An AI model using a single-lead sinus rhythm ECG and six risk factors detected concurrent atrial fibrillation with an AUC of 0.76 (95% CI 0.74-0.79), matching the performance of a 12-lead model.
Case-Control (n=67,395)
Does an AI model using a single-lead ECG and clinical risk factors accurately detect concurrent atrial fibrillation during sinus rhythm?
An AI model combining a 10-second single-lead ECG in sinus rhythm with six basic clinical risk factors can detect concurrent atrial fibrillation as accurately as a 12-lead ECG model, potentially improving opportunistic screening yields.
Effect estimate: AUC 0.76 (95% CI 0.74-0.79)
Absolute Event Rate: 0.76% vs 0.74%
AIMS: Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF. METHODS AND RESULTS: This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex. CONCLUSION: An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.
Dupulthys et al. (Sun,) conducted a case-control in Atrial fibrillation (n=67,395). Single-lead ECG Artificial Intelligence model with six risk factors vs. Single-lead ECG AI model without risk factors was evaluated on Prediction of atrial fibrillation (AUC 0.76, 95% CI 0.74-0.79). An AI model using a single-lead sinus rhythm ECG and six risk factors detected concurrent atrial fibrillation with an AUC of 0.76 (95% CI 0.74-0.79), matching the performance of a 12-lead model.