# Survival after ICU discharge is shaped more by chronic disease than admission severity

Alice Blet, Anouk Walter-Petrich, Ményssa Chérifa, Jolie Bruno, Alexy Tran

Dinh, Elie Azoulay, Benjamin Chousterman, Gad Cotter, Beth Davison,
Benjamin Deniau, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Alice Blet, Anouk Walter-Petrich, Ményssa Chérifa, Jolie Bruno, Alexy Tran Dinh, et al. Survival after ICU discharge is shaped more by chronic disease than admission severity. Journal of Critical Care, 2026, 92, pp.155401. 10.1016/j.jcrc.2025.155401. inserm-05502623

HAL Id: inserm-05502623

https://inserm.hal.science/inserm-05502623v1

Submitted on 10 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

Survival after ICU discharge is shaped more by chronic disease than
admission severity

Alice Blet<sup>a,b,c,1</sup>, Anouk Walter-Petrich<sup>d,e,1</sup>, Ményssa Chérifa<sup>e</sup>, Jolie Bruno<sup>b,c</sup>,
Alexy Tran Dinh<sup>f,g</sup>, Elie Azoulay<sup>b,h</sup>, Benjamin Chousterman<sup>a,b,c</sup>, Gad Cotter<sup>b,i</sup>, Beth Davison<sup>b,i</sup>,
Benjamin Deniau<sup>a,b,c</sup>, Etienne Gayat<sup>a,b,c</sup>, Bertrand Guidet<sup>j,k</sup>, Anatole Harrois<sup>l</sup>,
Antoine Kimmoun<sup>m</sup>, Marc Leone<sup>n</sup>, Xavier Wittebole<sup>o</sup>, Andrew P. Ambrosy<sup>p</sup>,
Alexandre Mebazaa<sup>a,b,c,*2</sup>, Matthieu Resche-Rigon<sup>b,d,e,2</sup>

ARTICLE INFO

Keywords:

ICU mortality

Charlson comorbidity index

SOFA

SAPS II

Biomarkers

A B S T R A C T

**Background:** Mortality is high both during intensive care unit (ICU) stay and in the year following discharge, yet factors influencing long-term survival remain poorly defined. We hypothesized that pre-existing chronic conditions may be more strongly associated with post-ICU survival than acute illness severity or admission diagnosis.

**Methods:** This is a *post-hoc* analysis of the prospective, observational, multicenter FROG-ICU cohort, which included all consecutive patients admitted to 21 French ICUs and followed for one year after discharge. ICU survivors with complete data on admission severity scores (Sequential Organ Failure Assessment [SOFA], Simplified Acute Physiology Score II [SAPS-II]), comorbidities (Charlson Comorbidity Index [CCI]), and cardiovascular/renal biomarkers at discharge (n = 1400) were included. Associations with one-year mortality were assessed using Cox models. Discriminatory performance was evaluated with time-dependent area under the receiver operating characteristic curve (AUC).

**Results:** Among 1548 ICU survivors, 1400 were analyzed (median age 61 years, 63 % male). Admission diagnoses included acute respiratory failure (19 %), septic shock (24 %), and neurologic conditions (16 %). The CCI was the strongest predictor of mortality at day 7 (AUC 0.70 [95 % Confidence Interval [CI], 0.64–0.77]), at 3 weeks (AUC

**Abbreviations:** AUC, Area Under the Curve; bio-ADM, Bioactive Adrenomedullin; CCI, Charlson Comorbidity Index; eGFR, Estimated Glomerular Filtration Rate; hs-cTnT, High-Sensitivity Cardiac Troponin T; ICU, Intensive Care Unit; IQR, Interquartile Range; NT-proBNP, N-Terminal pro-Brain Natriuretic Peptide; penKid, Proenkephalin A 119–159; PICS, Post-Intensive Care Syndrome; pNGAL, Plasma Neutrophil Gelatinase-Associated Lipocalin; SAPS II, Simplified Acute Physiology Score II; SOFA, Sequential Organ Failure Assessment; uNGAL, Urine Neutrophil Gelatinase-Associated Lipocalin.

* Corresponding author at: Department of Anesthesiology and Intensive Care Saint Louis, - Lariboisière University Hospitals, 2 rue Ambroise Paré, 75010 Paris, INSERM UMR-S 942, MASCOT unit, France.

E-mail address: alexandre.mebazaa@aphp.fr (A. Mebazaa).

1 AB and AWP contributed equally to this work.

2 AM and MRR contributed equally to this work.

<footnote><sup>a</sup> AP-HP, Lariboisière - Saint-Louis Hospitals, Department of Anesthesia, Critical Care and Burn Center, Paris, France</footnote>

<footnote>b Université Paris Cité, Inserm, Paris, France</footnote>

<footnote>c INSERM U942 MASCOT, Inserm, Paris, France</footnote>

<footnote>d SBIM, AP-HP, Saint-Louis Hospital, Paris, France</footnote>

<footnote>e ECSTRRA Team - CRESS - UMR 1153, INSERM, Paris, France</footnote>

<footnote>f AP-HP, Hôpital Bichat Claude Bernard, Département d'Anesthésie-Réanimation, Paris, France</footnote>

<footnote>g INSERM UMR 1148 LVTS, Paris, France</footnote>

<footnote>h Medical ICU, Saint-Louis Hospital, Paris, France</footnote>

<footnote>Momentum Research, Durham, NC, USA</footnote>

<footnote>j Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP UMRS 1136), Equipe 6, Paris, France</footnote>

<footnote>k APHP, Hôpital Saint-Antoine, Service de Réanimation Médicale, Paris, France</footnote>

