Development and validation of 10-year risk prediction models of cardiovascular disease in Chinese type 2 diabetes mellitus patients in primary care using interpretable machine learning-based methods.
Weinan DongEric Yuk Fai WanDaniel Yee Tak FongKathryn Choon-Beng TanWendy Wing-Sze TsuiEric Ming-Tung HuiKing Hong ChanColman Siu Cheung FungCindy Lo Kuen LamPublished in: Diabetes, obesity & metabolism (2024)
Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.
Keyphrases
- primary care
- end stage renal disease
- machine learning
- cardiovascular disease
- blood pressure
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- decision making
- type diabetes
- prognostic factors
- deep learning
- adipose tissue
- climate change
- skeletal muscle
- cardiovascular risk factors
- patient reported
- insulin resistance
- human health