An artificial intelligence–enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the ‘Turing test’?

Objective: To develop an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm capable of comprehensive, human-like ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods. Methods: We developed a novel AI-enabled ECG (AI-ECG) algorithm capable of complete 12-lead ECG interpretation. It was trained on nearly 2.5 million standard 12-lead ECGs from over 720,000 adult patients obtained at the Mayo Clinic ECG laboratory between 2007 and 2017. We then compared the need for human over-reading edits of the reports generated by the Marquette 12SL automated computer program, AI-ECG algorithm, and final clinical interpretations on 500 randomly selected ECGs from 500 patients. In a blinded fashion, 3 cardiac electrophysiologists adjudicated each interpretation as (1) ideal (ie, no changes needed), (2) acceptable (ie, minor edits needed), or (3) unacceptable (ie, major edits needed). Results: Cardiologists determined that on average 202 (13.5%), 123 (8.2%), and 90 (6.0%) of the interpretations required major edits from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 958 (63.9%), 1058 (70.5%), and 1118 (74.5%) interpretations as ideal from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 340 (22.7%), 319 (21.3%), and 292 (19.5%) interpretations as acceptable from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. Conclusion: An AI-ECG algorithm outperforms an existing standard automated computer program and better approximates expert over-read for comprehensive 12-lead ECG interpretation

Invasive electrophysiological testing to predict and guide permanent pacemaker implantation after transcatheter aortic valve implantation: A meta-analysis

Background: Atrioventricular conduction abnormalities after transcatheter aortic valve implantation (TAVI) are common. The value of electrophysiological study (EPS) for risk stratification of high-grade atrioventricular block (HG-AVB) and guidance of permanent pacemaker (PPM) implantation is poorly defined. Objective: The purpose of this study was to identify EPS parameters associated with HG-AVB and determine the value of EPS-guided PPM implantation after TAVI. Methods: We performed a systematic review and meta-analysis of studies investigating the value of EPS parameters for risk stratification of TAVI-related HG-AVB and for guidance of PPM implantation among patients with equivocal PPM indications after TAVI. Results: Eighteen studies (1230 patients) were eligible. In 7 studies, EPS was performed only after TAVI, whereas in 11 studies EPS was performed both before and after TAVI. Overall PPM implantation rate for HG-AVB was 16%. AV conduction intervals prolonged after TAVI, with the AH and HV intervals showing the largest magnitude of changes. Pre-TAVI HV >70 ms and the absolute value of the post-TAVI HV interval were associated with subsequent HG-AVB and PPM implantation with odds ratios of 2.53 (95% confidence interval [CI] 1.11–5.81; P = .04) and 1.10 (95% CI 1.03–1.17; P = .02; per 1-ms increase), respectively. In 10 studies, PPM was also implanted due to abnormal EPS findings in patients with equivocal PPM indications post-TAVI (typically new left bundle branch block or transient HG-AVB). Among them, the rate of long-term PPM dependency was 57%. Conclusion: Selective EPS testing may assist in the risk stratification of post-TAVI HG-AVB and in the guidance of PPM implantation, especially in patients with equivocal PPM indications post-TAVI

Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging owing to the low disease prevalence and potentially high false-positive rates. Objective: Identify clinical characteristics associated with true- and false-positive HCM AI-ECG results to improve its clinical application. Methods: We reviewed the records of the 200 patients with highest HCM AI-ECG scores in January 2021 at our institution. Logistic regression was used to create a clinical variable–based “Candidacy for HCM Detection (HCM-DETECT)” score, differentiating true-positive from false-positive AI-ECG results. We validated the HCM-DETECT score in an independent cohort of 200 patients with the highest AI-ECG scores from January 2022. Results: In the 2021 cohort (median age 71 [interquartile range 58–80] years, 48% female), the rates of true-positive, false-positive, and indeterminate AI-ECG results for HCM detection were 36%, 48%, and 16%, respectively. In the 2022 cohort, the rates were 26%, 47%, and 27%, respectively. The HCM-DETECT score included age, coronary artery disease, prior pacemaker, and prior cardiac valve surgery, and had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.73–0.87) for differentiating true- vs false-positive AI results. When the 2022 cohort was limited to HCM detection candidates identified with the HCM-DETECT score, the false-positive AI-ECG rate was reduced from 47% to 13.5%. Conclusion: Application of a clinical score (HCM-DETECT) in tandem with an AI-ECG model improved HCM detection yield, reducing the false-positive rate of AI-ECG more than 3-fold