<footnote>l Department of Anesthesiology and Critical Care, AP-HP, Université Paris Saclay, Le Kremlin Bicêtre, France</footnote>

<footnote>m Université de Lorraine, CHRU de Nancy, Service de Médecine Intensive et Réanimation Brabois, F CRIN INI-CRCT, Nancy, France</footnote>

<footnote>n Aix Marseille University, Assistance Publique Hôpitaux de Marseille, Hospital Nord, Department of Anesthesiology and Intensive Care Unit, Marseille, France</footnote>

<footnote>° Soins Intensifs, Cliniques universitaires Saint-Luc, Brussels, Belgium</footnote>

<footnote>P Kaiser Permanente Northern California - Division of Research, Pleasanton, CA, USA.</footnote>
0.75 [0.69–0.78]), and remained high over one year, outperforming SAPS-II (0.63 [0.54–0.72]) and SOFA (0.61 [0.53–0.68]). Although cardiovascular (NT-proBNP, bio-ADM) and kidney (pNGAL) biomarkers had comparable short-term discriminatory value, CCI performed better for long-term outcomes across different admission diagnoses.

**Conclusion:** In this analysis, pre-existing chronic conditions were primary drivers of short- and long-term survival after ICU discharge, exceeding prognostic value of acute illness severity at admission.

**Clinical trial registration:** French and European Outcome Registry in Intensive Care Units (FROG-ICU) study: [ClinicalTrials.gov](http://ClinicalTrials.gov) NCT01367093. Registered 3 June 2011.

# 1. Introduction

Intensive Care Unit (ICU) stay is associated with a high mortality, both during the ICU stay and after discharge. It is increasingly recognized that patients discharged alive from the ICU often face a challenging post-ICU trajectory. While the literature on ICU survivors has largely focused on post-intensive care syndrome (PICS)—which affects approximately one in two patients at three months [1,2]—its relationship with long-term post-ICU mortality remains poorly understood [3].

The FROG-ICU (French and European Outcome Registry in Intensive Care Units) study, a large multicenter European cohort, reported an ICU mortality of 22 %, and a post-discharge one-year mortality of 21 % [4]. Other studies have shown even higher post-ICU mortality rates, with 27 % at four weeks and up to 46 % at one year among patients with cardiogenic shock [5] and 27 % in those with septic shock [6]. Despite these concerning figures, the prognostic determinants of long-term post-ICU survival remain insufficiently defined.

Severity scores at ICU admission, such as Sequential Organ Failure Assessment (SOFA) and the Simplified Acute Physiology Score II (SAPS-II), have traditionally been used to estimate prognosis. However, these tools have shown limited accuracy in predicting long-term outcomes, as they often fail to account for key clinical variables at discharge. Recent evidence suggests that biomarkers may provide additional prognostic value [7–9]. Moreover, we previously identified other potential predictors of one-year post-ICU mortality, including age, comorbidities, red blood cell transfusion, ICU length of stay, and other abnormalities in routine clinical parameters (e.g., low systolic blood pressure, hypothermia, low total protein, thrombocytopenia, and leukopenia) [4].

The Charlson Comorbidity Index (CCI) is a simple, rapid, and widely used tool for assessing major comorbidities. It has been employed to predict mortality in various settings [10–13], but its potential correlation with standard ICU severity scores and its prognostic value for long-term outcomes are not well-established [14]. Further research is needed to fill this gap and improve the accuracy of post-ICU outcome prediction.

This study assessed whether chronic disease burden (CCI) or acute illness severity at ICU admission (SOFA, SAPS-II) better predicts mid- and long-term mortality among patients discharged alive from the ICU. We also examined whether select biological biomarkers improve post-ICU risk stratification beyond these clinical measures.

# 2. Methods

## 2.1. Study design and patients

Our study is based on data from the FROG-ICU (French and European Outcome Registry in Intensive Care Units) study (NCT01367093) [4]. FROG-ICU was a prospective, observational, multicenter (21 French centers and 1 Belgian center) cohort study, designed to assess one-year mortality following ICU discharge and to identify associated risk factors. The study design and primary results have been previously published [15].

This study was conducted across the 21 French ICUs, including medical, surgical, and mixed ICUs. A total of 2087 consecutive eligible patients were enrolled between August 2011 and June 2013. Patient recruitment per center reflected differences in ICU size. Eligible patients

met at least one of the following inclusion criteria: invasive mechanical ventilation for ≥24 h and/or administration of vasoactive agents for ≥24 h. Exclusion criteria included: less than 18 years old, severe head injury (initial Glasgow Coma Scale <8), brain death or a persistent vegetative state, pregnancy or breastfeeding, organ transplantation within the previous 12 months, a prognosis of imminent death or hospital non-discharge, and lack of social security coverage [4]. The study was conducted in accordance with Good Clinical Practice (GCP), the Declaration of Helsinki, and approved by the ethical committee or Institutional Review Board (IRB) [16]. Informed consent was obtained from all patients or their relatives.

Among the 2087 enrolled patients, 1548 were discharged alive from the ICU. Of these, 1400 had complete data on comorbidity scores and biomarker measurements and were therefore included in the present secondary analysis.

## 2.2. Data collection and biological samples

In the FROG-ICU study, for each patient included, an electronic case report form was completed that documented relevant data throughout the ICU stay. The following patient data were collected: demographics, past medical history, ICU admission diagnosis, hemodynamic and respiratory parameters, and severity of disease classification scores. Blood samples were collected in ethylenediaminetetraacetic acid [EDTA] at patient inclusion and discharge. The following biomarkers were measured in plasma: hemoglobin, lactate, creatinine (with estimated glomerular filtration rate [eGFR] calculated), high-sensitivity troponin T (hs-cTnT), N-Terminal pro-Brain Natriuretic Peptide (NT-proBNP), bioactive adrenomedullin (bio-ADM), proenkephalin A 119–159 (penKid), plasma Neutrophil Gelatinase Associated Lipocalin (pNGAL), and urine NGAL (uNGAL).

