 ± 5.7</td><td>NR</td><td>6</td><td>NR</td><td>NR</td></tr><tr><td>Krutkyte 2023</td><td>2020-2021</td><td>69.9 ± 17.6</td><td>54%</td><td>27.9 ± 3</td><td>NR</td><td>12</td><td>Yes</td><td>Yes</td></tr><tr><td>Leung 2023</td><td>2020-2022</td><td>69.1 ± 6.6</td><td>82%</td><td>28.8 ± 3.8</td><td>NR</td><td>4</td><td>NA</td><td>No</td></tr><tr><td>Maeda 2019</td><td>2016-2017</td><td>69.6 ± 6.7</td><td>75%</td><td>26.1 ± 4.1</td><td>NR</td><td>6</td><td>Yes</td><td>No</td></tr><tr><td>Mao 2021</td><td>2020</td><td>57.1 ± 13.4</td><td>61%</td><td>21.21±2.51</td><td>NR</td><td>10</td><td>No</td><td>Yes</td></tr><tr><td>Mittal 2015</td><td>2013-2014</td><td>43.7 ± 10.1</td><td>NR</td><td>NR</td><td>NR</td><td>6</td><td>Yes</td><td>Yes</td></tr><tr><td>Munekage 2016</td><td>2015 or before</td><td>69 ± 9</td><td>27%</td><td>24 ± 5</td><td>NR</td><td>NR</td><td>Yes</td><td>Yes</td></tr><tr><td>Poljakova 2013</td><td>2010-2011</td><td>69</td><td>47%</td><td>NR</td><td>NR</td><td>3</td><td>NR</td><td>No</td></tr><tr><td>Price 2023</td><td>2022 or before</td><td>61.8 ± 10.8</td><td>43%</td><td>32.5 ± 6.9</td><td>NR</td><td>NR</td><td>No</td><td>Yes</td></tr><tr><td>Shaban 2023</td><td>2020-2021</td><td>60 ± 11.1</td><td>29%</td><td>28.5 ± 3.0</td><td>NR</td><td>14</td><td>No</td><td>Yes</td></tr><tr><td>Tripyla 2020</td><td>2019-2020</td><td>69 ± 13</td><td>25%</td><td>27.3 ± 6.6</td><td>NR</td><td>&gt; 7</td><td>NR</td><td>Yes</td></tr><tr><td>Turquetil 2021</td><td>2016-2017</td><td>47 ± 2</td><td>68%</td><td>41.9 ± 5.3</td><td>NR</td><td>14</td><td>NR</td><td>Yes</td></tr><tr><td>Hagerf 2023</td><td>2023 or before</td><td>64.5 ± 18.8</td><td>14%</td><td>24.35 ± 7.3</td><td>NR</td><td>9</td><td>Yes</td><td>NR</td></tr><tr><td>Wang 2021</td><td>2015-2017</td><td>35.0 ± 12.6</td><td>51%</td><td>41.0 ± 6.8</td><td>NR</td><td>6</td><td>NR</td><td>Yes</td></tr><tr><td>Wysocki 2019</td><td>2019 or before</td><td>43.1 ± 13.4</td><td>63%</td><td>43.3 ± 5.7</td><td>ASA 2: 87%; ASA 3: 20%</td><td>10</td><td>Unclear</td><td>Yes</td></tr><tr><td>Wysocki 2024</td><td>2020-2023</td><td>45 ± 10</td><td>70%</td><td>47.5 ± 5.9</td><td>ASA 2: 12%; ASA 3: 88%</td><td>14</td><td>NR</td><td>Yes</td></tr><tr><td>Yin 2022</td><td>2019</td><td>68.29 ± 8.48</td><td>31%</td><td>25.48 ± 4.24</td><td>NR</td><td>5</td><td>No</td><td>Yes</td></tr><tr><td>Yip 2014</td><td>2010-2012</td><td>42.3 ± 7.7</td><td>76%</td><td>43.3 ± 5.1</td><td>NR</td><td>6</td><td>NR</td><td>Yes</td></tr><tr><td>Yong Jin 2019</td><td>2017-2018</td><td>50.2 ± 9.0</td><td>39%</td><td>24.0 ± 3.1</td><td>NR</td><td>7</td><td>NR</td><td>NR</td></tr><tr><td>Zhang 2024</td><td>2022-2023</td><td>67.2 ± 8.9</td><td>32%</td><td>22.8 ± 2.3</td><td>NR</td><td>10</td><td>NR</td><td>NR</td></tr></tbody></table>

