Deep learning (DL) models can quantify coronary artery calcification using non-gated chest CT scans. However, the prognostic value of a DL-based coronary artery calcium score (DL-CACS) for predicting major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aimed to evaluate the prognostic value of DL-CACS derived from non-gated chest CT scans in patients with T2DM and to develop a risk stratification model for predicting MACEs. Patients with T2DM who underwent non-gated chest CT scans were retrospectively included and followed up for at least 2 years. Patients from Hospital A were randomly assigned to a training cohort and an internal validation cohort in a 3:2 ratio. Two predictive models were developed in the training cohort: Model 1 used the Framingham risk score (FRS), and Model 2 incorporated FRS and DL-CACS. The external validation cohort from Hospital B and the internal validation cohort were used to validate the proposed model. A total of 2,241 patients with T2DM (median age, 61 years; range, 54–68 years; 1,257 males) were included in this study. MACEs occurred in 10.71% (240/2241) of patients during follow-up. Patients who experienced MACEs exhibited significantly higher DL-CACS values than those without MACEs (p < 0.001). In the training cohort, multivariate Cox regression analysis identified DL-CACS as an independent predictor of MACEs (hazard ratio HR, 1.07; p < 0.001). Moreover, Model 2 demonstrated superior predictive performance compared to Model 1 across the training, internal validation, and external validation cohorts. In the external validation cohort, the C-index of Model 2 was larger than that of Model 1 (C-Index, 0.70 0.63–0.77 vs. 0.67 0.61–0.74; p = 0.007). DL-CACS derived from non-gated chest CT is an independent predictor of MACEs and provides incremental value in risk stratification for patients with T2DM compared with the FRS.
Xu et al. (2025) studied this question.