Implementation of a Clinical Screening and Response System for the Early Detection of Cardiac Complications After Noncardiac Surgery: Feasibility and Medicoeconomic Impact
Conditions: Myocardial Injury, Myocardial Infarction, Perioperative Complication
Sponsor: University Hospital, Basel, Switzerland

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Implementation of a clinical screening and 
response system for the early detection of 
cardiac complications after noncardiac 
surgery: feasibility and medicoeconomic 
impact (Implement-PMI) 
 
 
Type of Research Project: 
Research project in which health-related personal data is 
collected 
Risk Categorisation: 
A (clinically recorded routine data, prospective follow up) 
Project Identifier: 
Implement-PMI 
Project Leaders: 
Dr. Christian Puelacher, MD-PhD, University Hospital 
Basel, Petersgraben 4, 4031 Basel, 
christian.puelacher@usb.ch, +41 76 403 4450 
Prof. Christian Müller, University Hospital Basel, 
Cardiology, Petersgraben 4, 4031 Basel; 
christian.mueller@usb.ch, +41 61 328 65 49 
Health condition / problem 
Perioperative myocardial infarction/injury 
Major adverse cardiac events after major non-cardiac 
surgery 
Project Duration 
01.06.2021 – 01.12.2025 
Project Plan Version and Date: 
Version 1.2, 2022-05-023 
 
 

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Signature Pages 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and the methodologist (Dr. Christian Puelacher MD-PhD, Prof. Christian 
Müller MD) have approved the research plan Version 1.2, 2022-05-023, and confirm hereby to 
conduct the project according to the plan, the current version of the World Medical Association 
Declaration of Helsinki, GCP, the STROBE Guidelines, and the local legally applicable 
requirements. 
 
 
Co-Project Leader and project methodologist: 
Dr. Christian Puelacher, MD-PhD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 
Co-Project Leader: 
Prof. Christian Müller, MD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 
 

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Signature Page 2: Cantonal Hospital Lucerne 
 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and local principal investigator (PI) have approved the research plan 
Version 1.2, 2022-05-023, and confirm hereby to conduct the project according to the plan, the 
current version of the World Medical Association Declaration of Helsinki, GCP, the STROBE 
Guidelines, and the local legally applicable requirements. 
 
 
Local PI: 
Dr. Matthias Bossard, MD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 

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Signature Page 3: Cantonal Hospital Olten 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and local principal investigator (PI) have approved the research plan 
Version 1.2, 2022-05-023, and confirm hereby to conduct the project according to the plan, the 
current version of the World Medical Association Declaration of Helsinki, GCP, the STROBE 
Guidelines, and the local legally applicable requirements. 
 
 
Local PI: 
PD Dr. Nisha Arenja, MD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 
 

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Signature Page 4: University Hospital Innsbruck 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and local principal investigator (PI) have approved the research plan 
Version 1.2, 2022-05-023, and confirm hereby to conduct the project according to the plan, the 
current version of the World Medical Association Declaration of Helsinki, GCP, the STROBE 
Guidelines, and the local legally applicable requirements. 
 
 
Local Co-PI: 
PD Dr. Judith Martini 
 
 
 
 
 
Place/Date 
 
Signature 
 
Local Co-PI: 
Dr. Petra Hillinger: 
 
 
 
 
 
Place/Date 
 
Signature 
 
 
 
 

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Signature Page 5: University Hospital Geneva 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and local principal investigator (PI) have approved the research plan 
Version 1.2, 2022-05-023, and confirm hereby to conduct the project according to the plan, the 
current version of the World Medical Association Declaration of Helsinki, GCP, the STROBE 
Guidelines, and the local legally applicable requirements. 
 
 
Local PI: 
PD Dr. Bernardo Bollen Pinto, MD, PhD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 

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Signature Page 5: Cantonal Hospital Solothurn 
Project number Implement-PMI 
Project Title 
Implementation of a clinical screening and response 
system for perioperative myocardial infarction/injury after 
noncardiac surgery: feasibility and medicoeconomic impact   
 
The project leaders and local principal investigator (PI) have approved the research plan 
Version 1.2, 2022-05-023, and confirm hereby to conduct the project according to the plan, the 
current version of the World Medical Association Declaration of Helsinki, GCP, the STROBE 
Guidelines, and the local legally applicable requirements. 
 
 
Local PI: 
PD Dr., Nisha Arenja, MD 
 
 
 
 
 
Place/Date 
 
Signature 
 
 
 

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Abbreviations 
 
ACE-I 
Angiotensin-converting enzyme inhibitor 
AHF 
Acute heart failure 
AHF-PMI 
PMI caused by acute heart failure 
AMI 
Acute myocardial infarction 
ARB 
Angiotensin receptor blocker 
ARNI 
Angiotensin receptor blocker and neprilysin inhibitor 
ASA 
Acetylsalicylic acid (Aspirin) 
BASEL-PMI 
Basel Incidence, Patient Characteristics, Outcome and possible 
Strategies to improve Outcome of Perioperative Myocardial Injury after 
non-cardiac surgery: 1-Year Follow-up 
CAD 
Coronary artery disease 
CHF 
Chronic heart failure 
CI 
Confidence interval 
CTn 
Cardiac troponin 
DAPT 
Dual antiplatelet therapy 
ECG 
Electrocardiogram 
Hs-cTnT/hs-cTnI 
High-sensitivity cardiac troponin T / high-sensitivity cardiac troponin I 
IDDM 
Insulin-dependent diabetes mellitus 
MACE 
Major adverse cardiac events 
NIDDM 
Non-insulin-dependent diabetes mellitus 
MS 
Milestones 
PAD 
Peripheral arterial disease 
PCI 
Percutaneous coronary intervention 
PI 
Principal investigator 
PID 
Patient identification number 
PMI 
Perioperative myocardial injury/infarction 
SGLT-2 
Sodium-glucose cotransporter 2 
Implement-PMI 
Implementation of a clinical screening and response system for 
perioperative myocardial infarction/injury after noncardiac surgery: 
feasibility and medicoeconomic impact   
T1MI-PMI 
PMI caused by type 1 myocardial infarction 
Tachy-PMI 
PMI caused by acute tachyarrhythmia 
WP 
Work package 
 
 
 

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Administrative structure 
Co-Principal investigator, project Leader, 
methodologist, and coordinating researcher  
 
Institution. University Hospital Basel, 
Cardiology 
Name: Christian Puelacher, MD-PhD 
Address: Petersgraben 4 
Email: Christian.puelacher@usb.ch  
Phone: +41 61 556 58 30 
Fax: +41 61 265 85 77 
Co-Principal investigator, sponsor, project 
leader and coordinating researcher  
 
Institution. University Hospital Basel, 
Cardiology 
Name: Christian Müller, Professor, MD 
Address: Petersgraben 4 
Email: Christian.mueller@usb.ch  
Phone: +41 61 328 65 49 
Fax: +41 61 265 85 77 
Local principal investigator Lucerne: 
 
Name: Matthias Bossard, MD 
Address: Spitalstrasse 16, 6000 Luzern 
Email: matthias.bossard@luks.ch 
Phone: +41 41 205 14 77 
Local principal investigator Olten and 
Solothurn: 
 