ASA, American Society of Anesthesiologists; BMI, body mass index; CGM, continuous glucose monitoring; ICU, intensive care unit; NA, not applicable; NR, not reported.
<table><caption>Supplementary Table S7 – Risk of bias results for nonrandomized trials: MINORS</caption><thead><tr><th>Study</th><th>A clearly stated aim</th><th>Inclusion of consecutive patients</th><th>Prospective collection of data</th><th>Endpoints appropriate to the aim of the study</th><th>Unbiased assessment of the study endpoint</th><th>Follow-up period appropriate to the aim of the study</th><th>Loss to follow-up less than 5%</th><th>Prospective calculation of the study size</th><th>An adequate control group</th><th>Contem porary groups</th><th>Baseline equivalent of groups</th><th>Adequate statistical analyses</th><th>TOTAL for non-randomized studies and comparative studies</th><th>Overall methodologic al quality</th></tr></thead><tbody><tr><td>Carlsson 2023</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>14</td><td>Fair</td></tr><tr><td>Fagler 2023</td><td>2</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>1</td><td>1</td><td>14</td><td>Poor</td></tr><tr><td>Farmanov 2024</td><td>1</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>1</td><td>1</td><td>2</td><td>1</td><td>1</td><td>14</td><td>Poor</td></tr><tr><td>Hagerf 2023</td><td>1</td><td>0</td><td>2</td><td>2</td><td>1</td><td>2</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>1</td><td>1</td><td>14</td><td>Poor</td></tr><tr><td>J labor 2023</td><td>2</td><td>0</td><td>0</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>Poor</td></tr><tr><td>Jo 2022</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>Poor</td></tr><tr><td>Kim 2022</td><td>2</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>15</td><td>Fair</td></tr><tr><td>Leung 2023</td><td>2</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>Poor</td></tr><tr><td>Maeda 2019</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>Poor</td></tr><tr><td>Mao 2021</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>Poor</td></tr><tr><td>Mittal 2015</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>2</td><td>2</td><td>1</td><td>1</td><td>10</td><td>Poor</td></tr><tr><td>Munckage 2016</td><td>2</td><td>0</td><td>2</td><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>0</td><td>2</td><td>2</td><td>1</td><td>1</td><td>15</td><td>Poor</td></tr><tr><td>Pojjakova 2013</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>Poor</td></tr><tr><td>Price 2023</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>Poor</td></tr><tr><td>Shaban 2023</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>13</td><td>Fair</td></tr><tr><td>Tripyla 2020</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td><td>Fair</td></tr><tr><td>Turquetil 2021</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>Poor</td></tr><tr><td>Wang 2021</td><td>1</td><td>0</td><td>1</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>Poor</td></tr><tr><td>Wysocki 2019</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td><td>Fair</td></tr><tr><td>Wysocki 2024</td><td>2</td><td>0</td><td>2</td><td>2</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td><td>Poor</td></tr><tr><td>Yin 2022</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td><td>Poor</td></tr><tr><td>Yip 2014</td><td>1</td><td>0</td><td>0</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>Poor</td></tr><tr><td>Yong Jin 2019</td><td>1</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8</td><td>Poor</td></tr><tr><td>Zhang 2024</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>Poor</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Supplementary Figure S1 — Risk of bias results for randomized trials: ROB2</figcaption></figure>

Image created using robvis. Reference: McGuinness, LA, Higgins, JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Syn Meth. 2020; 1-7.

