Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.

Autor: Cai Y; China Medical University, Shenyang, 110122, China., Cai YQ; China Medical University, Shenyang, 110122, China., Tang LY; China Medical University, Shenyang, 110122, China., Wang YH; China Medical University, Shenyang, 110122, China., Gong M; Digital Health China Co. Ltd, Beijing, 100089, China., Jing TC; Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China., Li HJ; Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China.; Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China., Li-Ling J; Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China., Hu W; Bayi Orthopedic Hospital, Chengdu, 610017, China., Yin Z; Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China. zhyin@cmu.edu.cn., Gong DX; Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China. gongdx@cmu1h.com.; The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China. gongdx@cmu1h.com., Zhang GW; Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China. gwzhang@cmu.edu.cn.; The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China. gwzhang@cmu.edu.cn.
Jazyk: angličtina
Zdroj: BMC medicine [BMC Med] 2024 Feb 05; Vol. 22 (1), pp. 56. Date of Electronic Publication: 2024 Feb 05.
DOI: 10.1186/s12916-024-03273-7
Abstrakt: Background: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.
Methods: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).
Results: In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively.
Conclusion: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
(© 2024. The Author(s).)
Databáze: MEDLINE
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