Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest

Out-of-hospital cardiac arrest (OHCA) is a public health burden with the majority occurring in the general population for whom there is no firm strategy to predict risk.

The authors evaluated whether artificial intelligence enhanced electrocardiography (ECG) and clinical information from electronic health records (EHRs) can stratify risk of OHCA in the general population.

We use a case-control study design (matching on age and sex), to derive and temporally validate models to predict OHCA. To evaluate the potential use case of models in a real-world context, we evaluated the 2-year cumulative incidence of OHCA in individuals undergoing ECG in a health care system, while accounting for the competing risk of non-OHCA mortality.

In the temporal validation cohort, discrimination of OHCA was highest for the multimodal ECG + EHR model (area under the receiver operating characteristic curve: 0.83; area under the precision recall curve: 0.44) followed by the EHR-only model and the ECG-only model (Bonferroni adjusted P for all pairwise comparisons <0.05). In the real-world cohort of individuals undergoing ECG, the EHR + ECG model flagged two-thirds (153 of 228) of those with incident OHCA over a 2-year period as high-risk. Using the ECG + EHR model, the 2-year cumulative incidence of OHCA was 2.4% (95% CI: 2.0%-2.8%) in individuals identified as high-risk compared with 0.5% (95% CI: 0.3%-0.8%) in individuals designated as low risk.

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.