Evidence map›Paper›PMID 40967934›Full record

Observational studyBritish journal of anaesthesia2025

Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach.

Carl Harris, Anway Pimpalkar, Ataes Aggarwal, Jiyuan Yang, Xiaojian Chen, Samuel Schmidgall, Sampath Rapuri, Joseph L Greenstein, Casey O Taylor, Robert D Stevens

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in British journal of anaesthesia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
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

10 authors.

Carl HarrisDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA; Laboratory of Computational Intensive Care Medicine, School of Medicine, Baltimore, MD, USA.
Anway PimpalkarDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA.
Ataes AggarwalDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA.
Jiyuan YangDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA.
Xiaojian ChenDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA.
Samuel SchmidgallDepartment of Electrical and Computer Engineering, Whiting School of Engineering, Baltimore, MD, USA.
Sampath RapuriDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA; Laboratory of Computational Intensive Care Medicine, School of Medicine, Baltimore, MD, USA.
Joseph L GreensteinDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA; Institute for Computational Medicine, Whiting School of Engineering, Baltimore, MD, USA.
Casey O TaylorDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA; Institute for Computational Medicine, Whiting School of Engineering, Baltimore, MD, USA; Department of Medicine, Division of General Internal Medicine, School of Medicine, Baltimore, MD, USA.
Robert D StevensDepartment of Biomedical Engineering, Whiting School of Engineering, Baltimore, MD, USA; Laboratory of Computational Intensive Care Medicine, School of Medicine, Baltimore, MD, USA; Institute for Computational Medicine, Whiting School of Engineering, Baltimore, MD, USA; Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA. Electronic address: rsteven1@jh.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Revised Cardiac Risk Index (RCRI) only modestly predicts major adverse cardiovascular events after noncardiac surgery. We investigated whether preoperative 12-lead ECGs analysed with deep learning could improve risk prediction.

methodsIn a retrospective cohort of 37 081 adults undergoing major noncardiac surgery (2008-2019, MIMIC-IV database), convolutional neural networks were trained to predict in-hospital myocardial infarction, in-hospital mortality, and a composite of in-hospital myocardial infarction, in-hospital stroke, and 30-day mortality. Models using ECG waveforms alone were compared with fusion models that combined ECGs with 34 routinely collected clinical variables. The primary outcome was discrimination, assessed by the area under the receiver-operating characteristic curve (AUROC) with 10-fold cross-validation and permutation tests vs the RCRI. A generative counterfactual framework provided waveform-level explanations.

resultsThe fusion model yielded an AUROC=0.858 (95% confidence interval [95% CI], 0.845-0.872) for myocardial infarction, AUROC=0.899 (95% CI, 0.889-0.908) for in-hospital mortality, and AUROC=0.835 (95% CI, 0.827-0.843) for the composite outcome. Fusion model AUROC values exceeded those of ECG-only models (P≤0.002) and the RCRI (myocardial infarction: P=0.001; composite: P<0.001). Counterfactual analysis highlighted prolonged QRS duration, low-voltage complexes, and ST-segment depression as electrophysiologic patterns that consistently correlated with higher predicted risk.

conclusionsA multimodal deep-learning model that integrates preoperative ECG waveforms with routinely collected clinical data improves prediction of major adverse cardiovascular events, compared with the Revised Cardiac Risk Index. This fully automated approach provides explainable, patient-specific insights, and may improve perioperative risk stratification.

Indexed as

Cardiovascular DiseasesDeep LearningElectrocardiographyPostoperative ComplicationsPreoperative CareSurgical Procedures, OperativeAgedArea Under CurveConvolutional Neural NetworksDatabases, FactualFemaleHospital MortalityHumansMaleMiddle AgedMyocardial Infarctioncounterfactual explanationsdeep learningelectrocardiogramperioperative outcomesprognostic modellingrisk stratification

Identifiers

PMID40967934
PMCPMC12597439

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.