Background: The identification of high-risk coronary artery disease (HRCAD) is important in diabetes mellitus (DM) patients. However, the reliability of current models to predict HRCAD has not been fully investigated. Thus, we aimed to validate and compare CONFIRM and PROMISE high-risk model (CHM and PHM) in DM patients. Methods: 5936 symptomatic DM patients who underwent coronary computed tomographic angiography (CCTA) were identified. Probability of HRCAD for each patient was estimated based on CHM and PHM, respectively. We used Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer-Lemeshow (H-L) test to evaluate model’s predictive accuracy. Results: Overall, 470 (8%) patients had HRCAD on CCTA. There was no difference between the AUC for CHM and PHM (0.744 v.s. 0.721, p = 0.0873). Compared to CHM, PHM demonstrated a positive IDI (3.08%, p < 0.0001), positive NRI (12.50%, p < 0.0001) and less discrepancy between observed and predicted probabilities (H-L χ2 for CHM: 35.81, p < 0.0001; H-L χ2 for PHM: 23.75, p = 0.0025). Conclusions: Compared to CHM, PHM was associated with a more accurate prediction for HRCAD and might optimize downstream management strategy in symptomatic patients with DM. Clinical Trial Registration: ClinicalTrials.gov (NCT04691037).

Background The risk assessment of patients with stable chest pain (SCP) to defer further cardiovascular testing is crucial, but the most appropriate risk assessment strategy remains unknown. We aimed to compare current strategies to identify low risk SCP patients. Methods 5289 symptomatic patients who had undergone coronary artery calcium score (CACS) and coronary computed tomographic angiography scan were identified and followed. Pretest probability (PTP) of obstructive coronary artery disease (CAD) for every patient was estimated according to European Society of Cardiology (ESC)-PTP model and CACS-weighted clinical likelihood (CACS-CL) model, respectively. Based on the 2019 ESC guideline-determined risk assessment strategy (ESC strategy) and CACS-CL model-based risk assessment strategy (CACS-CL strategy), all patients were divided into low and high risk group, respectively. Area under receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) was used. Results CACS-CL model provided more robust estimation of PTP than ESC-PTP model did, with a larger AUC (0.838 versus 0.735, p < 0.0001), positive IDI (9%, p < 0.0001) and less discrepancy between observed and predicted probabilities. As a result, compared to ESC strategy which only applied CACS-CL model to patients with borderline ESC-PTP, CACS-CL strategy incorporating CACS with estimation of PTP to entire SCP patients indicated a positive NRI (19%, p < 0.0001) and a stronger association to major adverse cardiovascular events, with hazard ratios: 3.97 (95% confidence intervals: 2.75–5.72) versus 5.11 (95% confidence intervals: 3.40–7.69). Conclusion The additional use of CACS for all SCP patients in CACS-CL strategy improved the risk assessment of SCP patients to identify individuals at low risk.