Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
July 2, 2026European Heart JournalOpen Access

Predicting mortality in cardiovascular patients using electrocardiogram data and artificial intelligence

View Full Paper
Ask AI
Bookmark
Share

Why the study?

Does an AI model using a single 12-lead ECG predict long-term mortality comparably to the ESC-SCORE in patients with suspected chronic coronary syndrome?

Population

n=720 patients scheduled for invasive coronary angiography due to suspected chronic coronary syndrome, with…

Comparison

Deep learning artificial intelligence model… vs ESC-SCORE (weighed for a German population).

Design

Cohort

Follow-up

long-term

Key result

An AI model using a single 12-lead ECG predicted long-term mortality with an AUROC of 0.606 (vs 0.584 for ESC-SCORE) in primary prevention and 0.612 (vs 0.658) in secondary prevention.

Authors

SWS WegenerDGD GruenJPJoshua Prim

Discussion

Loading...

Member takes

Overview

AI-ECG modestly outperforms ESC-SCORE for mortality in primary prevention but underperforms in secondary; extends ECG-AI risk tools while needing validation.

Key Points

  • The aim is to predict mortality in cardiovascular patients using artificial intelligence and ECG data.
  • Analyzed data from 720 patients enrolled in a registry for coronary angiography.
  • Utilized a deep learning model previously trained on the PTB-XL dataset to predict mortality from ECG recordings.
  • Compared AI model performance to traditional risk scoring systems like ESC-SCORE.
  • For patients without CAD, the AI model achieved an AUROC of 0.606, compared to the ESC-SCORE at 0.584.
  • For patients with CAD, the AI model achieved an AUROC of 0.612, compared to the ESC-SCORE at 0.658.

Study Design

Type

Cohort (n=720)

Structured PICO

Does an AI model using a single 12-lead ECG predict long-term mortality comparably to the ESC-SCORE in patients with suspected chronic coronary syndrome?

P
Population
720 patients scheduled for invasive coronary angiography for suspected chronic coronary syndrome, with available ECG and ESC-SCORE variables, followed for long-term mortality.
E
Exposure
Deep learning artificial intelligence model applied to a single 12-lead ECG obtained at admission.
C
Comparator
ESC-SCORE (weighed for a German population).
O
Outcome
Long-term overall mortality.hard clinical

Main Result

Effect estimate: AUROC 0.606 vs 0.584 (primary prevention); 0.612 vs 0.658 (secondary prevention)

Cite This Study

Wegener et al. (2021) conducted a cohort in Suspected chronic coronary syndrome (n=720). AI model based on a single 12-lead ECG vs. ESC-SCORE was evaluated on Long-term mortality (AUROC 0.606 vs 0.584 (primary prevention); 0.612 vs 0.658 (secondary prevention)). An AI model using a single 12-lead ECG predicted long-term mortality with an AUROC of 0.606 (vs 0.584 for ESC-SCORE) in primary prevention and 0.612 (vs 0.658) in secondary prevention.

synapsesocial.com/papers/6a468172aa56e3314088d014https://doi.org/10.1093/eurheartj/ehab724.1132
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients2025
  2. 2Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement2026
  3. 3Artificial intelligence in electrocardiogram-based prediction of heart failure: a systematic review and meta-analysis2026 · 4 citations
  4. 4Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.2025
  5. 5Artificial Intelligence in Predicting Sudden Cardiac Death2025