Name: Nisha Arenja, MD 
Address: Baslerstrasse 150, 4600 Olten 
Email: nisha.arenja@spital.so.ch 
Phone: +41 62 311 41 30 
Key person in Olten 
Name: Reto Paganoni, MD 
Address: Baslerstrasse 150, 4600 Olten 
Email: reto.paganoni@spital.so.ch 
Phone: +41 62 311 47 11 
Key person in Solothurn 
Name: David Garay 
Address: Schöngrünstrasse 42, 4500 
Solothurn 
Email: david.garay@spital.so.ch 
Phone: +41 32 627 43 84 
Local Co-principal investigator Innsbruck: 
 
Name: Judith Martini, PD, MD 
Address: Anichstraße 35, 6020 Innsbruck 
Email: judith.martini@tirol-kliniken.at 
Phone: +43 512 504 80478 
Fax: +43 512 504 22450 
Local Co-principal investigator Innsbruck: 
 
Name: Petra Hillinger, MD 
Address: Anichstraße 35, 6020 Innsbruck 
Email: petra.hillinger@tirol-kliniken.at 
Phone: +43 512 504 80449 
Fax: +43 512 504 22450 
Key person in Innsbruck: 
Name: Axel Bauer, MD 
Address: Anichstraße 35, 6020 Innsbruck 
Email: axel.bauer@tirol-kliniken.at 
Phone: +43 50 504 256 21 
Fax: +43 50 504 256 22 
Local principal investigator Geneva: 
 
Name: Bernardo Bollen Pinto, MD, PhD 
Address: Geneva University Hospitals 
Department of Anaesthesia 
Rue Gabrielle-Perret-Gentil 4 
1205 Geneva 
Email: bernardo.bollenpinto@hcuge.ch 
Phone: +41 795532125 
Fax: +41 22 372 76 90 

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Key persons involved 
Name: Noemi Glarner, MD-PhDc 
Address: Petersgraben 4, 4031 Basel 
Email: noemi.glarner@usb.ch 
Phone: +41 61 328 56 58 
Fax: +41 61 265 85 77 
Key persons involved 
Name: Giovanna Lurati Buse, MD, MSc 
Address: Moorenstraße 5, 40225 Düsseldorf 
Email: Giovanna.LuratiBuse@med.uni-
duesseldorf.de 
Phone: +49 211 8118102 
Key persons involved 
Name: Mirjam Pargger, MD-PhDc 
Address: Petersgraben 4, 4031 Basel 
Email: mirjamregula.pargger@usb.ch 
Phone: +41 61 328 56 58 
Fax: +41 61 265 85 77 
 
 

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1. Background 
Perioperative setting 
The perioperative setting is increasingly recognised as an area of potential improvement. With 
over 300 million surgical procedures performed annually1,2, especially in resource-rich countries 
(~900.000 procedures done per year in Switzerland and Austria, respectively)1. Demand for 
surgery is likely to increase in the coming years with an increasingly elderly and comorbid 
population3. Despite advances in many fields of medicine, there is still a significant risk of death 
related to major noncardiac surgical procedures: a recent study even estimated postoperative 
death to be the third leading cause of death worldwide, accounting for 7.7% of global mortality4. 
The observed postoperative 30-day mortality depends on patient as well as procedural factors 
and ranges between 1% and 10%, much higher than commonly anticipated5–13. 
 
Perioperative cardiac complications and myocardial infarction/injury 
Perioperative myocardial infarction/injury (PMI), has been identified as a major contributor to 
these perioperative deaths8,9,13,14. First studies estimated that PMI, identified using high-sensitivity 
cardiac troponin (hs-cTn) only, is a major contributor to 34-42% of all deaths following noncardiac 
surgery within 30 days5,9. One of the key differences to spontaneous acute myocardial infarction 
(AMI) is that the vast majority of patients experiencing PMI do not show any typical ischemic 
symptoms, e.g. chest pain, or ischaemic changes on the electrocardiogram (ECG)5,6,8–13,15. The 
reasons were hypothesised to include intraoperative analgosedation and intense analgesia 
following surgery, but different pathophysiological mechanisms underlying PMI likely 
contribute14,16–19. As a consequence of the lack of typical symptoms and signs, most patients with 
PMI are currently not detected in routine clinical practice5,6. An elevated and dynamic value of 
cardiac troponin (cTn) is the sine-qua-non feature of AMI and due to its independence of 
symptoms and signs readily identifies patients with PMI if used as a routine screening in the 
perioperative phase. Importantly, patients with PMI detected via cTn not fulfilling any of these 
additional criteria of the Universal Definition of myocardial infarction required for the diagnosis of 
spontaneous AMI, showed a similar mortality to those that did (10.4%, 95% confidence interval 
[CI] 6.7-15.7 versus 8.7%, 95%CI 4.2-16.7, p=0.68)13. The finding that PMI is associated with 
such high mortality irrespective of whether it fulfils any additional criteria required for spontaneous 
AMI was also shown by other groups9,13,15 and highlights the importance of implementing 
systematic screening using hs-cTnT. 
 
An opportunity to improve outcome? 
Detection of a previously often undetected perioperative complication offers an opportunity to 
improve perioperative noncardiac surgery outcomes. Pilot data generated in previous studies, 
including the BASEL-PMI study, yielded promising results: in a retrospective study, intensification 
of medical treatment was associated with a reduced rate of major adverse cardiac events 
compared to patients without intensification (hazard ratio 2.8, p=0.04)20. In a subanalysis of the 
POISE study, use of aspirin and statins was associated with a reduced risk of death within 30 
days21. A first randomized controlled trial showed a reduction in a combined cardiovascular 
endpoint with the use of dabigatran (hazard ratio 0.72, 95% CI 0.55–0.93). Further, in BASEL-
PMI we demonstrated a change in mortality trend associated with the threshold that triggered 
cardiac consultations (Figure 1)13. 
In light of these data, first guidelines have begun advocating for routine troponin screening via 
perioperative high-sensitivity cTn (hs-cTn) measurements21–24. Given that PMI is largely 
asymptomatic and associated with high mortality rates, developing effective screening and 
appropriate management strategies to improve perioperative outcomes is paramount. 
However, details on implementation of such a PMI-screening as well as management 
recommendations are currently lacking, creating a situation of uncertainty in which other major 
guidelines recommend against screening25. Such striking differences create a situation of clinical 
uncertainty, which, if not addressed, will severely hamper the uptake in clinical routine, thus 
potentially missing an opportunity to improve perioperative care. Conversely, uncritical 
implementation of perioperative hs-cTn measurements might lead to overtreatment with e.g. 
coronary angiography. Indeed, previous studies have shown that only a minority of patients have 

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coronary findings suggestive of type 1 myocardial infarction, the typical presentation of 
spontaneous AMI16,26–28. Further, scarcity of resources calls for a balance in benefit and cost of 
any health program29,30, an aspect incompletely understood for this novel hs-cTn-screening. 
Currently, a small number of studies concerning PMI are ongoing. However, to the best of our 
knowledge, no study addresses our research topic. Registered trials currently focus on preventing 
PMI, 
with 
placebo-controlled 
trials 
testing 
colchicine 
(NCT04139655), 
ivabradine 
(NCT04436016), metoprolol (NCT03138603), and tranexamic acid (NCT03505723), as well as 
management strategies including hypothermia (NCT03111875), or perioperative hypotension 
avoidance strategies (NCT02533128, NCT03505723).  
 
