Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome
TL;DR
Artificial intelligence-based CCTA analysis identified a large low-risk subgroup and a smaller subgroup at increased HF risk, supporting further evaluation of opportunistic HF assessment from CCTA.
Abstract
Abstract Aims Heart failure (HF) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is associated with poor prognosis but often under-recognized. Coronary computed tomography angiography (CCTA), increasingly used in NSTE-ACS, contains cardiopulmonary features not routinely assessed for HF. We evaluated whether an artificial intelligence (AI) algorithm applied to CCTA could identify HF likelihood in NSTE-ACS. Methods and results In this retrospective external validation study, the AI algorithm was applied without retraining or recalibration to CCTA scans from 1009 patients with NSTE-ACS in the VERDICT trial. Using a pre-specified threshold, patients were classified as low or high AI likelihood of HF. The primary outcome was HF during index hospitalization. The secondary outcome was post-discharge HF hospitalization among patients discharged alive without HF, with analyses adjusted for global registry of acute coronary events score >140 and severe coronary artery disease. Death was treated as a competing risk. Overall, 838 patients (83%) were classified as low AI likelihood and 171 (17%) as high. During index hospitalization, HF was diagnosed in 10 patients (1%) with low AI likelihood and 12 (7%) with high. Sensitivity was 55%, specificity 84%, positive predictive value 7%, and negative predictive value 99%. High AI likelihood was associated with increased risk of index HF (subdistribution hazard ratio, 5.39, 95% confidence interval (CI) 2.32–12.50). After discharge, HF hospitalization occurred in 25 patients (3%) with low AI likelihood and 14 (8%) with high. High AI likelihood remained associated with HF hospitalization (subdistribution hazard ratio 2.56, 95% CI 1.34–4.90). Conclusion AI-based CCTA analysis identified a large low-risk subgroup and a smaller subgroup at increased HF risk, supporting further evaluation of opportunistic HF assessment from CCTA.