Multimorbidity profile and the risk of major cardiovascular events in new antipsychotic users: a time-to-event prediction study using explainable machine learning
Authors: Sun Qi , Liu Wenlong , Wei Cuiling , Hu Yuqi , Zhou Lingyue , Liu Boyan , Chu Rachel Yui Ki , Song Song , Tian Wenxin , Chan Esther Wai Yin , Chan Sherry Kit Wa , Tsoi Kelvin Kam Fai , Wong Ian Chi Kei , Osborn David P.J. , Smith Daniel , Lai Francisco Tsz Tsun 
Published: November 2024

Introduction:
Antipsychotic users are at an elevated risk of major adverse cardiovascular events (MACE) due to many interacting risk factors. However, specific antipsychotic agents, underlying multimorbidity, and chronic medication patterns in relation to MACE are little explored.
Aims:
To identify patients’ characteristics with increased risk of MACE in people with multimorbidity and using antipsychotics and to develop and evaluate a time-to-event prediction model.
Methods:
This retrospective cohort study utilized electronic health records from public healthcare facilities in Hong Kong. We included MACE-free patients aged 18-65 years with records of two or more chronic health conditions within three years prior to their first antipsychotic use. Baseline characteristics, such as age, sex, chronic disease history, antipsychotic usage history, and drug intake history over the previous year, were considered. The outcome was major adverse cardiovascular events (MACE), which included stroke, acute myocardial infarction (AMI), and cardiovascular-related death (CV death). The dataset was randomly divided into training and validation subsets in a 7:3 ratio based on the initial year of antipsychotic prescription. A Conditional Inference Survival Tree (CISTree) was employed to identify MACE risk groups. Ten machine learning models were trained using 5-fold cross-validation for hyperparameter optimization and validated on the validation set. We conducted time-dependent ROC curve analysis, calibration plots, and decision curve analysis plots to compare the models' discrimination capacity, calibration, and clinical application value, respectively. Time-dependent variable importance, partial dependence plots, and SHAP plots were used to explain the selected model.
Results:
A total of 26,274 patients were included in the study. The CISTree model identified older patients (>48 years) with chronic kidney disease (CKD), who were using antibacterial and antiplatelet drugs but not taking antidepressants, and without metastatic cancer, as having the highest MACE incidence rate (171.317 per 1,000 person-years; 95% CI: [130.088, 221.467]). The random survival model outperformed the other nine models, identifying age, antidepressant usage, and CKD as the top three significant predictors, consistent with the CISTree model. The survival C-statistics (ranging from 0 to 1, with higher values indicating better predictive precision) for 1-, 3-, and 5-year MACE predictions in the validation cohort were estimated at 0.841, 0.835, and 0.824, respectively.
Conclusion:
We identified specific high-risk MACE groups among individuals with multimorbidity who started using antipsychotics. Predictions based on these features demonstrated excellent accuracy and have the potential to aid clinical decision-making.