During the last years, we contributed significantly to the evolving evidence surrounding PMI. We 
successfully performed BASEL-PMI at three tertiary centres, one of the largest observational 
studies on this topic and the largest including high-risk patients (NCT02573532)13,31–35. Our 
analyses incorporated a definition of PMI in line with the Universal Definition of Myocardial 
Infarction, by measuring a preoperative baseline hs-cTn value to reliably distinguish acute events 
from chronic elevations13. Besides corroborating the results from previous studies showing the 
strong association of PMI with mortality, we undertook efforts to elucidate subtypes of PMI. First, 
we described in 2546 single centre cases the distinction between “cardiac” PMI (analogous to the 
previously prospectively derived “myocardial injury after noncardiac surgery (MINS)”5,9,15) versus 
“extracardiac” PMI, caused by primarily extracardiac events such as severe sepsis13. We found 
strikingly different associated outcomes of 6.1% (95%CI 3.6-10.0%) mortality within 30 days after 
cardiac PMI vs 33% (95%CI 20–48%) after extracardiac PMI. 
At our institution we had already implemented routine hs-cTnT screening into clinical practice, 
with cardiology consultations following detection of PMI. In an exploratory analysis, we found a 
change in mortality trend associated with the threshold that triggered cardiac consultations 
(Figure 1)13, providing further evidence for the potential to improve outcome with PMI screening. 
 
 
Figure 1 Association of 30-day mortality in percent with left) absolute hs-cTnT increase and 
right) maximum hs-cTnT level. The green dashed line indicates the threshold for activating the 
response system of PMI in BASEL-PMI13 
 
We expanded on this first study by engaging two further centres in our prospective observational 
study and increased the sample size significantly to further explore the aetiology of PMI. After 
inclusion of 5602 cases with 848 detected PMI, we were able to show that PMI can be further 
grouped into different aetiologies: only a minority of PMI cases (7%) seems to be caused by type 
I myocardial infarction due to atherothrombosis, and type II myocardial infarction due to supply-
demand mismatch as well as acute injury resulting from tachyarrhythmias, acute heart failure, or 
extracardiac PMI are as common33 (Figure 2). These distinct aetiologies show starkly different 
outcomes (Figure 3), highlighting the unmet clinical need. However, identification of these 
subtypes also may help clinicians, as guidelines are available, providing management 
recommendations for clinicians confronted with asymptomatic PMI36–41. 

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Figure 2 PMI aetiologies in high-risk patients (adapted from Puelacher et al, JACC 202033)  
 
 
Figure 3 Kaplan-Meier and adjusted hazard ratios (aHR) for (left) all-cause mortality and (right) 
major adverse cardiac events (MACE) within 30-days stratified according to PMI-aetiology, 
showing relevant differences in occurrence and timing (Puelacher et al, JACC 2020 in press33) 
 
The way forward: a management trial 
Using data generated from our previous work we estimated the required size of an outcome trial 
evaluating a hs-cTn-based screening for PMI. Assuming an incidence of PMI of ~20%, a MACE 
rate of 15-45% at 90 days (dependent on PMI aetiology, Figure 2+3) in patients with PMI vs 6% 
in patients without PMI, a 25% reduction of MACE in screened patients, and screening being 
performed successfully in 90% of cases, a total of >50.000 patients would be needed in a step-
wedge-cluster design to be able to test for superiority of PMI-screening. This high sample size is 
mainly due to the numerous patients screened without detection of PMI, as their outcome will not 
be affected by PMI screening. Before initiating such a large scale trial, we think that a feasibility 
study is paramount. Therefore, the aim of the present study is to generate essential data to 
quantify assumptions for a large randomised trial, identify and prevent barriers to implementation 
of the intervention, and generate safety and medicoeconomic data to facilitate buy-in of the 
required interdisciplinary teams at our potential collaborating partner institutions. 

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2. Aim 
Primary aim 
We aim to generate feasibility data for a future randomised step-wedge cluster trial assessing the 
effect of implementing a clinical routine screening and response system for cardiac complications, 
including perioperative myocardial infarction/injury (we will use the umbrella term “PMI-
screening”) in patients at increased cardiovascular risk undergoing inpatient noncardiac surgery. 
Primary outcomes are the feasibility of implementation and quantifying opportunities to improve 
care via PMI-screening. 
 
Secondary aims 
 Evaluate the medicoeconomic impact of implementing a PMI-screening 
 Explore the occurrence and timing of MACE following PMI to find the best follow-up period  
 Identify potential barriers to implementation 
3. Design and target measurements 
 
This pilot study is a prospective observational multicentre binational before-after-comparison 
study. Centres which have decided to implement the novel guideline-recommended PMI-
screening for routine clinical care are eligible for the present study. We will observe a pre-
implementation phase of 3 months prior to implementation of PMI-screening, followed by a two-
week blinded transition period, and finally the post-implementation phase of at least 6 months. 
 
 
Figure 4 Study flow chart; during the pre-implementation phase patients are managed 
according to local protocols, during the two-week transition phase (X) the implementation of a 
PMI-screening according to standard operating procedure is introduced, after which the post-
implementation phase begins 
 
Pre-implementation phase 
Patients in the pre-implementation phase are treated according to current local standard of care, 
which usually includes measurement of cTn and cardiology consultation after noncardiac surgery 
only at discretion of the treating physician, usually if perioperative myocardial infarction/injury is 
suspected clinically. 
In this phase, patient inclusion begins according to the inclusion criteria set out for the local PMI-
screening. The same data as during the post-implementation phase will be collected, even though 
the screening is not done at this time. 
 
Blinded transition phase 
Each centre transitions its standard operating procedure to the guideline-recommended 
perioperative PMI-screening (see below paragraph “PMI-screening”). The transition will be 
supported by study staff in form of minimal training requirements, support for generation of local 
standard operating procedures, presentations for involved disciplines, and information material. 
As organisational issues are expected during this transition, no patients are included into the 
study during these two transition weeks. 
 
Post-implementation phase 
During the post-implementation phase, hospitals utilise routine PMI-screening (see below 
paragraph “PMI-screening”). Hospitals adopt routine PMI-screening for eligible patients. If they 
satisfy the inclusion criteria for this study, they are included, even if no PMI screening was 
conducted or PMI screening was incomplete (no or only one troponin measurement).  

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Special considerations concerning SARS-COV-2 
In case of an interruption of planned surgery at one of the participating hospitals, the patient 
recruitment will be suspended until restrictions are lifted and the local principal investigator (PI) 
judges the situation to again reflect routine clinical conditions. The total duration of each phase 
shall be maintained. 
 