https://doi.org/10.1002/jrsm.1411
<table><caption>Supplementary Table S8 – Details on conflicts of interests</caption><thead><tr><th>Trial</th><th>OVERALL Col</th><th>Industrial funding?</th><th>Details on funding</th><th>Authors with a conflict of interest?</th><th>Details on authors Conflicts of interests</th><th>Stage of trial contribution (design, conduct, analysis, reporting)</th><th>Authors with a non-financial conflict of interest</th></tr></thead><tbody><tr><td>Carlsson 2023</td><td>Notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>Yes</td><td>2nd co-last author, reports direct and indirect research funding from Ferring Pharmaceuticals, Merck, Sharp & Dohme Corp. and Boehringer Ingelheim outside the submitted work as well as lecture fees from Radiometer. 3rd co-last author: reports institutional research funding from Norpharma A/S outside the submitted work as well as lecture fees from Radiometer. Second author: has served as a consultant for the following CGM producing companies Medtronic, Dexcom and Abbott, and her institution has received research funding from Medtronic and Dexcom. 1st co-last author, 2nd co-last author, and 3rd co-last author: have founded a start-up company, WARD247 ApS, to pursue the regulatory and commercial activities of the WARD-project. WARD247 ApS has finalized terms for license agreement for any WARD-project software and patents</td><td>2<sup>nd</sup> co-last author, 3<sup>rd</sup> co-last author, 2<sup>nd</sup> author: conception, design, conduct, reporting</td><td>Yes (2nd and last author: multiple publications and acknowledged experts)</td></tr><tr><td>Fagher 2023</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Fermanov 2024</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Hagerf<sup>†</sup> 2023</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Hagerf<sup>†</sup> 2024</td><td>Notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>Yes</td><td>13th author: a consultant to Better Therapeutics, Eoflow, GlucoTrack, Life-care, Nevro, Novo Nordisk, Sanofi, and Third-way</td><td>13th author: involved in the data analysis and interpretation, discussion, and critical revision and editing of the manuscript. Provided (with another author) newly developed software support for data analysis, and reviewed and edited the manuscript. Reviewed the manuscript and approved the final version</td><td>No</td></tr><tr><td>Herzig 2022</td><td>Notable concern about conflict of interest</td><td>Yes</td><td>Dexcom provided product support (CGM equipment).</td><td>Yes</td><td>11th author reports receiving license fees from B. Braun and patents related to the closed-loop field and being a consultant at CamDiab Ltd. 16th author reports receiving speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk; license fees from B. Braun and Medtronic; consulting fees from Abbott Diabetes Care, patents related to the closed-loop field with the University of Cambridge and the University of Cambridge and Abbott Diabetes Care, being director and stockholder at CamDiab Ltd., and having a leadership/fiduciary role for Advanced Technologies & Treatments for Diabetes. 17th author reports advisory board fees from MSD.</td><td>11<sup>th</sup> and 16<sup>th</sup> author: data analysis, 17<sup>th</sup> author: screening and enrolment of patients, consent, patient care, data analysis, design</td><td>Yes (1st, 11th, and last author: multiple publications and/or acknowledged experts)</td></tr><tr><td>J labor 2023</td><td>Notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>Yes</td><td>M. Haluzik is on the advisory panel of: Advisory Panel; Novo Nordisk, Lilly Diabetes, Boehringer Ingelheim, Research Support, Sanofi, Speaker's Bureau; Abbott, AstridZeneca p</td><td>NR</td><td>No</td></tr><tr><td>Jo 2022</td><td>Unclear concern about conflict of interest</td><td>Not Reported</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>No</td></tr><tr><td>Kim 2022</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Leung 2023</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Maeda 2019</td><td>No notable concern about conflict of interest</td><td>No</td><td>N/A</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr></tbody></table>
<table><tr><td>Mao 2021</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Midal 2015</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>Yes (1st and last author: multiple publications and acknowledged experts)</td></tr><tr><td>Mumekage 2016</td><td>Unclear concern about conflict of interest</td><td>Not Reported</td><td>NR</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Poljakova 2013</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Price 2023</td><td>Notable concern about conflict of interest</td><td>Yes</td><td>CGM devices were supplied at no cost by Dexcom and Abbott.</td><td>Yes</td><td>3rd author discloses that he receives research support from Abbott Diabetes.</td><td>Not reported</td><td>No</td></tr><tr><td>Shabon 2023</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Trayola 2020</td><td>Notable concern about conflict of interest</td><td>Yes</td><td>The study was supported by the Dexcom External Research Program.</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Turquetil 2021</td><td>Notable concern about conflict of interest</td><td>No</td><td>NA</td><td>Yes</td><td>3rd author has performed clinical trials as a co-investigator for Medtronic, Eli Lilly, Novo Nordisk, Sanofi and Abbott Laboratories; provided advisory services to Medtronic, Abbott, Novo Nordisk, Eli Lilly, Sanofi, Bayer, AstraZeneca, MSD, Boehringer Ingelheim and Air Liquide Sante International; and attended conferences organized by Novo Nordisk, Eli Lilly, Sanofi, Medtronic, Abbott, LifeScan, MSD and AstraZeneca as a contributor. Last author has done clinical trials as a co-investigator for Medtronic, Eli Lilly and Novo Nordisk; provided advisory services to Medtronic, Abbott Laboratories, Novo Nordisk, Eli Lilly and Air Liquide Sante; attended conferences organized by Novo Nordisk, Eli Lilly, Sanofi and Medtronic as a contributor, and received investigator's fees from Medtronic.</td><td>3rd author: data analysis, review, data collection, Last author: conception, writing, data analysis, data collection, guarantor of this work (full access to the data)</td><td>Yes (2nd and last author: multiple publications and/or acknowledged experts)</td></tr><tr><td>Wang 2021</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Wysocki 2019</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>Yes (1st and last author: multiple publications and acknowledged experts)</td></tr><tr><td>Wysocki 2024</td><td>No notable concern about conflict of interest</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>Yes (1st and last author: multiple publications and acknowledged experts)</td></tr><tr><td>Yin 2022</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Yip 2014</td><td>Unclear concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>Yes (last author)</td></tr><tr><td>Yang Jin 2019</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr><tr><td>Zhang 2024</td><td>No notable concern about conflict of interest</td><td>No</td><td>NA</td><td>No</td><td>NA</td><td>NA</td><td>No</td></tr></table>