## 2.3. Endpoints

The primary objective was to determine whether pre-existing comorbidities (CCI) and selected biologic biomarkers measured at ICU discharge improve prediction of short- and long-term post-ICU mortality among ICU survivors, compared with admission severity scores (SOFA, SAPS-II). Secondary objectives included: (1) subgroup analyses among patients admitted with acute respiratory failure (ARF), cardiogenic shock (CS), septic shock (SS), or neurologic conditions; (2) comparison of discharge plasma biomarkers—cardiovascular (NT-proBNP, bio-ADM) and renal (plasma NGAL [pNGAL], estimated GFR [eGFR])—in the overall survivor cohort and within these subgroups; and (3) additional analyses in the overall survivor cohort including high-sensitivity cardiac troponin T (hs-cTnT), proenkephalin (penKid), and urinary NGAL (uNGAL). An exploratory objective assessed the predictive value of the same comorbidity scores and biomarkers in the full ICU population at study inclusion.

## 2.4. Statistical methods

Results are presented as count and percentage for categorical variable or median and first to third quartile range for continuous variables.

The one-year survival after ICU discharge was defined as the delay
between ICU discharge and death from any cause. Survival was censored either at one year or at the last follow-up if below one-year. Survival was estimated using the Kaplan-Meier method. The impact of studied risk factors was assessed using Cox proportional hazard models. The discriminatory ability of each risk factor was evaluated using time-dependent area under the receiver operating characteristic curve (AUC) with their 95 % confidence intervals (CI) [17,18]. Estimations of cumulative/dynamic time-dependent ROC curve were plotted against time. In this framework, the definition of cases and controls varies with time. At each time T, a case is defined as a subject with a time to death below or equal to T and a control is defined as a subject with a follow-up above T. Subgroup analyses were performed similarly as well as the analyses based on biomarkers. All analyses were performed with R 4.0.1 [19]. The main packages used were 'survival' and 'timeROC'.

# 3. Results

## 3.1. Patient characteristics

Among the 1548 patients discharged alive from the ICU, 1400 had complete data on clinical scores and biomarkers measured at admission and were included in our analysis (Tables 1 and B). The consort diagram is presented in **suppl. Fig. 1**. The remaining 148 patients with missing data were excluded and are described in **suppl. Table 1**. The final analytical cohort had a median age of 61 years [IQR 48; 72] and were predominantly male (n = 885, 63 %). Main causes for ICU admission were ARF (19 %), SS (24 %), CS (7 %), and neurologic conditions (16 %) (Table 1A). At the time of ICU discharge, most clinical parameters including vital signs and laboratory values had normalized (Table 1B).

## 3.2. Predictive factors of post-ICU outcome

Of the 1400 studied ICU survivors, 267 (19 %) died within one year following ICU discharge, including 92 patients (7 %) who died during the hospital stay (Table 1A). Fig. 1 illustrates the time-dependent AUC curves for predicting one-year mortality using the CCI, SAPS-II, and SOFA scores. CCI was the best predictor of mortality very early following ICU discharge, with a better performance compared to the ICU severity scores throughout the follow-up period (Graphical abstract). As detailed in **suppl. Table 2**, CCI had an AUC of 0.70 (CI 95 %: 0.64; 0.77) at day 7, outperforming SAPS-II (AUC: 0.63 [0.54; 0.72]) and SOFA (0.61 [0.53; 0.68]). The AUC of CCI increased steadily until the third week (0.74 [0.69; 0.78]) and remained higher than that of either SAPS-II or SOFA throughout the one-year period, with no overlap in confidence interval.

Suppl. Fig. 2A shows that CCI, within the first few weeks after ICU discharge, demonstrated greater predictive value for post-ICU mortality than severity scores across each of the four main admission categories: ARF, SS, CS and neurologic conditions.

When exploring the predictive value of both routinely available and novel biomarkers on post-ICU mortality, plasma cardiovascular biomarkers measured at discharge, such as NT-proBNP or bio-ADM, as well as plasma kidney biomarkers like pNGAL, showed predictive performance comparable to the CCI during the first six months after ICU discharge, as illustrated in Fig. 2 and detailed in **Supplementary Fig. 3**. Beyond six months, however, CCI remained the most accurate predictor of mortality. In contrast, eGFR, PenKid, hs-cTnT, and uNGAL consistently demonstrated poor prognostic value throughout the one-year follow-up period.

