Abstract
Introduction The present study evaluated the application of incorporating non-linear J/U-shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non-AMI-related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus. Methods This was a territory-wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti-diabetic agents between January 1st, 2009 to December 31st, 2009 at government-funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non-AMI-related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model. Results This study included 261 308 patients (age = 66.0 +/- 11.8 years old, male = 47.6%, follow-up duration = 3552 +/- 1201 days, diabetes duration = 4.77 +/- 2.29 years). Mean HbA1c and low high-density lipoprotein-cholesterol (HDL-C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL-C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J-shaped relationship with non-AMI-related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models. Conclusion A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.