Identifying Heart Failure in ECG Data With Artificial Intelligence—A Meta-Analysis - Grün, Dimitri; Rudolph, Felix; Gumpfer, Nils; Hannig, Jennifer; Elsner, Laura K.; von Jeinsen, Beatrice; Hamm, Christian W.; Rieth, Andreas; Guckert, Michael; Keller, Till (Frontiers in Digital Health, 2021)
Introduction: Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. Aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies. Methods and Results: An independent comprehensive search of the PubMed and GoogleScholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient level data and three with ECG based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG based datasets. Applying a random-effects model an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 (95% CI: 3.12- 3.76) to 13.61 (95% CI: 13.14-14.08) also with lower values in patient level datasets. The meta-analysis diagnostic odds ratio was 7.60 (95% CI: 5.85-9.34). Conclusions: The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient level data stipulate the need for robust prospective studies.