European heart journal. Digital health · 2026
Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome.
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Olesen Anne Sophie Overgaard, Miger Kristina Cecilia, Ørting Silas Nyboe, Petersen Jens, de Bruijne Marleen, Boesen Mikael Ploug, Kofoed Klaus Fuglsang, Grand Johannes, Thune Jens Jakob, Henderson Alasdair D, Jhund Pardeep S, Køber Lars, Nielsen Olav Wendelboe
Аннотация
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.
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.