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Background <p>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.</p> Objectives <p>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.</p> Methods <p>Patients with T2DM who underwent non-gated chest CT scans were retrospectively included and followed up for at least 2&#xa0;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.</p> Results <p>A total of 2,241 patients with T2DM (median age, 61&#xa0;years; range, 54–68&#xa0;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 (<i>p</i> &lt; 0.001). In the training cohort, multivariate Cox regression analysis identified DL-CACS as an independent predictor of MACEs (hazard ratio [HR], 1.07; <i>p</i> &lt; 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] <i>vs.</i> 0.67 [0.61–0.74]; <i>p</i> = 0.007).</p> Conclusion <p>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.</p>

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Deep learning-based coronary calcium score derived from non-gated chest CT and major adverse cardiovascular events in patients with type 2 diabetes mellitus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月8日

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  • Yidan Xu,
  • Yarong Yu,
  • Xiaoying Ding,
  • Jiajun Yuan,
  • Lihua Yu,
  • Xu Dai,
  • Runjianya Ling,
  • Yufan Wang,
  • Jiayin Zhang

摘要

Background

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.

Objectives

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.

Methods

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.

Results

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).

Conclusion

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.