Predictive values of biomarkers for post-ICU mortality differed with the causes of ICU admission (Suppl. Fig. 2B). In ARF, pNGAL, bio-ADM and NT-proBNP showed predictive performance comparable to that of CCI during the first six months after ICU discharge, but not thereafter, when CCI continued to show higher AUC values, reflecting its superior prognostic value. In SS, bio-ADM was the best predictive biomarker, with a performance similar to CCI in the first six months but declining

<table><caption>**Table 1**

Characteristics of ICU survivors at discharge.</caption><caption>A Characteristics of ICU survivors at discharge</caption><thead><tr><th>Variable</th><th>n</th><th>Statistics</th></tr></thead><tbody><tr><td>Age</td><td>1400</td><td>61 [48;72]</td></tr><tr><td>Male</td><td>885</td><td>63 %</td></tr><tr><td>Scores</td><td></td><td></td></tr><tr><td>Charlson Comorbidity Index</td><td>1400</td><td>3 [1;4]</td></tr><tr><td>SAPS-II</td><td>1400</td><td>46 [34;59]</td></tr><tr><td>SOFA</td><td>1400</td><td>6 [4;9]</td></tr><tr><td>Comorbidities</td><td></td><td></td></tr><tr><td>Chronic heart failure</td><td>98</td><td>7 %</td></tr><tr><td>Hypertension</td><td>559</td><td>40 %</td></tr><tr><td>Diabetes mellitus</td><td>231</td><td>16 %</td></tr><tr><td>Obesity</td><td>139</td><td>10 %</td></tr><tr><td>Chronic Kidney Disease</td><td>135</td><td>10 %</td></tr><tr><td>Cause of ICU admission</td><td></td><td></td></tr><tr><td>Cardiogenic shock</td><td>102</td><td>7 %</td></tr><tr><td>Septic shock</td><td>337</td><td>24 %</td></tr><tr><td>Acute Respiratory Failure</td><td>264</td><td>19 %</td></tr><tr><td>Neurologic</td><td>229</td><td>16 %</td></tr><tr><td>Other</td><td>468</td><td>34 %</td></tr><tr><td>Organ support</td><td></td><td></td></tr><tr><td>Inotropes/ vasopressors</td><td>961</td><td>69 %</td></tr><tr><td>RRT</td><td>260</td><td>19 %</td></tr><tr><td>LOS and death</td><td></td><td></td></tr><tr><td>LOS in ICU</td><td>1400</td><td>12 [7;21]</td></tr><tr><td>Death during hospitalization</td><td>92</td><td>7 %</td></tr><tr><td>Death at 1-year</td><td>267</td><td>19 %</td></tr></tbody></table>

<table><caption>B Clinical and biological characteristics of ICU survivors at inclusion and at discharge</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">At inclusion</th><th colspan="2">At discharge</th></tr><tr><th>n</th><th>Statistics</th><th>n</th><th>Statistics</th></tr></thead><tbody><tr><td>Clinical parameters</td><td></td><td></td><td></td><td></td></tr><tr><td>SOFA</td><td>1400</td><td>6 [4;9]</td><td>1278</td><td>0 [0;2]</td></tr><tr><td>Glasgow Coma Scale</td><td>983</td><td>13 [3;15]</td><td>1171</td><td>15 [15;15]</td></tr><tr><td>Systolic Blood Pressure (mmHg)</td><td>1380</td><td>123 [109;139]</td><td>1218</td><td>124 [111;139]</td></tr><tr><td>Diastolic Blood Pressure (mmHg)</td><td>1322</td><td>62 [55;72]</td><td>1159</td><td>68 [59;77]</td></tr><tr><td>Mean Blood Pressure (mmHg)</td><td>1322</td><td>82.7 [73.7;93]</td><td>1159</td><td>87.3 [77.7;96.3]</td></tr><tr><td>Heart Rate (bpm)</td><td>1358</td><td>90 [77;104]</td><td>1211</td><td>89 [78;100]</td></tr><tr><td>Biology</td><td></td><td></td><td></td><td></td></tr><tr><td>Hemoglobin (g/dL)</td><td>1341</td><td>10 [9;11.5]</td><td>1217</td><td>10 [9;11.2]</td></tr><tr><td>Lactate (mmol/L)</td><td>1088</td><td>1.2 [0.9;1.7]</td><td>569</td><td>1 [0.7;1.3]</td></tr><tr><td>Troponin (pg/mL)</td><td>1400</td><td>36.3 [14.3;111.1]</td><td>1115</td><td>30 [13.8;72.8]</td></tr><tr><td>NT-proBNP (pg/mL)</td><td>1400</td><td>859 [223.4;3287]</td><td>1115</td><td>543.1 [146.4;2086]</td></tr><tr><td>Bio-ADM (pg/mL)</td><td>1400</td><td>54.6 [29.3;107.9]</td><td>1114</td><td>32.9 [19.9;59.1]</td></tr><tr><td>Creatinine (µmol/L)</td><td>1389</td><td>77 [57;131]</td><td>1270</td><td>66 [51;95]</td></tr><tr><td>eGFR</td><td>1400</td><td>86 [46.1;125]</td><td>1348</td><td>104.5 [67.6;141.3]</td></tr><tr><td>penKid (pmol/mL)</td><td>1400</td><td>53.3 [34.2;96.1]</td><td>1114</td><td>58.9 [42.1;94.6]</td></tr><tr><td>Plasma NGAL (ng/mL)</td><td>1377</td><td>169 [85;398]</td><td>1102</td><td>123 [74;218]</td></tr><tr><td>Urinary NGAL (ng/mL)</td><td>1328</td><td>97.5 [31.9;486.3]</td><td>973</td><td>52.8 [23.8;170.1]</td></tr></tbody></table>

Data are presented as percentages or as median with interquartile range (IQR), as appropriate.

Abbreviations: ICU Intensive Care Unit; LOS Length of stay; RRT Renal Replacement Therapy; SAPS-II Simplified Acute Physiology Score II; SOFA Sequential Organ Failure Assessment.

Data are presented as percentages or as median with interquartile range (IQR), as appropriate.

