Artificial intelligence–enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial

Abstract Background Secondary prevention of coronary heart disease (CHD) remains suboptimal due to fragmented care and therapeutic inertia. While digital health interventions offer a promising strategy, few existing tools provide comprehensive, closed-loop management of multidimensional risk factors without increasing clinician workload. This study aimed to evaluate the efficacy of an artificial intelligence (AI)–enhanced management system (AIM-CHD) in improving multidimensional risk factor control among patients with CHD. Methods This single-center, open-label, parallel-group, randomized clinical trial was conducted from November 2024 to June 2025 in China. A total of 1100 adults with confirmed CHD were randomized 1:1 to receive either the AIM-CHD intervention ( n = 549) or usual care ( n = 551) for 3 months. The AIM-CHD system featured automated multi-source data capture, guideline-directed risk stratification, and closed-loop feedback via smartphone. The primary outcome was the low-density lipoprotein cholesterol (LDL-C) level at 3 months. Secondary outcomes included goal attainment rates for LDL-C (< 70 mg/dL) and blood pressure (BP, < 130/80 mm Hg), glycated hemoglobin, smoking cessation, body mass index (BMI), medication adherence, and a composite cardiovascular endpoint at 3 months. Analysis was performed on an intention-to-treat (ITT) basis. Results Of 1100 randomized participants (mean age 61 years; 75.3% male), 943 (85.7%) completed the 3-month follow-up. The intervention group achieved a significantly lower mean LDL-C level compared with the control group (60.4 ± 23.4 vs. 63.7 ± 26.0 mg/dL), with an adjusted mean difference of − 3.2 mg/dL (95% CI, − 6.2 to − 0.3; p = 0.03). Furthermore, participants in the intervention group were more likely to achieve the LDL-C target (71% vs. 64%; RR, 1.10; 95% CI, 1.01–1.20; p = 0.03) and the BP target (45% vs. 35%; RR, 1.27; 95% CI, 1.09–1.48; p = 0.002). No significant differences were observed for HbA1c, BMI, or medication adherence. Conclusions The AIM-CHD system significantly improved short-term lipid and BP control compared with usual care. These findings support interoperable, low-burden digital management systems as a scalable strategy in routine secondary prevention. Trial registration ClinicalTrials.gov Identifier: NCT06686056. Registered on 11 November 2024.

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Publication Details

Journal
BMC Medicine
Published
2026-08-18
DOI
https://doi.org/10.1186/s12916-026-05135-w
Primary Topic
Artificial Intelligence in Healthcare and Education
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article
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article

Artificial intelligence–enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial

Zhengqing Ba, Zuoxiang wang, Guangzhi Chen, Xiaojin Gao et al.
BMC Medicine
Artificial Intelligence in Healthcare and Education
article

Artificial intelligence–enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial

Zhengqing Ba, Zuoxiang wang, Guangzhi Chen, Xiaojin Gao, Jing Yuan, Xiaodan Lian, Mengyuan Liu, Lanshu Yang, Fei Yu, Yongjian Wu, Ximei Wang, Yang Wang, Yajing Su, Xue Zhang, Wei Zhao, Sheng Zhao
article en

Abstract

Abstract Background Secondary prevention of coronary heart disease (CHD) remains suboptimal due to fragmented care and therapeutic inertia. While digital health interventions offer a promising strategy, few existing tools provide comprehensive, closed-loop management of multidimensional risk factors without increasing clinician workload. This study aimed to evaluate the efficacy of an artificial intelligence (AI)–enhanced management system (AIM-CHD) in improving multidimensional risk factor control among patients with CHD. Methods This single-center, open-label, parallel-group, randomized clinical trial was conducted from November 2024 to June 2025 in China. A total of 1100 adults with confirmed CHD were randomized 1:1 to receive either the AIM-CHD intervention ( n = 549) or usual care ( n = 551) for 3 months. The AIM-CHD system featured automated multi-source data capture, guideline-directed risk stratification, and closed-loop feedback via smartphone. The primary outcome was the low-density lipoprotein cholesterol (LDL-C) level at 3 months. Secondary outcomes included goal attainment rates for LDL-C (< 70 mg/dL) and blood pressure (BP, < 130/80 mm Hg), glycated hemoglobin, smoking cessation, body mass index (BMI), medication adherence, and a composite cardiovascular endpoint at 3 months. Analysis was performed on an intention-to-treat (ITT) basis. Results Of 1100 randomized participants (mean age 61 years; 75.3% male), 943 (85.7%) completed the 3-month follow-up. The intervention group achieved a significantly lower mean LDL-C level compared with the control group (60.4 ± 23.4 vs. 63.7 ± 26.0 mg/dL), with an adjusted mean difference of − 3.2 mg/dL (95% CI, − 6.2 to − 0.3; p = 0.03). Furthermore, participants in the intervention group were more likely to achieve the LDL-C target (71% vs. 64%; RR, 1.10; 95% CI, 1.01–1.20; p = 0.03) and the BP target (45% vs. 35%; RR, 1.27; 95% CI, 1.09–1.48; p = 0.002). No significant differences were observed for HbA1c, BMI, or medication adherence. Conclusions The AIM-CHD system significantly improved short-term lipid and BP control compared with usual care. These findings support interoperable, low-burden digital management systems as a scalable strategy in routine secondary prevention. Trial registration ClinicalTrials.gov Identifier: NCT06686056. Registered on 11 November 2024.

BMC MedicineVol. 24(1)
Good health and well-being
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
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