CGM, continuous glucose monitoring; NA, not applicable; NR, not reported.
<table><caption>Supplementary Table S9 – Device related adverse events, device replacement, and device dysfunction.</caption><thead><tr><th rowspan="2">Study</th><th colspan="3">DRAEs</th><th colspan="4">Localization (number of patients)</th><th colspan="3">Replacement (number of events)</th><th colspan="2">Device dysfunction</th></tr><tr><th>Yes</th><th>Patients</th><th>%</th><th>Arm</th><th>Abdomen</th><th>Infraclavicular</th><th>Yes</th><th>Patients</th><th>Yes</th><th>Patients</th><th>%</th></tr></thead><tbody><tr><td>Carlsson 2023</td><td>0</td><td>70</td><td>0,00%</td><td>79</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>9</td><td>85</td><td>10.59%</td></tr><tr><td>Fagher 2023</td><td>NR</td><td>NR</td><td>NR</td><td>100</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>12</td><td>100</td><td>12.00%</td></tr><tr><td>Farmanov 2024</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Hagerf 2023</td><td>NR</td><td>NR</td><td>NR</td><td>7</td><td>0</td><td>7</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Hagerf 2024</td><td>2</td><td>65</td><td>3.08%</td><td>0</td><td>0</td><td>65</td><td>NR</td><td>NR</td><td>2</td><td>65</td><td>3.08%</td></tr><tr><td>Herzig 2022</td><td>0</td><td>44</td><td>0.00%</td><td>NR</td><td>NR</td><td>NR</td><td>3</td><td>23</td><td>2</td><td>22</td><td>9.09%</td></tr><tr><td>Jabor 2023</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>0</td><td>61</td><td>3</td><td>61</td><td>3</td><td>61</td><td>4.92%</td></tr><tr><td>Jo 2022</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Kim 2022</td><td>0</td><td>20</td><td>0.00%</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>20</td><td>0</td><td>20</td><td>0.00%</td></tr><tr><td>Leung 2023</td><td>NR</td><td>NR</td><td>NR</td><td>83</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Maeda 2019</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>26</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Mao 2021</td><td>NR</td><td>NR</td><td>NR</td><td>18</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Mittal 2015</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>30</td><td>0</td><td>0</td><td>30</td><td>4</td><td>30</td><td>13.33%</td></tr><tr><td>Munekage 2016</td><td>NR</td><td>NR</td><td>NR</td><td>19</td><td>0</td><td>0</td><td>0</td><td>19</td><td>1</td><td>19</td><td>5.26%</td></tr><tr><td>Poljakova 2013</td><td>0</td><td>17</td><td>0.00%</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>17</td><td>0</td><td>17</td><td>0.00%</td></tr><tr><td>Price 2023</td><td>0</td><td>76</td><td>0.00%</td><td>76</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>12</td><td>94</td><td>12.77%</td></tr><tr><td>Shaban 2023</td><td>NR</td><td>NR</td><td>NR</td><td>21</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Tripyla 2020</td><td>2</td><td>20</td><td>10.00%</td><td>20</td><td>0</td><td>0</td><td>7</td><td>20</td><td>7</td><td>20</td><td>35%</td></tr><tr><td>Turquetil 2021</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>2</td><td>33</td><td>6.06%</td></tr><tr><td>Wang 2021</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Wysocki 2019</td><td>NR</td><td>NR</td><td>NR</td><td>32</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Wysocki 2024</td><td>NR</td><td>NR</td><td>NR</td><td>32</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Yin 2022</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Yip 2014</td><td>NR</td><td>NR</td><td>NR</td><td>0</td><td>31</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Yong Jin 2019</td><td>NR</td><td>NR</td><td>NR</td><td>31</td><td>0</td><td>0</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td></tr><tr><td>Zhang 2024</td><td>2</td><td>42</td><td>4.76%</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>NR</td><td>2</td><td>42</td><td>4.76%</td></tr></tbody><tfoot><tr><td>TOTAL:</td><td>6</td><td>354</td><td>1.69%</td><td>486</td><td>87</td><td>133</td><td>12</td><td>199</td><td>56</td><td>608</td><td>9.21%</td></tr></tfoot></table>