Definition of PMI 
PMI will be prospectively defined as an absolute increase in hs-cTn of +≥99th percentile above 
preoperative values (or between two postoperative values if the preoperative value is missing) 
within three days of surgery.  
PMI represents a spectrum of different potential triggers for the acute myocardial injury, including 
myocardial infarctions as well as acute injuries by cardiac or extracardiac triggers [Puelacher C 
et al, JACC 202033]. For management of PMI, identification of these aetiologies likely provides an 
opportunity for individualised care. These include 
 
cardiac PMI: type I myocardial infarction [T1MI-PMI], type II myocardial infarction [T2MI-
PMI], acute injury due to acute heart failure or Tako-Tsubo [AHF-PMI], acute injury due to 
tachyarrhythmia [Tachy-PMI] 
 
extra-cardiac PMI: severe sepsis, systemic inflammatory response syndrome, pulmonary 
embolism, cardiac trauma, other causes 
Based on findings from prior studies showing that asymptomatic elevations in cTn were also 
associated with increased short-term mortality5, we do not mandate specific symptoms or specific 
ECG changes into the definition of PMI. We use delta values instead of maximum postoperative 
levels to ensure that our definition reflects “acute” myocardial damage and was time-related to 
surgery, thus avoiding misclassification of chronically elevated levels. Chronic hs-cTn elevations 
are expected in a relevant amount of (surgical) patients42, and were previously shown to be 
independently associated with increased risk of death and major adverse cardiac events43. We 
chose an absolute rather than a relative delta hs-cTn level for the diagnosis of PMI, because 
absolute changes have shown higher diagnostic accuracy as compared to relative changes in the 
detection of acute MI in the non-operative setting44,45. The absolute increase of ≥99th percentile 
of each respective assay was chosen as it represents the 99th percentile of healthy individuals 
and thereby all PMIs invariably would fulfil the change as well as the absolute cTn criteria required 
for the diagnosis of spontaneous AMI46. This definition was evaluated in the prospective 
observational study BASEL-PMI for three different cTn-assays, using the respective 99th 
percentiles: Roche Elecsys hs-cTnT (+≥14ng/L)13,31, Siemens Ultra sensitive cTn assay 
(+≥45ng/L)31. 
 
PMI-screening 
The PMI-screening is a bundle of care consisting of  
 Identification of high-risk patients prior to surgery (for details, see “Origin of the data” and 
“Inclusion criteria”) 
 Measurement of high-sensitivity cardiac troponin within 30 days prior to surgery and on the 
first and second postoperative day (Figure 5) 
 Daily comparison of postoperative hs-cTn values to preoperative values and identification 
of PMI defined as absolute increase of ≥+99th percentile of hs-cTn without need for 
symptoms or ischemic electrocardiography changes (for details, see “Definition PMI”) 
  

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Figure 5 Schematic flow-chart of PMI-screening13 
 
 
Activation of the PMI-screening response system via automated or manual alert 
 
Clinical evaluation of patients with PMI according to local standardised operating 
procedure (recommendation see Figure 6) 
 
Clinical adjudication of PMI aetiology to identify the most likely cause 
 
Management of PMI according to a recommended management pathway (see below), 
following ESC guidelines recommendations36–41 
 
Screening days 
Successful PMI-screening depends on multiple disciplines working together closely. To avoid the 
chain breaking due to reduced personal resources on weekends and work holidays, participating 
institutions will prospectively define whether screening will be done on every day or prospectively 
specify exceptions, e.g. weekends.  
 
 
 

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PMI management pathway  
 
Figure 6 Recommendation for local standard operating procedure for evaluation and 
management of patients with PMI detected via PMI-screening8,36–41 
 
 
 

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Schedule and milestones 
Work packages (WP) and milestones (MS) 
WP 1 Study initiation 
• MS 1: data environment on basis of BASEL-PMI environment created (03/21) 
• MS 4 site initiation: Olten (07/21), Lucerne (06/22), Solothurn (06/22) Innsbruck (07/22), 
Geneva (08/22) 
 
WP 2 Transition phase 
• MS 1 preparation for transition complete: Olten (09/21), Lucerne (08/22), Solothurn (08/22), 
Innsbruck (09/22), Geneva (10/22) 
• MS 2 transition phase: Olten (10/21), Lucerne (11/22), Solothurn (11/22) Innsbruck (10/22), 
Geneva (11/22) 
 
WP 3 Data generation 
• MS 1 First patient in: Olten (07/21), Lucerne (06/22), Solothurn (06/22), Innsbruck (07/22), 
Geneva (08/22) 
• MS 2 stop patient enrolment: Olten (05/22), Lucerne (04/23), Solothurn (04/23), Innsbruck 
(05/23), Geneva (06/23) 
• MS 3 follow up complete (Q4/24) 
• MS 4 export e-data complete (Q1/25) 
• MS 5 monitoring and data cleaning done (Q2/25) 
 
WP 4 Statistical analyses 
• MS 1 statistical data file complete (Q3/25) 
• MS 2 feasibility data analysed and definite sample size estimation done (Q4/25) 
• MS 3 submission of first manuscript (Q2/23) 
4. Origin of the data 
Consecutive patients at increased cardiovascular risk undergoing inpatient noncardiac surgery 
with a planned postoperative hospitalisation of ≥2 overnight stays, eligible for routine PMI-
screening at the participating institutions. 
5. Inclusion criteria 
 Patients eligible for routine PMI-screening at participating institutions 
 Patients requiring hospitalisation with ≥2 overnight stays after surgery  
 Age ≥40 to ≤85 years AND history of coronary artery disease (CAD), peripheral artery 
disease (PAD), cerebrovascular disease/stroke, insulin-dependent diabetes mellitus 
(IDDM), or chronic heart failure (CHF) 
 Elective or non-elective surgery 
 Patients undergoing orthopaedic, trauma, vascular, spinal, thoracic, neurological, urology, 
or visceral surgery 
6. Exclusion criteria  
 Heart surgery, cardiac valve intervention, or cardiac catheter ablation within 14 days prior 
to surgery OR involvement of heart surgery at index surgery OR surgery planned on 
cardiopulmonary bypass 
 Patients undergoing plastic/reconstructive, ophthalmologic, dental, hand surgery, or ear-
nose-and-throat surgery 
 Chronic renal failure in dialysis, patients undergoing renal transplant surgery 
 Moderate to severe dementia 
 Inclusion into study within the last five days 

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 Existence of a documented refusal to further use of existing data OR active decline of further 
use of existing data 
 
6.1 Exclusion from follow-up analysis 
 
Decline of consent during follow-up (irrespective of mail or telephone) 
7. For which health-related personal data should the approval be 
granted? 
We wish to collect data from clinical forms and electronic health records manually and 
electronically, depending on local availability of electronic health records and complexity of the 
data extracted. This will be done according to our standard operating procedures, with which we 
have been successful in implementing such a data collection in a previous study, NCT02573532, 
at two centres with different clinical management systems. Reporting will adhere to the REporting 
of studies Conducted using Observational Routinely-collected health Data (RECORD) 
Statement47. 
 
Baseline and preoperative clinical data: We will collect data as assessed by routine pre-
operative anaesthesia examination and/or from prior documents, including patient demographics 
(age, sex, weight, height), medical history (pre-existing CAD, PAD, stroke, CHF, cardiac valve 
disease, diabetes mellitus, chronic kidney failure, chronic obstructive pulmonary disease, surgery 
due to malignancy), current cardiovascular medication, basic laboratory data (haemoglobin, 
creatinine, cardiac troponin), ECG  
 
Intraoperative data: We will collect type of surgery, type of anaesthesia, cardiovascular risk of 
surgery (classified using the ESC/ESA surgical risk category21), and urgency of procedure. If 
electronic continuous monitoring data and/or ECG-recordings are available, these will be 
electronically exported. 
 
Postoperative data: Postoperative laboratory data (haemoglobin, creatinine, cardiac troponin). 
Postoperative complications (sepsis, stroke, pulmonary embolism, pneumonia, need for blood 
products) will be extracted from the discharge report as diagnosed by the attending physicians. 
 
Resource data: Number of postoperative blood draws on day 1 and 2, length of hospital stay, 
days on intensive care unit, consultations within day 1-3, ECG within day 1-3, cardiac stress 
testing within 30 days, and cardiac catheterisation within 30 days.  
 
