Acute cardiac care & resuscitationCase-control study
Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest
- case-control study design (matching on age and sex), to derive and temporally validate models to predict OHCA
- temporal validation
- real-world cohort of individuals undergoing electrocardiography (ECG)
Authors' conclusion
In a large U.S. health care system, artificial intelligence-enhanced ECG and EHR data effectively discriminated individuals at risk of OHCA and identified those at clinically relevant risk of incident OHCA over a 2-year period.
Study context
- Population
- General population; individuals undergoing ECG in a health care system
- Comparison
- Discrimination of OHCA by the multimodal ECG + EHR model, the EHR-only model and the ECG-only model
Main finding
As reported in the abstract
Discrimination of OHCA, temporal validation cohort
| Group | area under the receiver operating characteristic curve | area under the precision recall curve |
|---|---|---|
| ECG + EHR model | 0.83 | 0.44 |
- Highest for ECG + EHR model, followed by the EHR-only model and the ECG-only model
- Bonferroni adjusted P for all pairwise comparisons <0.05
Observational data: associations, not effects.
Further findings
| Outcome | high-risk | low risk |
|---|---|---|
| 2-year cumulative incidence of OHCA by ECG + EHR model risk group · accounting for the competing risk of non-OHCA mortality | 2.4% (95% CI 2.0%–2.8%) | 0.5% (95% CI 0.3%–0.8%) |
Incident OHCA flagged as high-risk by the EHR + ECG model, real-world cohort · 2-year period
- of those with incident OHCA flagged as high-risk
- two-thirds (153 of 228)
Original publication
Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest. JACC Adv. Published 11 May 2026. doi:10.1016/j.jacadv.2026.102787
Summarised from the published abstract; every number is checked against it