DRAE, device-related adverse event; NR, not reported.
## 4. DISCUSSION

Ce travail de thèse examine le rôle émergent des CGM dans le contexte périopératoire en chirurgie non cardiaque. La revue systématique menée montre que les profils glycémiques captés par les CGM varient de manière significative selon les types d'interventions chirurgicales et les populations de patient-es. Cette variabilité est observée à la fois entre patient-es diabétiques et non diabétiques, mais aussi entre différents types de chirurgies. Une telle hétérogénéité souligne la complexité de la régulation glycémique en période périopératoire et met en évidence les limites des approches conventionnelles.

Les résultats suggèrent que les CGM pourraient représenter un outil pertinent pour la gestion périopératoire de la glycémie, en particulier chez les patient-es à haut risque chirurgical et à haut risque de dysglycémie. Dans ces populations, les CGM pourraient permettre une détection précoce de l'hyperglycémie comme de l'hypoglycémie, et favoriser une intervention rapide. En fournissant des données continues, quasi instantanées, sur les concentrations de glucose, les CGM offrent une vision dynamique des fluctuations glycémiques, impossible à obtenir avec les simples mesures ponctuelles par tests POC. Cela permettrait aux clinicien-nes d'intervenir avant que des seuils critiques ne soient franchis. La gestion des trajectoires glycémiques, plutôt que l'interprétation de valeurs isolées, pourrait théoriquement s'avérer déterminante pour atténuer les effets de la variabilité glycémique.

Malgré ces perspectives prometteuses, les données actuellement disponibles restent limitées. Un obstacle majeur à l'évaluation de la réelle valeur clinique des CGM dans ce contexte est la rareté des études comparatives. La majorité de la littérature existante repose sur des études observationnelles sans groupe contrôle, et seules quelques-unes comparent directement les CGM aux méthodes classiques de surveillance POC. En conséquence, il n'a pas été possible de déterminer dans quelle mesure les CGM permettent d'améliorer le contrôle glycémique postopératoire ou de réduire l'incidence des complications par rapport aux stratégies
conventionnelles. Cette carence en données renforce la nécessité urgente d'essais contrôlés
randomisés (ECR) rigoureux et d'études de cohorte à large échelle afin d'évaluer l'efficacité
et la sécurité des CGM en période périopératoire.

Un autre aspect important concerne la performance et la sécurité des dispositifs eux-mêmes.
Les événements indésirables liés à l'utilisation des CGM ont été rarement rapportés, et
lorsqu'ils l'ont été, ils étaient généralement bénins. Toutefois, une grande hétérogénéité a été
observée dans la définition et la déclaration des dysfonctionnements ou incidents liés aux
dispositifs. Environ 10 % des CGM présentaient un dysfonctionnement, allant du délogement
accidentel du capteur à des anomalies de lecture ou de communication. Bien que ces
incidents n'aient pas semblé entraîner de conséquences graves dans les études analysées, ils
soulèvent des interrogations importantes sur la fiabilité, la formation des utilisateurs et la
gestion des dispositifs dans l'environnement complexe des soins chirurgicaux. Il apparaît
donc essentiel de mettre en place une approche systématique et standardisée pour l'évaluation
et le signalement des dysfonctionnements et événements indésirables liés aux CGM dans les
futures études.