Barriers to implementation: structured interviews will be conducted with the local PI, local study 
nurses, and one further person in charge of the local screening (identified by local PI). 
8. Request for an exception according to the Swiss Human Research 
Law (HFG) Art. 34 
We request an exemption from the individual prospective consent for our study for data generated 
during clinical routine according to the Swiss Human Research Law (HFG) Art. 34 during both 
the pre- and post-implementation phases. Using a consent procedure would be detrimental for 
this study for the following reasons: 
 
PMI-screening is increasingly recommended by international guidelines22–24,48,49. Hence, 
implementation in clinical routine is planned with or without parallel scientific evaluation at 
the participating hospitals and is expected to occur in further hospitals in the coming years. 
Evaluation of the routine implementation could greatly enhance our understanding of 
benefits and potential harms of PMI-screening. 

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 
As the institutional standard of practice of perioperative care will be modified, the most 
relevant data will be generated as part of the clinical routine, not for the study. There will 
be no study-specific intervention. The only variables collected specifically for this study 
will be the follow-up and the adjudication of PMI. 
 
Patients in the pre-implementation and the post-implementation phase need to have 
identical inclusion criteria during the study to ensure their comparability. Therefore, 
implementing a new consent process between these phases would introduce a severe 
selection bias. Both patients and clinicians may be less likely to participate during the post-
implementation phase if they must handle an additional consent procedure. Forgoing the 
analysis of the pre-implementation phase to avoid a different consent procedure would 
prevent us to observe the effects of PMI-screening implementation. 
 
Patients undergoing emergency procedures may not be recruited using a standard 
consent procedure. Retrospective inclusion of recently operated patients would also 
induce a sampling bias towards patients with a favourable outcome. 
 
During follow-up, patients are informed about the study and asked to return a questionnaire. 
Returning the questionnaire will be seen as consent for follow-up data. If a patient is contacted 
by phone, consent is requested after information about the study prior to doing the questionnaire 
on the phone.  
For patients who die before follow-up can be conducted, we request an exemption from individual 
prospective consent. In this case, follow-up will be done via family physician and/or treating 
institutions. Our reasoning: 
 
PMI-screening will occur in patients undergoing elective as well as emergency 
procedures, and PMI was associated with a mortality at 30 days of 10% in our initial 
observational study despite early detection and management. Therefore, a consent 
process would introduce a sampling bias towards elective patients. Retrospectively 
requesting consent from relatives following the death of a patient shortly after surgery 
would be intrusive, constitute a breach of piety, and hence yield elevated rates of non-
consent. 
 
 
For this study based on the observation of a novel clinical procedure aimed at improving 
outcomes, but not tested prospectively, we believe the interest of research seems to outweigh 
the limited disturbance and risks for the patients. The data generated by this prospective 
implementation study will help further patient care in the perioperative period by providing data 
on the benefits and safety of perioperative systematic screening, and on response systems for 
PMI. Due to the large sample size and representative patient sampling, we expect a good external 
validity for tertiary centres in Switzerland and worldwide, particularly if we can broadly include 
patients without selection or sampling bias. Further, the results will provide the much needed pilot 
data for a future investigator-initiated randomised controlled trial. Finally, the data will be used for 
internal quality control at the participating hospitals to improve patient care directly. 
 
The risk incurred by patients participating in this study would be minimal. The only threat could 
be a breach in confidentiality. We will counteract this risk by regularly training our staff, coding 
patient data at the earliest time point, and using safe data exchange servers. 
9. Confirmation that no documented decline of further use of data 
exists 
The PI and the local PIs confirm that no health-related data and no biologic material will be used 
in patients with a written or documented decline of consent for further use of their routine data or 
if the patient actively declines further use of data during the follow-up. In case of the patient’s 
death before follow-up, no decline of further use of data is assumed. 

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10. Informed consent for follow-up questionnaire 
During follow-up, patients are informed about the study and asked to return a questionnaire. 
Returning the questionnaire will be seen as consent for follow-up data. If a patient is contacted 
by phone, consent is requested after information about the study prior to doing the questionnaire 
on the phone.  
Patients who die prior to contact during follow-up, consent to data collection is assumed. 
11. Which person group is responsible for the transmission of biological 
material and health-related data 
Christian Puelacher MD-PhD, Prof. Christian Müller MD 
Local PIs:  
Geneva: 
Bernardo Bollen Pinto MD-PhD 
Lucerne: 
Matthias Bossard MD 
Olten: 
Nisha Arenja MD 
Innsbruck: Judith Martini MD, Petra Hillinger MD 
Solothurn: Nisha Arenja MD 
 
No biological material will be collected. 
12. Who takes responsibility for receiving this data / material? 
Christian Puelacher MD-PhD, Prof. Christian Müller MD 
Local PIs:  
Geneva: 
Bernardo Bollen Pinto MD-PhD 
Lucerne: 
Matthias Bossard MD 
Olten: 
Nisha Arenja MD 
Innsbruck: Judith Martini MD, Petra Hillinger MD 
Solothurn: Nisha Arenja MD 
 
No biological material will be collected. 
13. Which person group will have access to health-related data during 
the conduct of this study? 
Patient data will be processed by a dedicated research team consisting of future doctoral 
candidates, study nurses or master students of medicine at the participating institutions as well 
as the study coordination centre (Cardiovascular Research Institute Basel).  
Persons already known are  
Coordination centre: 
Christian Puelacher MD-PhD, Prof. Christian Müller MD,  
 
Danielle Gualandro MD-PhD, Noemi Glarner MD-PhDc,  
 
Mirjam Pargger MD-PhDc 
Geneva: 
Bernardo Bollen Pinto MD-PhD 
Lucerne: 
Matthias Bossard MD 
Olten: 
Nisha Arenja MD 
Innsbruck: 
Judith Martini MD, Petra Hillinger MD  
Solothurn: 
Nisha Arenja MD 
14. Who is responsible for data protection? 
The PIs and dedicated data manager will ensure confidentiality, and an audit trail will be done for 
manual as well as automated data entries. Staff will be trained and regularly retrained in safe data 

