A deep learning-based coronary artery calcium score >400 was associated with a significantly increased risk of chronic kidney disease progression compared to a score of 0 (HR 4.52).
Cohort (n=509)
No
Does higher deep learning-based coronary artery calcium score (DL-CACS) predict CKD progression and MACE in adults with chronic kidney disease?
Higher deep learning-based coronary artery calcium scores derived from routine non-gated chest CT are independently associated with an increased risk of CKD progression and major adverse cardiovascular events in patients with chronic kidney disease.
Effect estimate: HR 4.52 (95% CI 2.45-8.33)
Coronary artery calcification (CAC) is a pathological manifestation of coronary atherosclerosis in chronic kidney disease (CKD) patients. CAC on non-gated chest CT images can be precisely quantified through deep learning algorithms. Nevertheless, the relationship between deep learning-based coronary artery calcium score (DL-CACS) and the progression of CKD remains unclear. Between January 2017 and June 2022, data from individuals with CKD were retrospectively collected. All enrolled participants had undergone non-gated chest CT scans and were stratified by DL-CACS at baseline: 0, 1-100, 101–400, and > 400 Agatston units (AU). The primary outcome of this study was a composite endpoint related to CKD progression, defined as either a ≥ 50% decrease in eGFR from baseline or the initiation of kidney replacement therapy during follow-up. The secondary outcome was major adverse cardiovascular events (MACEs), including cardiac death, non-fatal myocardial infarction, revascularization, rehospitalization resulting from heart failure or aggravated angina and all-cause mortality. Among the 509 patients with CKD (median age: 64.00 57.00-70.50 years old; 317 men) finally included in this study, 155 (30.5%) patients achieved primary outcome during the follow-up period of 2152 person-years. Compared to individuals without CAC, higher DL-CACS was greatly associated with CKD progression. In the fully adjusted hazard models, the hazard ratio of DL-CACS of 1-100 was 2.27 (95% confidence interval CI, 1.26–4.10), 3.75 (95% CI, 2.01-7.00) for DL-CACS of 101–400, and 4.52 (95% CI, 2.45–8.33) for DL-CACS > 400. The sensitivity analyses yielded similar results with primary findings. Of the 48 patients experienced the secondary outcome of MACEs, DL-CACS of 1-100, 101–400, and > 400 were associated with HRs of 1.65 (95% CI, 0.39–7.06), 5.46 (95% CI, 1.41–21.14), and 11.60 (95% CI, 3.09–43.58), respectively, in the final hazard models. Higher DL-CACS is associated with an increased risk of CKD progression. Associations with MACE were directionally consistent but imprecise, reflecting the limited events and wide confidence intervals.
Yang et al. (2026) conducted a cohort in Chronic kidney disease (n=509). Deep learning-based coronary artery calcium score (DL-CACS) >400 vs. DL-CACS of 0 was evaluated on Composite of ≥50% decline in eGFR or initiation of kidney replacement therapy (HR 4.52, 95% CI 2.45-8.33). A deep learning-based coronary artery calcium score >400 was associated with a significantly increased risk of chronic kidney disease progression compared to a score of 0 (HR 4.52).