Abbreviations: bio-ADM bioactive adrenomedullin; eGFR estimated Glomerular Filtration Rate; NGAL Neutrophil Gelatinase Associated Lipocalin; NT-proBNP N-Terminal Pro-Brain Natriuretic Peptide; penKid Proenkephalin A 119–159.
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Charlson Comorbidity Index (CCI) better predicts one-year post-ICU survival at discharge. Time-dependent AUC curves for one-year mortality in ICU survivors at discharge (N = 1400), comparing the Charlson Comorbidity Index (CCI) with standard severity scores assessed at ICU admission: SAPS-II (Simplified Acute Physiology Score II) and SOFA (Sequential Organ Failure Assessment). Number of patients at risk are listed below as well as AUC for each specific score.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Biomarkers at discharge predict mid-term post-ICU survival. Time-dependent AUC curves for one-year mortality in ICU survivors at discharge (n = 1169), comparing the Charlson Comorbidity Index (CCI) with cardiovascular and renal biomarkers: NT-proBNP (N-Terminal Pro-brain natriuretic peptide), bio-ADM (bio-adrenomedullin), pNGAL (plasma Neutrophil Gelatinase-Associated Lipocalin) and eGFR (estimated Glomerular Filtration Rate). Number of patients at risk are listed below the curves, as well as AUC for each specific score.</figcaption></figure>

thereafter when CCI became the dominant predictor. In CS, both cardiovascular and renal plasma biomarkers demonstrated predictive values that were comparable to—and in some instances exceeded, like for NT-proBNP—those of CCI throughout the entire one-year follow-up. In the group with neurologic conditions, bio-ADM had predictive value similar to CCI over the whole post-ICU year.

**Fig. 3** displays the association between one-year survival and three categories of CCI, stratified by SAPS-II (Fig. 3A) or SOFA (Fig. 3B) scores. It shows that the prognostic impact of CCI appeared to be more pronounced among patients with lower severity at ICU admission.

### 3.3. Predictive factors of one-year outcome starting at ICU admission

Baseline characteristics of all ICU patients with available SOFA scores at admission (n = 1778) are described in **suppl Table 3**. As shown in **Suppl. figs. 4 A & B**, SAPS-II, SOFA, NT-proBNP, bio-ADM or pNGAL—measured at ICU admission—were strong predictors of early mortality. However, later and starting around three months after ICU admission, the CCI progressively emerged as the most prominent predictor of mortality throughout the remainder of the one-year follow-up.

Analyses by main cause of ICU admission showed distinct prognostic patterns (**suppl. Fig. 5 A**): in ARF and neurologic conditions, CCI outperformed SAPS-II and SOFA throughout the post-ICU year in predicting mortality and showed superior prognostic value compared with admission biomarkers from three months onward. In SS, SAPS-II, SOFA and

CCI had similar good predictive value, while CCI maintained an advantage over biomarkers after three months. In CS, both CCI and SOFA performed better than SAPS-II; however, in contrast to other conditions, several biomarkers demonstrated predictive performance comparable to CCI, with NT-proBNP exhibiting a slight but consistent superiority. The only exception was eGFR, which demonstrated lower prognostic value (**suppl. Fig. 5B**).

### 4. Discussion

In this *post-hoc* analysis of ICU survivors from the FROG-ICU cohort, we found that the burden of chronic comorbidities—captured by the CCI—was a stronger predictor of post-ICU mortality than acute illness severity scores assessed at admission, such as SOFA and SAPS-II. This relationship held true across a range of conditions, including ARF, SS, CS, and neurologic disease. While several cardiovascular and kidney biomarkers showed comparable short-term prognostic value, their predictive accuracy declined over time, whereas the CCI maintained its superiority throughout the one-year follow-up. Notably, the prognostic impact of chronic disease burden was particularly pronounced among patients with lower severity of acute illness at ICU admission, suggesting that—even when acute derangement appear limited—long-term outcomes can still be substantially affected by preexisting comorbidities. These findings suggest that long-term outcomes in ICU survivors may be driven more by underlying comorbidity profile than by the acuity of the
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Probability of one-year survival according to SOFA and SAPS-II in the different Charlson Comorbidity Index (CCI) categories.</figcaption></figure>

A. Survival probability at one year according to SAPS-II at ICU admission in all-comer ICU patients (n = 1988), stratified by CCI category.

B. Survival probability at one year according to SOFA at ICU admission in all-comer ICU patients (n = 1778), stratified by CCI category.

CCI was divided into three groups: 0–1 (patients without comorbidity), 2–3 (low-intermediate comorbidities), and > 3 (high comorbidities; above the cohort median).

initial critical illness.

PICS was originally described in survivors of acute respiratory distress syndrome (ARDS) and SS and is now widely recognized as a common consequence of critical illness, affecting almost all ICU survivors to varying degrees [20–23]. It encompasses cognitive, psychologic, physical, and functional impairments that often persist beyond ICU discharge and often does not resolve after one year [24,25]. In addition to these disabilities, one fifth of ICU survivors die within the year following discharge [4,5,26–28]. Recent trials targeting early physical and psychological rehabilitation have shown no increase in adverse events or mortality, but have not consistently improved benefit on long-term survival, suggesting that mechanisms underlying post-ICU mortality extend beyond functional recovery alone [29,30].

In this context, our findings suggest that chronic health status may exert a greater influence on long-term prognosis than the initial severity of acute illness — in terms of mortality, as shown in our study, and of quality of life, as previously reported [31]. This observation is in line with the study by Iwashyna et al., which showed that physiologic rearrangements (e.g., ICU scores) strongly predicted early mortality but lost predictive value within two weeks, after which patient characteristics such as age and comorbidities became more relevant [32]. However, their study only analyzed 28-day (i.e. short-term) mortality, while our analysis extends the observation to one-year following ICU discharge in a large, unselected cohort. Notably, the prognostic impact of chronic disease burden was independent of the cause of ICU admission, and particularly prominent in patients with high CCI scores.

