Evidence map›Paper›PMID 41102258›Full record

ArticleNPJ digital medicine2025

External validation of PreOpNet to predict 30-day mortality after major non-cardiac surgery using digital electrocardiogram.

Arnaud Champetier, Pedro Lopez-Ayala, Christian Puelacher, Koray Durak, Emel Kaplan, Danielle Gualandro, Noemi Glarner, Mirjam Pargger, Gabrielle Huré, Katrin Burri-Winkler and 11 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02573532 (Basel Incidence, Patient Characteristics, Outcome and Possible Strategies to Improve Outcome of Perioperative Myocardial Injury After Non-cardiac Surgery), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02573532 active not recruitingnot on this map

Basel Incidence, Patient Characteristics, Outcome and Possible Strategies to Improve Outcome of Perioperative Myocardial Injury After Non-cardiac Surgery: 1-Year Follow-up

TypeobservationalSponsorUniversity Hospital, Basel, SwitzerlandRan2015 to 2027Enrolled19,645ConditionsPerioperative Myocardial Injury
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Arnaud Champetier *Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Pedro Lopez-Ayala *Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Christian PuelacherCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Koray DurakCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Emel KaplanCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Danielle GualandroCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Noemi GlarnerCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Mirjam ParggerCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Gabrielle HuréCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Katrin Burri-WinklerCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Daniel BolligerDepartment of Anesthesiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Luzius A SteinerDepartment of Anesthesiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Stefan SchärenDepartment of Spinal Surgery, University Hospital Basel, Basel, Switzerland.
Edin MujagicDepartment of Vascular Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
Martin ClaussDepartment for Orthopedics and Trauma Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
Andreas M MuellerDepartment for Orthopedics and Trauma Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
Didier LardinoisDepartment of Thoracic Surgery, University Hospital Basel, University of Basel, Basel, Switzerland.
Jasper BoeddinghausCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Felix MahfoudCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Ivo StrebelCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland. ivo.strebel@usb.ch.
Christian MuellerCardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland. christian.mueller@usb.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PreOpNet is a novel deep-learning algorithm using 12-lead digital electrocardiogram (ECG) for preoperative risk assessment of all-cause death and major adverse cardiac events (MACE) within 30 days. Its performance in European high-risk patients undergoing major non-cardiac surgery-the target population for guideline-recommended risk assessment-and comparison to high-sensitivity cardiac troponin T (hs-cTnT), is unknown. In a prospective European study (2014-2019), 6098 high-risk patients with available ECGs were enrolled. PreOpNet showed moderate discrimination for death (AUC 0.707) and MACE (0.675), but overestimated risk. It outperformed the revised cardiac risk index (RCRI) for death (AUC 0.644), but not for MACE (0.662). Hs-cTnT remained superior for both outcomes (AUC 0.762 and 0.743). Importantly, PreOpNet provided incremental prognostic value when combined with RCRI and/or hs-cTnT. PreOpNet has limited benefit for preoperative risk stratification in high-risk surgical patients as a stand-alone test. However, it holds promise when used in conjunction with RCRI and hs-cTnT. Clinical Trial Registration: ClinicalTrials.gov number: NCT02573532; https://www.clinicaltrials.gov/study/NCT02573532 .

Identifiers

PMID41102258
PMCPMC12533097

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.