Bien que les CGM ne fassent pas encore partie de la pratique clinique courante en chirurgie
non cardiaque, leurs bénéfices potentiels sont de plus en plus reconnus (70). Leur capacité à
détecter précocement les excursions glycémiques et à décrire en détail les tendances intra- et
postopératoires du glucose offre aux clinicien-nes une nouvelle dimension dans la prise en
charge périopératoire (70). En particulier chez les patient-es ayant une susceptibilité accrue à
la dysglycémie, les CGM pourraient permettre une adaptation plus fine des stratégies de
gestion glycémique et, potentiellement, améliorer le pronostic.

L'identification d'une population cible clairement définie, à risque élevé de dysglycémie et de
complications postopératoires, sera vraisemblablement essentielle pour faire progresser
l'intégration clinique des CGM. Dans de tels groupes, les CGM pourraient fournir des données exploitables en temps réel, renforcer la sécurité et orienter les plans de prise en charge postopératoire. Enfin, les approches personnalisées du contrôle glycémique rendues possibles par les CGM pourraient marquer un changement de paradigme : passer d'une stratégie réactive à une stratégie proactive de gestion glycémique.

## 5. CONCLUSIONS

Cette thèse met en lumière le potentiel de la CGM pour transformer la surveillance glycémique en période périopératoire dans le cadre de la chirurgie non cardiaque. Bien que les données actuelles suggèrent une utilité prometteuse, en particulier chez des populations à haut risque sélectionnées, l'absence d'études comparatives robustes limite la formulation de conclusions définitives.

Pour faire progresser ce domaine, les recherches futures devraient se concentrer sur :

(1) la standardisation des critères de jugement et du reporting des résultats, en particulier concernant les plages glycémiques, les événements indésirables liés aux dispositifs et la fiabilité des capteurs ;

(2) des comparaisons directes – idéalement des ECR – avec les modalités de référence telles que la surveillance POC ;

(3) l'identification des sous-groupes chirurgicaux les plus susceptibles de bénéficier de l'utilisation des CGM.

Une meilleure compréhension de l'impact des CGM sur les critères centrés sur les patient-es sera essentielle pour favoriser leur adoption à plus large échelle en médecine périopératoire.
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## REMERCIEMENTS

Je dédie cette thèse à ma famille, sans qui ce travail n'aurait pas été possible. Ivana, Filiberto, Costantino et Paola, votre soutien inconditionnel — et votre patience ! — ont été inestimables. À tous mes amis en Suisse, en Italie, en Angleterre et ailleurs dans le monde. Enfin, à mon chien Zac, qui nous a récemment quittés, mais qui nous a accompagnés fidèlement pendant tant d'années.

À mon directeur de thèse, le Professeur Eduardo Schiffer : je vous remercie pour votre soutien et votre engagement, tant envers moi qu'envers mes collègues, dans nos activités cliniques et de recherche au sein du département.

Enfin, je souhaite exprimer ma gratitude aux figures les plus marquantes que j'ai rencontrées au cours de ma carrière de clinicien et de chercheur, dans l'ordre chronologique de notre rencontre : Luigi Beretta, Giacomo Monti, Nicole Proz, Matthias Hanggi, Rosa Maria Moresco, Giovanni Landoni, Rinaldo Bellomo, Tiziano Cassina, Sonia Chimchila Chevili, Martin Tramer, Eduardo Schiffer, George Savoldelli, Bernardo Bollen Pinto, Ramani Moonesinghe, Carolina Romero, Arash Afshari.

Le résumé, l'introduction, la discussion et les conclusions ont été rédigés initialement en anglais. La traduction en français scientifique a été effectuée avec l'assistance de ChatGPT-4o,ª puis relue attentivement et modifiée en fonction des besoins.

<footnote>ª OpenAI. ChatGPT (GPT-4o) [Internet]. San Francisco (CA) : OpenAI ; 2024 [cité le 20 mai 2025]. Disponible sur : https://openai.com/chatgpt</footnote>