In addition, we found that specific biomarkers measured at ICU discharge—particularly NT-proBNP, bio-ADM, and pNGAL—had predictive performances comparable to CCI, especially during the first six months post-discharge. These biomarkers reflect underlying cardiovascular, endothelial, and renal dysfunction, which may not be clinically evident but nonetheless portend poor outcomes. Their prognostic relevance was observed across all major categories of ICU admission, suggesting a broad applicability. In patients with CS, NT-proBNP showed particularly strong predictive value, while bio-ADM was most informative in septic shock.

Accordingly, our data suggest that, despite meeting criteria for ICU discharge, many ICU survivors may carry unrecognized organ damage or subclinical dysfunction that increases their vulnerability, as observed in recent publications [33,34]. The elevated levels of biomarkers at

discharge may reflect both pre-existing disease burden and ICU-acquired organ injury, though the precise mechanisms remain unclear. What emerges, however, is the likely limitation of standard ICU scores to capture these chronic or cumulative insults.

The comparison between acute severity scores and chronic disease burden was designed to explore how these distinct but interrelated domains contribute to the prognosis. Although SOFA and SAPS II are primarily calibrated for short-term mortality, they capture the extent of organ dysfunction and the physiologic reserve at ICU admission, factors that could plausibly influence outcomes well beyond the acute phase. Our results indicate that chronic comorbidity, as assessed by the CCI, exerts a more sustained prognostic effect after ICU discharge, suggesting that baseline vulnerability, probably aggravate, continues to influence post-discharge phase despite apparent resolution of the acute episode.

For the assessment of the comorbidity burden, we selected the CCI for its simplicity, broad recognition, and integration into many clinical workflows, as it is automatically calculated in some electronic health record systems. Despite its limitations and its inability to capture the full complexity of ICU patients, the CCI remains a valid and reliable index in this population, with proven incremental value for mortality prediction beyond standard clinical assessment [35].

Our results suggest that simple tools such as CCI may help identify ICU survivors at increased long-term risk and could complement more refined approaches to post-discharge risk stratification. In addition, integrating CCI with biomarker data could help clinicians identify high-risk patients and implement more tailored post-ICU care strategies, both during hospitalization and in outpatient follow-up. Future studies should test whether interventions guided by comorbidity burden and biomarker profiles can improve long-term post-discharge outcomes. Moreover, it will be important to test the prognostic value of newer and more precise comorbidity scores, potentially supported by artificial intelligence, which could facilitate their registration and computation.

Finally, our findings have implications for the design of future ICU trials. First, comorbidity assessment should be integrated in post-ICU prognostic models. Second, most interventional studies in critical care still rely on short-term endpoints such as 28-day mortality. As both our study and prior evidence suggest [32], acute-phase interventions may influence early outcomes, while long-term survival, namely longer than 2–4 weeks after the acute event, may depend more heavily on chronic disease trajectories and recovery from ICU-acquired organ damage.
Distinguishing between these mechanisms could help refine endpoints and optimize treatment strategies across the continuum of critical illness and recovery.

## 4.1. Limitations

This study has several limitations. First, as a *post-hoc* analysis, it is subject to inherent biases related to hypothesis generation and model overfitting, despite the large, well-characterized cohort and near-complete follow-up. Moreover, AUROC's represent only one of several approaches to evaluate predictive performance of individual variables and were chosen here as an initial step for this analysis. Incorporating calibration metrics in future multivariate models, together with decision curve analysis, would provide a more comprehensive assessment of model performance and clinical utility. Second, we focused solely on all-cause mortality and did not assess other important outcomes such as functional status, quality of life, or rehospitalization. Third, while several biomarkers showed prognostic value, their interpretation remains complex due to overlapping pathophysiology and unclear thresholds for clinical significance. The variability in biomarker performance across subgroups underscores the need for better phenotyping tools, potentially through machine learning or AI-based approaches. Finally, as an observational study, residual confounding cannot be excluded, and causal inferences should be made with caution. Future interventional studies are needed to test whether biomarker-guided or comorbidity-informed strategies can improve outcomes in ICU survivors.

## CRediT authorship contribution statement

**Alice Blet:** Writing – original draft, Methodology, Formal analysis, Conceptualization.
**Anouk Walter-Petrich:** Formal analysis.
**Ményssa Chérifa:** Writing – review & editing, Formal analysis.
**Jolie Bruno:** Writing – review & editing.
**Alexy Tran Dinh:** Writing – review & editing.
**Elie Azoulay:** Writing – review & editing.
**Benjamin Kimmouterman:** Writing – review & editing.
**Gad Cotter:** Writing – review & editing.
**Beth Davison:** Writing – review & editing.
**Benjamin Deniau:** Writing – review & editing.
**Etienne Gayat:** Writing – review & editing, Methodology, Data curation, Conceptualization.
**Bertrand Guidet:** Writing – review & editing.
**Anatole Harrois:** Writing – review & editing.
**Antoine Kimmoun:** Writing – review & editing.
**Marc Leone:** Writing – review & editing.
**Xavier Wittebole:** Writing – review & editing.
**Andrew P. Ambrosy:** Writing – review & editing.
**Alexandre Mebazaa:** Writing – original draft, Supervision, Resources, Methodology, Formal analysis, Data curation.
**Matthieu Resche-Rigon:** Writing – review & editing, Formal analysis, Data curation.

## Consent for publication

All participants (or their legal representatives) consented to the use of their data for research purposes, including scientific publication, as part of the original informed consent process.

