Ejection fraction quantification from ungated chest CT by AI
Zhou, J.; Kwiecinski, J.; Shanbhag, A.; Pieszko, K.; Ramirez, G.; Lemley, M.; Killekar, A.; Hijazi, W.; Miller, R. J.; Kavanagh, P. B.; Liang, J. X.; Slipczuk, L.; Travin, M. I.; Alexanderson, E.; Carvajal-Juarez, I.; Packard, R. R.; Al-Mallah, M.; Einstein, A. J.; Acampa, W.; Knight, S.; Le, V. T.; Mason, S.; Rosamond, T. L.; Hiczkiewicz, J.; Wopperer, S.; Chareonthaitawee, P.; Berman, D. S.; Newby, D. E.; Di Carli, M. F.; Dey, D.; Slomka, P. J.
Show abstract
Left ventricular ejection fraction (LVEF) is an important clinical metric, obtained by specialized imaging across the cardiac cycle. We present a novel AI approach to estimate LVEF from ungated chest CT. Using multicenter (11 sites) registry of 25,852 patients, AI-derived CT LVEF (AI LVEF) showed strong correlation with 3D gated positron emission tomography (r=0.84), area under the curve (AUC) of 0.96, negative predictive value of 95% for reduced LVEF (< 40%), and effectively stratified risk of heart failure, cardiovascular death, and all-cause death. In a separate large multicenter population (n=24,054) with lung CT scans, reduced AI LVEF was associated with a hazard ratio of 13.3 (95% confidence interval 9.7-18.4) for cardiovascular death. AI LVEF could also predict reduced echocardiographic LVEF (AUC=0.91). LVEF can be accurately estimated from non-contrast, ungated, low-dose chest CT scans, effectively stratifying patients for heart failure and mortality, with potential widespread clinical utility.
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