Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Abstract

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.

Fig. 1. Consort diagram.

This diagram shows study participant flow in the screening for peripartum cardiomyopathies in Nigeria study (SPEC-AI Nigeria).

Fig. 2. Summary of study design.

This figure summarizes the study design, interventions, study-related visits and key study procedures. Participants could enter the study at any time point during pregnancy or postpartum (up to 12 months). As such, each individual participant could have up to seven visits if they enter the study in the first trimester of pregnancy and fewer depending on the time of study entry. AI-based screening was performed up to seven times during the study period, including during each trimester of pregnancy (first trimester, <14 weeks; second trimester, 14 to <28 weeks; and third trimester, 28 to <42 weeks and post-term) (up to three ECGs), between delivery and 6 weeks, between 6 weeks and 3 months, between 3 and 5 months, and between 5 and 12 months postpartum (up to four ECGs). Only participants in the intervention arm had a baseline echocardiogram as well as a simultaneous portable ECG recorded at each time point. AI-based prediction for LVSD using the digital stethoscope was available in real time at the point of care and 12-lead AI-ECG predictions for LVSD were provided asynchronously, usually within 1 week of ECG acquisition. *, visit 1 can vary for each participant depending on the time point at study entry in relation to delivery.

Fig. 3. Forest plots showing primary outcome stratified by subgroups.

a, Performance of the digital stethoscope. b, Performance of the US FDA-cleared 12-lead AI-ECG algorithm. c, Performance of the original Mayo Clinic 12-lead AI-ECG algorithm in detecting the primary outcome within each prespecified subgroup. Data in the columns are presented as frequencies and percentages with 95% exact CI in parenthesis. The column with error bars represents odds ratio estimates depicted as a black dot and the error bar represents the large sample 95% CI around the odds ratio estimate. The odds ratios and 95% large sample CI were estimated using logistic regression. HDP, hypertensive disorder of pregnancy (includes chronic hypertension, gestational hypertension, pre-eclampsia and eclampsia).