Abstract
AbstractBackgroundThe main risk stratification tools for identifying high-risk individuals of cardiovascular disease (CVD) are based on Western populations. Few models are developed specifically for Asian populations and are not enhanced by artificial intelligence (AI). The aim of this study is to develop the first AI-powered quantitative predictive tool for CVD (PowerAI-CVD) incorporate physiological blood pressure measurements, existing diseases and medications, and laboratory tests from Chinese patients.MethodsThe study analysed patients who attended family medicine clinics between 1stJanuary 2000 and 31stDecember 2003. The primary outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, transient ischaemic attack (TIA)/stroke or cardiovascular mortality, with follow-up until 31stDecember 2019. The performance of AI-driven models (CatBoost, XGBoost, Gradient Boosting, Multilayer Perceptron, Random Forest, Naïve Bayes, Decision Tree, k-Nearest Neighbor, AdaBoost, SVM-Sigmod) for predicting MACE was compared. Predicted probability (ranging between 0 and 1) of the best model (CatBoost) was used as the baselinein-silicomarker to predict future MACE events during follow-up.ResultsA total of 154,569 patients were included. Over a median follow-up of 16.1 (11.6-17.8) years, 31,061 (20.44%) suffered from MACE (annualised risk: 1.28%). The machine learningin-silicomarker captured MACE risk from established risk variables (sex, age, mean systolic and diastolic blood pressure, existing cardiovascular diseases, medications (anticoagulants, antiplatelets, antihypertensive drugs, and statins) and laboratory tests (NLR, creatinine, ALP, AST, ALT, HbA1c, fasting glucose, triglyceride, LDL and HDL)). MACE incidences increased quantitatively with ascending quartiles of thein-silicomarker. The CatBoost model showed the best performance with an area under the receiver operating characteristic curve of 0.869. The CatBoost model basedin-silicomarker shows significant prediction strength for future MACE events, across subgroups (age, sex, prior MACE, etc) and different follow-up durations.ConclusionsThe AI-powered risk prediction tool can accurately forecast incident CVD events, allowing personalised risk prediction at the individual level. A dashboard for predictive analytics was developed, allowing real-time dynamic updates of risk estimates from new data. It can be easily incorporated into routine clinical use to aid clinicians and healthcare administrators to identify high-risk patients.Graphical Abstract
Publisher
Cold Spring Harbor Laboratory