CABIT: a novel biomarkers-integrated inflammatory risk tool for ischemic heart disease developed in the USA and prospectively validated in China

Ischemic heart disease (IHD) remains a leading cause of death globally. Most of previous predictive models rely on conventional factors or inaccessible biomarkers and lack validation in Asian populations. Novel inflammatory indicators have shown promising potential in predicting IHD risk. This study aimed to develop a new model for predicting the risk of IHD based on the novel inflammatory indicators and validate its performance prospectively in two independent Chinese cohorts. Variables were selected through Elastic Net regression from the data of 11,840 participants in the US National Health and Nutrition Examination Survey (US NHANES). Using these variables, a prediction model was developed and internally validated. Then, external validation was performed in two prospective cohorts from the Second Affiliated Hospital of Nanjing Medical University in China, with median follow-up durations of 6.0 and 6.7 years, respectively. The model, named CABIT, was assessed by receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), clinical impact curves (CIC), and net reduction analysis (NR). A nomogram was derived from the model. Finally, we compared the performances of CABIT and the existing prediction models by the systematic review. CABIT was established by incorporating the natural logarithms of the white blood cell-to-high-density lipoprotein cholesterol ratio (WHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), monocyte-to-lymphocyte ratio (MLR), platelet -to-lymphocyte ratio (PLR), and conventional clinical covariates. It generated an area under the curve (AUC) of 0.838 (95% CI: 0.826–0.851) in the training set, 0.823 (95% CI: 0.799–0.843) in the internal validation set, 0.831 (95% CI: 0.761–0.897) in the external validation set 1 and 0.702 (95% CI: 0.553–0.850) in the external validation set 2. Consistent calibration and clinical utility were observed across all datasets. The systematic comparisons indicated that CABIT exhibited a favorable discriminative ability and could address some limitations of previous models. An interactive web-based risk calculator for CABIT is publicly available (https://cabit-risk-tool.netlify.app/). The CABIT showed a satisfactory performance in predicting the earlier-stage IHD across diverse populations, especially in Chinese cohorts, and might assist in timely and personalized clinical decision-making.