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management and confidentiality according to GCP50. Further, every staff member will at one 
point sign a document of confidentiality related to patient’s medical data from the (digital) hospital 
archives. Data will not be disclosed to third parties, except during monitoring or audits by the 
competent authorities. 
Data generation, transmission, storage and analysis of health-related personal data within this 
project will follow the current Swiss legal requirements for data protection and will be performed 
according to the Ordinance HRO Art. 5. 
15. Scientific methodology 
Feasibility endpoints 
 Screening: percentage of patients eligible for PMI-screening according to inclusion criteria, 
but not screened during the implementation phase (defined as no or only one measurement 
of hs-cTn done during screening days) 
 Incidence of PMI: percentage of patients experiencing PMI following noncardiac surgery 
 Cardiology consultation: percentage of patients with detected PMI by PMI-screening on 
screening days, seen vs. not seen by a cardiologist  
 Actionable: percentage of patients with PMI in whom improvements in care are possible, 
defined as guideline-recommended interventions or changes in medication 
o T1MI-PMI without absolute contraindications to percutaneous coronary intervention 
(PCI) and/or dual antiplatelet therapy (DAPT)24,36,37 
o Tachy-PMI requiring medical treatment or caused by newly diagnosed atrial fibrillation 
or preexisting atrial fibrillation with CHADS2-VASC-score of ≥2 without preexisting 
medical anticoagulation40,41 
o AHF-PMI with newly detected heart failure, or pre-existing heart failure not already on 
optimal medical therapy (defined as class I recommendation for angiotensin 
converting enzyme inhibitors [ACE-I] / angiotensin receptor blockers [ARB] / 
angiotensin receptor neprilysin inhibitors [ARNI] + aldosterone antagonist + sodium-
glucose cotransporter 2 [SGLT-2] inhibitor  + beta-blocker in heart failure with reduced 
ejection fraction) each on >50% target doses, or Tako-Tsubo cardiomyopathy39 
o newly diagnosed CAD, or preexisting CAD without optimal medical therapy [defined 
as class I recommendation for statin, ASS/DAPT, ACE-I/ARB/ARNI, betablocker, 
and/or SGLT-2-inhibitor]38 
o extracardiac PMI caused by pulmonary embolism without absolute contraindication 
to oral/intravenous anticoagulation50 
o extracardiac PMI caused by sepsis, pneumonia, or cardiac trauma are judged to not 
be actionable unless first diagnosis is made during cardiology consultation 
 Management: percentage of cardiac consultation recommendation for management in line 
with the proposed algorithm in respect to the suspected initial classification of PMI aetiology  
 Diagnostic challenge: number of cases with mismatch of initial classification of PMI 
aetiology (and management pathway) at time of consultation versus final adjudication. In 
case of two differential diagnoses stated on the cardiology consultation, mismatch is seen 
when none of the diagnoses correspond to the final adjudication. If three or more differential 
diagnoses are stated, mismatch is seen in any case even if the final adjudication diagnosis 
is stated 
 Barriers to outcome: concepts and themes identified by interviews  
 Safety: Occurrence of inappropriate interventions or complications of cardiology 
diagnostics and interventions 
o Overtreatment: Inappropriate coronary angiography, defined as showing no 
significant coronary stenosis of ≥25% of vessel diameter 
o Diagnostic angiography and interventional or surgical revascularisation: dissection of 
coronary artery, type 4a myocardial infarction, death attributed to complications of the 
percutaneous coronary intervention or bypass surgery  

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o Major bleeding: defined as Bleeding Academic Research Consortium (BARC)51 type 
3 (overt bleeding plus haemoglobin drop of ≥ 3 g/dL or need for transfusion, cardiac 
tamponade, bleeding requiring surgical intervention for control or vasoactive drugs, 
and intracranial, intraspinal or intraocular bleeding compromising vision), type 4 
(coronary artery bypass associated), and type 5 (fatal bleeding), with onset earliest 
on postoperative day 1 until twelve months 
 
Medicoeconomic impact 
 Clinical resources: number of postoperative blood draws on day 1 and 2, laboratory 
measurements (troponin, haemoglobin) pre- and postoperatively, length of hospital stay, 
days on intensive care unit, days on intermediate care unit, number of transfusions 
 Intraoperative resources: arterial blood pressure measurement 
 Cardiology resources: cardiology consultations or ECG within postoperative day 1-3, 
cardiac stress testing, cardiac magnetic resonance imaging, or coronary angiography within 
30 days, coronary angioplasty or surgical coronary bypass within 90 days 
o Resources which should not be impacted: cardiology consultation preoperatively, 
ECG preoperatively 
 Postoperative cardiovascular medication: new statins, ASS/DAPT, anticoagulation, 
betablockers, ACE-I/ARB/ARNI, SGLT-2-inhibitors  
 
Clinical endpoints 
Occurrence and timing of a composite of major adverse cardiac events (MACE) within twelve 
months, consisting of: 
 all-cause death 
 acute myocardial infarction type 1 (according to the fourth universal definition of myocardial 
infarction24, AMI) 
 survived sudden cardiac death 
 acute heart failure (AHF) 
 
Adjudication of PMI 
PMI will be prospectively defined as an absolute increase in hs-cTn of ≥+99th percentile above 
preoperative values (or between two postoperative values if the preoperative value is missing) 
within three days of surgery. 
 
Adjudication of PMI aetiology 
PMI-aetiology will be centrally adjudicated by two independent experts based on all clinical 
information obtained during index hospitalization, including ECG, serial laboratory measurements 
including cTn and haemoglobin, monitoring of vital signs in the perioperative and intraoperative 
period, as well as echocardiography, cardiac stress testing and coronary angiogram if performed, 
and follow-up. In cases of disagreement between the two reviewers, consensus will be sought 
and found by discussion with a third reviewer.  
PMI will be hierarchically adjudicated into:  
1. extra-cardiac if caused by a primarily extra-cardiac disease such as: severe sepsis52, 
pulmonary embolism, cardiac trauma, other cause 
2. cardiac, further subtyped into type I myocardial infarction (T1MI-PMI), tachyarrhythmia, 
acute heart failure or Tako-Tsubo cardiomyopathy (AHF-PMI; extra-cardiac: need for 
acute new haemodialysis) 
3. cardiac: type II myocardial infarction (T2MI-PMI) if there was absence of abovementioned 
causes (1, 2) with documented or suspected severe hypotension or anaemia 
 
Follow-up 
After one year, patients will be contacted by mail and, if they do not respond, by telephone to 
complete a questionnaire concerning the predefined MACE. In case of an event occurring, 
relevant medical files will be requested from the patient’s general practitioner and/or treating 

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hospitals. The follow-up will be assessed after at least 365 days.Data of patients eligible for but 
not receiving the PMI-screening during the post-implementation phase will be collected similar to 
that of patients in the pre-screening phase. 
In case of decline of consent for the follow-up, we will censor the patient at the time of last known 
clinical status. 
 
Statistical analyses 
95%-confidence intervals (95%CI) of absolute numbers and percentages will be calculated using 
the formula proposed by Agresti et al53. All analysis for the feasibility and resource endpoints will 
be done overall and repeated for each centre individually. 
 
Feasibility endpoints 
Feasibility endpoints will be calculated as absolute numbers and percentages with 95%CI. 
 Screening: patients eligible for PMI-screening during workdays, but not screened. 
 Incidence of PMI: the number of patients experiencing a PMI will be calculated. 
Subanalysis of the incidence for each of the patient characteristics used for inclusion into 
the screening (CAD, PAD, stroke, IDDM, CHF) will be performed. Further, incidence of each 
predefined aetiology will be calculated. 
 Cardiology consultation: patients with detected PMI by PMI-screening on screening days, 
but not seen by a cardiologist. As supplemental analysis percentage of all detected PMI 
irrespective of screening days will be calculated.  
 Actionable: patients with PMI in whom improvements in care were possible. Sensitivity 
analysis will be performed excluding SGLT-2-inhibitors as actionable medication, due to 
expected increased uptake in the next years. 
 Management: cardiac consultation recommendation for management in line with the 
proposed algorithm. Qualitative and exploratory evaluation of cardiology consultation 
recommendations deviating from the proposed algorithm will be performed.  
 Diagnostic challenge: cases with mismatch of initial classification will be calculated as the 
amount of misclassification when using the final adjudication as reference standard. As 
further exploration, we will do a comparison of the interobserver agreement Kappa using 
the initial classification and the final adjudication with the interobserver agreement during 
the final adjudication process. 
 Barriers to implementation: semi-structured interviews will be evaluated for common 
themes as well as identification of all individual problems encountered 
 Safety: we will calculate the total number of inappropriate interventions, complications and 
bleedings in the pre-implementation vs post-implementation phase, report them as absolute 
number at 365 days, and compare them via Fisher’s exact test. 
 
