Artificial Intelligence Improves Prediction of Major Adverse Cardiovascular Events in Patients Undergoing Transcatheter Aortic Valve Replacement Planning CT

Giuseppe Tremamunno; Milan Vecsey-Nagy; U Joseph Schoepf; Emese Zsarnoczay; Gilberto J Aquino; Dmitrij Kravchenko; Andrea Laghi; Athira Jacob; Puneet Sharma; Saikiran Rapaka; Jim O'Doherty; Pal Spruill Suranyi; Ismail Mikdat Kabakus; Nicholas S Amoroso; Daniel H Steinberg; Tilman Emrich; Akos Varga-Szemes

doi:10.1016/j.acra.2024.09.046

Artificial Intelligence Improves Prediction of Major Adverse Cardiovascular Events in Patients Undergoing Transcatheter Aortic Valve Replacement Planning CT

Acad Radiol. 2024 Oct 9:S1076-6332(24)00695-0. doi: 10.1016/j.acra.2024.09.046. Online ahead of print.

Authors

Giuseppe Tremamunno¹, Milan Vecsey-Nagy², U Joseph Schoepf³, Emese Zsarnoczay⁴, Gilberto J Aquino³, Dmitrij Kravchenko⁵, Andrea Laghi⁶, Athira Jacob⁷, Puneet Sharma⁷, Saikiran Rapaka⁷, Jim O'Doherty⁸, Pal Spruill Suranyi³, Ismail Mikdat Kabakus³, Nicholas S Amoroso⁹, Daniel H Steinberg⁹, Tilman Emrich¹⁰, Akos Varga-Szemes¹¹

Affiliations

¹ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University, Hospital Via di Grottarossa, 1035-1039 00189 Rome, Italy (G.T., A.L.).
² Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Heart and Vascular Center, Semmelweis University, Varosmajor utca 68, Budapest 1122, Hungary (M.V.N.).
³ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.).
⁴ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Koranyi Sandor street 2, Budapest 1083, Hungary (E.Z.).
⁵ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany (D.K.); Quantitative Imaging Laboratory Bonn (QILaB), Venusberg-Campus 1, 53127, Bonn, Germany (D.K.).
⁶ Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University, Hospital Via di Grottarossa, 1035-1039 00189 Rome, Italy (G.T., A.L.).
⁷ Siemens Healthineers, 755 College Rd E, Princeton, New Jersey 08540, USA (A.J., P.S., S.R.).
⁸ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Siemens Medical Solutions, 40 Liberty Blvd, Malvern, Pennsylvania 19355, USA (J.O.).
⁹ Division in Cardiology, Department of Medicine, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (N.S.A., D.H.S.).
¹⁰ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Langenbeckstraße 1, Mainz 55131, Germany (T.E.).
¹¹ Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.). Electronic address: vargaasz@musc.edu.

PMID: 39389811
DOI: 10.1016/j.acra.2024.09.046

Abstract

Rationale and objectives: Coronary CT angiography (CCTA) is mandatory before transcatheter aortic valve replacement (TAVR). Our objective was to evaluate the efficacy of artificial intelligence (AI)-powered software in automatically analyzing cardiac parameters from pre-procedural CCTA to predict major adverse cardiovascular events (MACE) in TAVR patients.

Materials and methods: Patients undergoing pre-TAVR CCTA were retrospectively included. AI software automatically extracted 34 morphologic and volumetric cardiac parameters characterizing the ventricles, atria, myocardium, and epicardial adipose tissue. Clinical information and outcomes were recorded from institutional database. Cox regression analysis identified predictors of MACE, including non-fatal myocardial infarction, heart failure hospitalization, unstable angina, and cardiac death. Model performance was evaluated with Harrell's C-index, and nested models were compared using the likelihood ratio test. Manual analysis of 170 patients assessed agreement with automated measurements.

Results: Among the 648 enrolled patients (77 ± 9.3 years, 58.9% men), 116 (17.9%) experienced MACE within a median follow-up of 24 months (interquartile range 10-40). After adjusting for clinical parameters, only left ventricle long axis shortening (LV-LAS) was an independent predictor of MACE (hazard ratio [HR], 1.05 [95% confidence interval, 1.05-1.11]; p = 0.04), with significantly improved C-index (0.620 vs. 0.633; p < 0.001). When adjusted for the Society of Thoracic Surgeons Predicted Risk of Mortality score, LV-LAS was also predictive of MACE (HR, 1.08 [95%CI, 1.03-1.13]; p = 0.002), while improving model performance (C-index: 0.557 vs. 0.598; p < 0.001). All parameters showed good or excellent agreement with manual measurements.

Conclusion: Automated AI-based comprehensive cardiac assessment enables pre-TAVR MACE prediction, with LV-LAS outperforming all other parameters.

Keywords: Artificial intelligence; Coronary CT angiography; Major adverse cardiovascular events; Outcome prediction; Transcatheter aortic valve replacement.