## Ethical approval and consent to participate

This study is a secondary analysis of previously collected data, conducted in France and in Belgium in accordance with Good Clinical Practice and Declaration of Helsinki 2002. The study was approved by the Comité de Protection des Personnes - Ile de France IV (IRB n°00003835) and the Commission d'éthique biomédicale hospitalo-facultaire de l'hôpital de Louvain (IRB n°B403201213352). Informed consent was obtained from all patients or their legal representatives.

## Funding

No funding was received for this work.

## Declaration of competing interest

Benjamin G Chousterman served as a member of an advisory board for Roche Diagnostic. Antoine Kimmoun had lecture fees for Aspen et Aguettant. Marc Leone lecturers for MSD, Aspen and Consultants for Gilead, Amomed. The others authors do not have any conflicts of interest to declare.

## Acknowledgements

The authors are particularly grateful to Marie-Céline Fournier, who coordinated organizational aspects of the study. The authors also thank the Centre de Recherche Clinique (CRC) of Lariboisière University Hospital for support.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jcrc.2025.155401.

## Data availability

All data supporting the findings of this study are available within the article and its Supplementary Information files. No third-party material requiring permission has been used.

## References

[1] Ohbe H, Satoh K, Totoki T, Tanikawa A, Shirasaki K, Kuribayashi Y, et al. Definitions, epidemiology, and outcomes of persistent/chronic critical illness: a scoping review for translation to clinical practice. Crit Care 2024;28(1). https://doi.org/10.1186/s13054-024-05215-4.

[2] Kang J, Lee MH. Incidence rate and risk factors for post-intensive care syndrome subtypes among critical care survivors three months after discharge: a prospective cohort study. Intensive Crit Care Nurs 2024;81:103605. https://doi.org/10.1016/j.iccn.2023.103605.

[3] Inoue S, Hatakeyama J, Kondo Y, Hifumi T, Sakuramoto H, Kawasaki T, et al. Post-intensive care syndrome: its pathophysiology, prevention, and future directions. Acute Med Surg 2019;6(3):233-46. https://doi.org/10.1002/ams2.415.

[4] Gayat E, Cariou A, Dey N, Vieillard-Barin A, Jaber S, Damoisel C, et al. Determinants of long-term outcome in ICU survivors: results from the FROG-ICU study. Crit Care Lond Engl 2018;22(1):8. https://doi.org/10.1186/s13054-017-1922-8.

[5] Zannad F, Mebazaa A, Juillière Y, Cohen-Solal A, Guize L, Alla F, et al. Clinical profile, contemporary management and one-year mortality in patients with severe acute heart failure syndromes: the EFICA study*. Eur J Heart Fail 2006;8(7):697-705. https://doi.org/10.1016/j.ejheart.2006.01.001.

[6] Higgins AM, Peake SL, Bellomo R, Jamie Cooper D, Delaney A, Harris AH, et al. Quality of life and 1-year survival in patients with early septic shock: long-term follow-up of the Australasian resuscitation in Sepsis evaluation trial. Crit Care Med 2019;47(6):765-73. https://doi.org/10.1097/ccm.00000000000003762.

[7] Davison BA, Edwards C, Cotter G, Kimmoun A, Gayat E, Latosinska, et al. Plasma and urinary biomarkers improve prediction of mortality through 1 year in intensive care patients: an analysis from FROG-ICU. J Clin Med 2023;12(9):3311. https://doi.org/10.3390/jcm12093311.

[8] Boutin L, Latosinska A, Mischak H, Deniau B, Asakage A, Legrand M, et al. Subclinical and clinical acute kidney injury share similar urinary peptide signatures and prognosis. Intensive Care Med 2023;49(10):1191-202. https://doi.org/10.1007/s00134-023-07198-2.

[9] Nkuipou-Kenfack E, Latosinska A, Yang WY, Fournier MC, Blet A, Mujaj B, et al. The French and European outcome registry in intensive care unit investigators. A novel urinary biomarker predicts 1-year mortality after discharge from intensive care. Crit Care 2020;24(1). https://doi.org/10.1186/s13054-019-2686-0.

[10] Zhang N, Lin Q, Jiang H, Zhu H. Age-adjusted Charlson comorbidity index as effective predictor for in-hospital mortality of patients with cardiac arrest: a retrospective study. BMC Emerg Med 2023;23(1). https://doi.org/10.1186/s12873-022-00769-4.

[11] Baris O, Oksuzler Kizilbay G, Holat CM, Uzturk ME, Canikoglu M, Durmaz A, et al. The efficacy of the Charlson comorbidity index and its age-adjusted version in forecasting mortality and postoperative outcomes following isolated coronary artery bypass grafting. J Clin Med 2025;14(2):395. https://doi.org/10.3390/jcm14020395.

[12] Jouffroy R, Parfait PA, Gilbert B, Tourtier JP, Bloch-Laine E, Ecollan P, et al. Relationship between prehospital modified Charlson comorbidity index and septic shock 30-day mortality. Am J Emerg Med 2022;60:128-33. https://doi.org/10.1016/j.ajem.2022.08.003.
[13] Zhang T, Chen R, Wen D, Wang X, Ma L. The prognostic value of the Charlson comorbidity index in aged patients with intracerebral hemorrhage. BMC Neurol 2022;22(1). https://doi.org/10.1186/s12883-022-02980-z.

[14] Christensen Johansen M, Christiansen C, Jensen Lemeshow. Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol 2011;203. https://doi.org/10.2147/clep.s20247. Published online June 2011.

