Protective Effect of Renin–Angiotensin–Aldosterone System Inhibitors Guided by Artificial Intelligence–Enabled Electrocardiograms on Incident Left Ventricular Dysfunction: A Multicenter Cohort Study
Abstract Body (Do not enter title and authors here): Background: Artificial intelligence–enabled electrocardiogram (AI-ECG) models can accurately detect left ventricular dysfunction (LVD), and their misclassification in patients with normal LV function may predict incident LVD. However, the prognostic value of AI-ECG and its role in guiding heart failure therapies, including angiotensin-converting enzyme inhibitors (ACEi) or angiotensin II receptor blockers (ARBs), remain insufficiently investigated. Methods: This retrospective study included adults with baseline LVEF ≥ 50% on echocardiography who underwent ECG evaluation in a multicenter database of 10 hospitals from January 2016 to December 2024. A previously developed ECG-based algorithm stratified patients into high- and low-risk LVD groups. The primary outcome was incident LVEF ≤ 40% on follow-up echocardiograms. Propensity score matching and Cox proportional hazards models were used to estimate treatment effects, with follow-up through December 2024. Results: After propensity score matching, 7,169 patients were analyzed (mean age 68 years; 55% male), including 1,683 low-risk and 880 high-risk patients who received ACEi or ARB therapy, and 3,041 low-risk and 1,565 high-risk patients who did not. Over a median follow-up of 1,320 days, the high-risk group had a significantly greater risk of incident LVEF ≤ 40% (hazard ratio [HR], 3.68; 95% confidence interval [CI]: 3.02–4.49). In the high-risk group, ACEi or ARB therapy was associated with a lower risk of LVEF decline compared to no treatment (HR, 0.70; 95% CI, 0.54–0.90); this effect was not observed in the low-risk group (HR, 1.10; 95% CI, 0.80–1.52; p for interaction = 0.028; Figure 1). Subgroup analyses confirmed the consistency of this finding across demographic groups, clinical settings, and comorbidities, and after adjusting for the competing risk of death. Conclusion: AI-ECG effectively identified individuals at high risk for incident LVEF decline, and preemptive ACEi or ARB therapy was associated with a reduced risk of LVEF decline in this group. Further investigation is warranted to establish a causal relationship between ACEi/ARB therapy and AI-ECG–based risk stratification.
Wang Yunfeng, Xu Wei, Song Lijuan, Wang Chunqi, Wu Yi, Krumholz Harlan, Li Xi, Hu Shengshou, Guo Weihong, Hao Yang, Zheng Xin, Zhang Haibo, Yang Yang, Chen Bowang, Zhang Xiaoyan, Cui Jianlan