Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study

Abstract

Objectives: To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFR_CT) in diabetes mellitus (DM) patients.

Methods: In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFR_CT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n = 112) and non-DM (n = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared.

Results: Sensitivity, specificity, accuracy, and AUC of FFR_CT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFR_CT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFR_CT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05).

Conclusions: DM has no negative impact on the diagnostic accuracy of machine learning-based FFR_CT.

Key points: • ML-based FFR_CT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM. • FFR_CT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients. • Coronary calcification had no significant effect on the diagnostic accuracy of FFR_CT to detect ischemia in DM patients.

Keywords: Computed tomography angiography; Coronary artery disease; Diabetes mellitus; Fractional flow reserve; Machine learning.