[15] Mebazaa A, Casadio MC, Azoulay E, Guidet B, Jaber S, Levy B, et al. Post-ICU discharge and outcome: rationale and methods of the French and euRopean outcome reGistry in intensive care units (FROG-ICU) observational study. BMC Anesthesiol 2015;15:143. https://doi.org/10.1186/s12871-015-0129-2.

[16] Toulouse E, Lafont B, Granier S, Mcgurk G, Bazin JE. French legal approach to patient consent in clinical research. Anaesth Crit Care Pain Med 2020;39(6):883–5. https://doi.org/10.1016/j.accpm.2020.10.012.

[17] Hung H, Chiang CT. Estimation methods for time-dependent AUC models with survival data. Can J Stat 2010;38(1):8–26. https://doi.org/10.1002/cjs.10046.

[18] Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013;32(30):5381–97. https://doi.org/10.1002/sim.5958.

[19] Team RC. R: A Language and Environment for Statistical Computing. Published online 2013, https://www.scirp.org/reference/ReferencesPapers?Reference ID=1787696; 2013.

[20] Herridge MS, Cheung AM, Tansey CM, Matte-Martyn A, Diaz-Granados N, Al-Saidi F, et al. One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med 2003;348(8):683–93. https://doi.org/10.1056/NEJMoa022450.

[21] Rengel KF, Hayhurst CJ, Pandharipande PP, Hughes CG. Long-term cognitive and functional impairments after critical illness. Anesth Analg 2019;128(4):772–80. https://doi.org/10.1213/ANE.0000000000004066.

[22] Herridge MS, Moss M, Hough CL, Hopkins RO, Rice TW, Bienvenu OJ, et al. Recovery and outcomes after the acute respiratory distress syndrome (ARDS) in patients and their family caregivers. Intensive Care Med 2016;42(5):725–38. https://doi.org/10.1007/s00134-016-4321-8.

[23] Herridge MS, Azoulay É. Outcomes after critical illness. Hardin CC, ed. N Engl J Med 2023;388(10):913–24. https://doi.org/10.1056/nejmra2104669.

[24] Bastian K, Hollinger A, Mebazaa A, Azoulay E, Féliot E, Chevreul K, et al. Association of social deprivation with 1-year outcome of ICU survivors: results from the FROG-ICU study. Intensive Care Med 2018;44(12):2025–37. https://doi.org/10.1007/s00134-018-5412-5.

[25] Honarmand K, Lalli RS, Priestap F, Chen Jennifer L, McIntyre CW, Owen AM, et al. Natural history of cognitive impairment in critical illness survivors. A systematic review. Am J Respir Crit Care Med 2020;202(2):193–201. https://doi.org/10.1164/rccm.201904-0816CI.

[26] Harjola VP, Lassus J, Sionis A, Kober L, Tarvasmäki T, Spinar J, et al. Clinical picture and risk prediction of short-term mortality in cardiogenic shock. Eur J Heart Fail 2015;17(5):501–9. https://doi.org/10.1002/ejhf.260.

[27] Mebazaa A, Geven C, Hollinger A, Wittebole X, Choustermann BG, Blet A, et al. Circulating adrenomedullin estimates survival and reversibility of organ failure in sepsis: the prospective observational multinational Adrenomedullin and outcome in Sepsis and septic Shock-1 (AdrenOSS-1) study. Crit Care 2018;22(1):354. https://doi.org/10.1186/s13054-018-2243-2.

[28] Lokhandwala S, McCague N, Chahin A, Escobar B, Feng M, Ghassemi MM, et al. One-year mortality after recovery from critical illness: a retrospective cohort study. PLoS One 2018;13(5):e0197226. https://doi.org/10.1371/journal.pone.0197226.

[29] Mehlhorn J, Freytag A, Schmidt K, Brunkhorst FM, Graf J, Troitzsch U, et al. Rehabilitation interventions for postintensive care syndrome: a systematic review. Crit Care Med 2014;42(5):1263–71. https://doi.org/10.1097/CCM.0000000000000148.

[30] Paton M, Chan S, Serpa Neto A, Tipping CJ, Stratton A, Lane R, et al. Association of active mobilisation variables with adverse events and mortality in patients requiring mechanical ventilation in the intensive care unit: a systematic review and meta-analysis. Lancet Respir Med 2024;12(5):386–98. https://doi.org/10.1016/S2213-2600(24)00011-0.

[31] Orwelius L, Wilhelms S, Sjöberg F. Is comorbidity alone responsible for changes in health-related quality of life among critical care survivors? A purpose-specific review. Crit Care 2024;28(1). https://doi.org/10.1186/s13054-024-04997-x.

[32] Iwashyna TJ, Hodgson CL, Pilcher D, Bailey M, van Lint A, Chavan S, et al. Timing of onset and burden of persistent critical illness in Australia and New Zealand: a retrospective, population-based, observational study. Lancet Respir Med 2016;4(7):566–73. https://doi.org/10.1016/S2213-2600(16)30098-4.

[33] Dépret F, Hollinger A, Cariou A, Deye N, Vieillard-Baron A, Fournier MC, et al. Incidence and outcome of subclinical acute kidney injury using penKid in critically ill patients. Am J Respir Crit Care Med 2020;202(6):822–9. https://doi.org/10.1164/rccm.201910-19500C.

[34] Ostermann M, Zarbock A, Goldstein S, Kashani K, Macedo E, Murugan R, et al. Recommendations on acute kidney injury biomarkers from the acute disease quality initiative consensus conference: a consensus statement. JAMA Netw Open 2020;3(10):e2019209. https://doi.org/10.1001/jamanetworkopen.2020.19209.

[35] Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom 2022;91(1):8–35. https://doi.org/10.1159/000521288.