Medicoeconomic impact 
We will do a cost-consequence analysis with a time horizon of 365 days. We consider the 
heterogeneous long-term prognosis of patients undergoing noncardiac surgery (i.e. ranging from 
cancer surgery to orthopaedic surgery) as impedimental for outcome projection and modelling 
beyond the first postoperative year. We will use the perspective of the statutory/public health care 
payer. Resource items will be valued using country-specific price weights. Because we will 
multiply countries specific resource use by country-specific price weights, heterogeneity of unit 
cost is not expected to bias the pooled results54. The main source for price weights will be national 
tariffs. We will use constant price weights, i.e. those valid at the time of the analysis. Considering 
the follow-up duration of 1 year, we will not adjust for time preference. Pharmacy cost will be 
obtained from national formulary lists and relevant professional fees. We will conduct complete 
case analysis (no imputation of missing data). The main analysis will apply non-parametric 
bootstrapping (2000 replications) for calculation of the mean effect and mean costs per patient 
and their 95% CI (nonparametric bootstrap percentile). Disaggregated cost will be reported for 
the pooled sample and separately for each country.  
We will present cost in Swiss Francs as common currency base (exchange rates valid at the time 
of the analysis will be used), but secondary also in Euro. We will present graphically (cost-

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effectiveness plane) the distribution of incremental cost effectiveness ratios (ICER) generated by 
bootstrapping replications. 95%CI of the ICER point estimate will be calculated using the 
bootstrap percentile method or bootstrap acceptability method as applicable. Further, we will plot 
cost-effectiveness acceptability curves (CEAC). Reporting will follow the Consolidated Health 
Economic Evaluation Reporting Standards (CHEERS) statement55. 
 
Exploration of heterogeneity in medicoeconomic impact  
We will present disaggregated cost and health consequences and their incremental ratio (95%CI) 
and as multiple CEAC for the following predefined subgroups: 1) low, intermediate and elevated 
procedural risk according to the classification endorsed by the ESC/ESA21; 2) elective vs 
nonelective surgery; 3) major vascular surgery; 4) previous history of coronary artery disease; 5) 
Revised Cardiac Risk Score56 ≤2 / >2. The same approach will be used to evaluate heterogeneity 
by jurisdiction. 
Finally, we will compare the incidence of preoperative ECG and cardiology consultations (which 
should not be impacted by the screening), to detect potential changes in management not 
mandated by the PMI-screening. 
 
Clinical endpoint analysis 
For outcome analysis of the pre- and post-implementation phase we will use a censoring phase 
of 2 days following surgery for AMI and AHF, defining AMI and AHF as occurring between day 3 
and 365 after surgery (= after PMI-screening). This will allow to differentiate detection of 
perioperative AMI and AHF by the PMI-screening vs. events during the follow-up. Death and 
survived sudden cardiac death occurring on the day of surgery will be blinded as PMI-screening 
could not affect occurrence of events before postoperative day 1.  
 Estimation of treatment effect: for comparison of occurrence of death and MACE in the 
pre- vs post-implementation period, we will first construct univariable Kaplan-Meier plots. 
As a secondary analysis, this we be repeated for each single component of MACE. Second, 
we will compare preoperative baseline characteristics of the pre-implementation and post-
implementation phase and construct a multivariable binary logistic model including baseline 
variables statistically significant between the phases as well as each centre and month as 
independent variables. The number of outcome events will determine the maximum amount 
of variables in the model (outcome events/10)56. 
 Timing of MACE following PMI: to determine the postoperative period during which the 
MACE rate remains increased following the surgical procedure, we will investigate the time 
of occurrence of MACE within the follow-up period of 365 days. We will construct a model 
with days after surgery on the x-axis and pending MACE on the y-axis. We will investigate 
changes in the slope of the curve, and assume that after postoperative day 300 the MACE 
rate is no longer affected by the surgical procedure57. By comparison of the slope of piece 
wise linear regression of pending MACE, a change in trend in MACE rate will be estimated.  
 Total number of MACE: we will calculate the total number of detected MACE in the pre-
implementation vs post-implementation phase and report them as absolute number and 
compare them via Fisher’s exact test. No events will be censored for this analysis, all MACE 
and all cardiac PMI will be counted at 30 days, and 365 days. 
 
Sample size considerations 
This observational before-after cohort is designed as a pilot study to assess the feasibility and 
safety of a step wedge-cluster implementation study. The data generated at different centres with 
different levels of care will allow us to estimate the feasibility of implementing a screening, quantify 
the opportunities to improve care following PMI detection, and allow for identification of the 
optimal follow-up period after surgery to optimise the effect/noise ratio. To observe the full range 
of the PMI-screening, a sufficient number of patients is required. We expect PMI-screening to be 
successfully and completely done in >90% of patients, PMI to occur in 20-25% of patients 
screened, and the PMI aetiologies with lowest incidence (T1MI-PMI, AHF-PMI, Tachy-PMI) to 
occur in ~5-7% of PMI each. Therefore, each centre would need to include a minimum of 300 

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patients in the post-implementation phase to provide data on the full range of PMI aetiologies at 
least twice, assuming worst-case incidences.  
We expect that each participating centre will be able to include at least 150 patients in the pre-
implementation phase and 300 patients in the post-implementation phase, with Lucerne and 
Innsbruck being expected to contribute 200 pre-implementation and 400 post-implementation 
patients each. With a minimum of 900 patients undergoing screening, we will be able to assess 
feasibility concerning implementation of screening or cardiology consultation. We expect to find 
a minimum of 160 PMI, allowing for a good approximation of actionability, management, and 
diagnostic challenge of PMI detected in PMI-screening. 
 
Methods for minimising bias 
Selection bias: to avoid positive or negative selection bias, we will include consecutive eligible 
patients irrespective of whether they received screening or not. Inclusion of hospitals providing 
different levels of care shall ensure a representative sample. 
Misclassification bias: PMI aetiology, management, actionability, and endpoints will be 
adjudicated by two independent reviewers trained in either cardiology, internal medicine, or 
anaesthesiology, following a standard operating procedure. In cases of disagreement, a third 
senior reviewer will aid in finding the correct diagnosis.  
Attrition bias: loss to follow-up could have major impact on our study. A three-step procedure 
using first postal letters, then contacting the patient via telephone, followed by contacting the 
primary care physician and checking local death records shall ensure completeness of follow-up 
data. In the BASEL-PMI study, this approach led to a data completeness of 99.7% for death and 
99% for MACE at one year13. 
Effect of season: to avoid seasonal effects (e.g. influenza, ice-induced falls), we aim to distribute 
the site initiations over the year to balance this effect. Further, month of surgery will be 
implemented in the multivariable model as random effect. 
 
