Patient-specific multifactorial mortality risk assessment using classification and regression tree analysis in the context of ambulatory blood pressure monitoring

Abstract

Background: Ambulatory blood pressure monitoring is essential for understanding blood pressure patterns beyond clinical visits, aiding in risk assessment, treatment evaluation, and managing hypertension. This retrospective cohort study aimed to identify risk factors for all-cause mortality and major cardiovascular events in patients who underwent ambulatory blood pressure monitoring. Methodology: Eligible participants aged 18 or older, with an estimated glomerular filtration rate (eGFR) > 60 ml/min/1.73 m(2), who underwent ambulatory blood pressure monitoring for various reasons, were included in the study. Data were gathered through telephone interviews, electronic health records, and the national health record system. Descriptive analysis and classification and regression tree modeling were used to uncover significant risk factors related to all-cause mortality and cardiovascular events, and to assess the model's performance compared to traditional Cox survival analysis. Results: The study included 1291 patients, primarily male (51.8%) with a mean age of 61.1 +/- 15.2 years. During a mean follow-up of 46.9 months, 76 (5.9%) patients died of any cause, and 195 (15.1%) had a cardiovascular event. The highest survival rates were observed in patients with a diastolic blood pressure (BP) dipping percentage between - 2% and 29%, nighttime systolic BP variability below 32 mmHg, and age below 72. Conversely, smokers with a diastolic BP dipping percentage below - 10% showed the lowest survival rates. The best cardiovascular outcomes were observed in patients with diastolic BP dipping above - 11%, nighttime mean systolic BP < 144 mmHg, no statin use, normotensive status, and daytime mean heart rate >= 60 bpm. Conversely, the worst outcomes were seen in patients with diastolic BP dipping below - 11% and a morning surge >= 14 mmHg. In all-cause mortality and cardiovascular event analysis, the combined model demonstrated excellent calibration and predictive power, like the classification and regression tree model and traditional analysis. Conclusion: These findings highlight the potential of a combined model for assessing mortality and cardiovascular event risk in patients who have undergone ambulatory blood pressure monitoring.