Further potential pitfalls 
While judged unlikely, a centre might drop out of the study, e.g. due to potential problems with 
recruitment or implementation of PMI-screening. In this event we will strive to include one of the 
centres with whom we are in contact for the potential follow-up step-wedge trial instead. In this 
case, we would inform the ethics commission immediately. 
Conversely, a surge in the number of surgeries (e.g. post-COVID) might provide more eligible 
patients per week than expected. For this, we would limit the inclusion per centre to 250 patients 
in the pre-implementation phase and 500 in the post-implementation phase. Recruitment would 
terminate early instead of reducing patients enrolled per day to avoid a selection bias. 
Moreover, the increasing amount of article retractions due to data manipulation or scientific fraud 
mandate preventive measures. Therefore, all statistical analyses will be double-checked by a 
second experienced researcher, using a securely held and in double available statistical data file. 
16. Obligation of reporting 
A change of Principal Investigator and modifications of the contents of the approval must be 
announced to the ethics commission in advance. 
Completion or cancellation of the research project must be announced to the ethics commission 
within 90 days. 
We plan no stopping rule before achieving the necessary patient number. If termination is due to 
external causes such as lack of funding, or ethical concerns results will be made available by 
publication of incomplete results, for use in meta-analyses. 
Significant changes to the protocol will be sent to the ethics commission for amendment. This 
includes changes in e.g. eligibility criteria, primary or secondary endpoints, or extent of data 
acquisition. 

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17. Data protection: coding and storage 
See below at 18. 
18. Procedure with non-coded data 
Data will be collected by specially trained research staff, and a) entered into a password-protected 
data environment, or b) automatically extracted from clinical records. Each patient will be 
attributed a study-specific patient identification number (PID). For statistical analysis, these 
datasets will be merged using the PID as identifier. 
At the end of the data acquisition, including follow-up, patient data will be coded using the PID, 
and the database will be locked. Coding using the PID will be done at the earliest time point after 
completion of follow-up data collection. 
Data generation, transmission, storage and analysis of health related personal data within this 
project will follow the current Swiss legal requirements for data protection and will be performed 
according to the Ordinance HRO Art. 5. 
Health related personal data captured during this project are strictly confidential and disclosure 
to third parties is prohibited; coding will safeguard participants' confidentiality. 
19. Information about storage 
Collected data will be entered into Redcap to allow appropriate storage with tracking of changes 
made to the data. Database and datasets will be stored on specially protected data folders 
accessible only for the research teams of the Cardiovascular Research Institute Basel and the 
respective sites, with regular back-ups in place. Printouts will be kept in a locked room at each 
site to protect patient confidentiality. 
The coding key, containing the PID and all data allowing patient identification (name, birthdate, 
address), will be stored on two different secured physical hard drives only accessible to the PIs, 
the study coordinator, or the local PIs. 
20. Duration of data storage 
Data will be stored for 10 years in the clinical archives and study specific data will be stored for 
10 years in a central archive at the Cardiovascular Research Institute in Basel. No biological 
material is collected. 
21. Ethical and regulatory requirements 
This project complies to the regulatory requirements of the Swiss human research law (HFG) and 
human research ordinance (HFV). Acceptance by the cantonal ethics commission is the 
necessary condition before the conduction of the research project. The research project will be 
carried out in accordance to the research plan and with principles enunciated in the current 
version of the Declaration of Helsinki58, the Essentials of Good Epidemiological Practice issued 
by Public Health Schweiz, the Swiss Law and Swiss regulatory authority’s requirements59 as 
applicable. 
22. Financing / Publication / Declaration of interest 
Funding and support 
This project will be supported by the University Hospital Basel and the University Basel. Further 
funding will be sought from the Swiss National Science Foundation, and the Swiss Heart 
Foundation. Further support will be sought from industry partners, e.g. diagnostic companies who 
supported the initial BASEL-PMI study (Roche Diagnostics, Abbott Diagnostics). 
 

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Publication strategy 
Publication of results is planned as:  
• Medical-scientific outreach: a) manuscripts submitted to peer-reviewed medical journals; b) 
oral and poster presentations at international and national meetings; c) presentation in 
hospital; d) key results in clinical trial databases 
• Public outreach: a) presentation to patient organisations; b) key results on Twitter 
(@CRIBasel); c) press release for key findings. 
 
Data sharing 
Data of the post-implementation phase will be merged with the prospective multicentre 
observational study BASEL-PMI to answer questions requiring large numbers of PMI, e.g. risk 
stratification and validation of scores as well as identifying subtypes of PMI. 
Data sharing with other research groups will be the responsibility of the PI. 
Data for local quality improvement programs will be made available to the local PIs as soon as 
possible following data base closure. 
 
Planned analysis in combination with BASEL-PMI dataset 
Outcome analysis (+BASEL-PMI) 
Due to organisational issues, we expect that a certain amount of patients in the post-
implementation period will not receive a cardiology consultation despite detection of PMI (seen 
during implementation at the University Hospital Basel). Therefore, we plan an exploratory 
analysis of patients with PMI receiving vs not-receiving cardiology consultation.  
Population: For this analysis we will exclude patients staying at the intensive care unit for more 
than the first postoperative night. 
Statistical analysis: We will then construct a multivariable model for occurrence of MACE including 
baseline characteristics (age, coronary artery disease, chronic heart failure, urgency of 
procedure) and whether patients received cardiology consultation or not. As a sensitivity analysis 
we will conduct a propensity score model using a 1:1 or 2:1 matching ratio (depending on 
incidence of not-receiving a cardiology consultation) and a caliper of 0.2 matching using nearest-
neighbour matching. 
 
Prediction of perioperative MACE including PMI (+BASEL-PMI) 
Aim: Generate a pre- and an immediate postoperative prediction score for occurrence of MACE 
including PMI.  
Statistical analysis: The cohort will be split into a derivation (2/3) and validation (1/3) set using a 
time dependent split of our cohort. Two different scores will be defined for 1) the preoperative and 
2) the immediate postoperative setting, utilizing all data available before and directly after surgery. 
To generate the scores, first, using the derivation set, potential predictive variables will be pre-
specified and included simultaneously into regression analysis. Second, significant variables will 
be added in descending order of significance until no significant improvement of the Akaike 
Information Criterion is reached by addition of another variable. Third, risk categories will be 
derived. Using the validation set, we will then a) calculate likelihood ratios for occurrence of death 
and MACE for each risk category, and b) calculate the diagnostic accuracy by area under the 
receiver operating characteristics curve (using bootstrap for estimation of 95%-CI) and compared 
to established perioperative risk scores by the method proposed by de Long60. 
 
Exploration of ECG-variables for prediction of perioperative MACE and PMI (+BASEL-PMI) 
Aim: Explore association of continuous ECG-markers with occurrence of MACE and PMI 
Population: For this analysis only patients with continuous ECG >3 minutes on the day of surgery 
(e.g. during anaesthesiology preparation) will be included. 
Statistical analysis: exploratory. 
 

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Risk scores for PMI-subtypes (+Basel-PMI) 
Aim: Derive and validate a risk score in patients with different subtypes of PMI for the occurrence 
of major cardiac adverse events to inform treatment decisions  
Population: For this analysis we will only include patients with detected PMI. 
Statistical analysis: We will derive the subtype-specific risk score using multivariable Cox 
proportional hazards analysis to identify predictive variables for the occurrence of death and 
MACE for the different subtypes of PMI. Candidate variables are selected prior to the analysis 
using literature analysis and included into the model via forced entry to avoid overfitting of the 
model. These will be used to generate a comprehensive risk-scoring algorithm using risk 
categories. Validation will be done by calculating the percentage of correctly classified patients. 
95% CI will be calculated using bootstrapping. Sensitivity analysis will be done by generating sets 
with different prevalence of subtypes and frequency of comorbidities from the validation cohort. 
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