Association of Artificial Intelligence Derived Cardiothoracic Ratio Assessment on Non-Cardiac Chest CT with Heart Failure and All-Cause Mortality: A Retrospective Single Center Study.
Authors
Affiliations (12)
Affiliations (12)
- Department of Medicine, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35233, USA. Electronic address: [email protected].
- Department of Medicine, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35233, USA. Electronic address: [email protected].
- Division of Cardiovascular Disease, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL 35233. Electronic address: [email protected].
- Department of Medicine, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35233, USA. Electronic address: [email protected].
- Department of Medicine, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35233, USA. Electronic address: [email protected].
- University of Alabama at Birmingham Heersink School of Medicine, 1670 University Blvd, Birmingham, AL 35233, USA. Electronic address: [email protected].
- Division of Cardiovascular Disease, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL 35233. Electronic address: [email protected].
- Division of Cardiovascular Disease, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL 35233. Electronic address: [email protected].
- Division of Cardiovascular Disease, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL 35233. Electronic address: [email protected].
- Division of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35233, USA; Division of Radiology, St. Jude Children's Research Hospital, 262 Danny Thomas Pl, Memphis, TN 38105, USA. Electronic address: [email protected].
- Division of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35233, USA. Electronic address: [email protected].
- Division of Cardiovascular Disease, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL 35233. Electronic address: [email protected].
Abstract
The cardiothoracic ratio (CTR) is estimated by dividing cardiac width by thoracic width. The Area Deprivation Index (ADI) is a metric to quantify socioeconomic conditions. This study investigates the use of artificial intelligence (AI) to identify elevated CTR as a predictor for heart failure (HF) and mortality and assessed ADI's influence on these risks. This retrospective cohort study included 9,693 consecutive patients with non-cardiac chest CTs. An AI algorithm automated measurement of cardiac and thoracic diameters to calculate the cardiothoracic ratio (CTR). Patients were categorized into CTR tertiles: <0.5 (normal), 0.5-0.55 (borderline cardiomegaly), and ≥0.56 (cardiomegaly). Socioeconomic metrics were derived from electronic health records and stratified by ADI quartiles. Cardiovascular outcomes were extracted from ICD-10 codes over a six-year follow-up. Associations between CTR and cardiac outcomes were assessed using multivariate logistic regression and Cox proportional hazard models adjusted for age, sex, race, and ADI. Elevated CTR, notably in the cardiomegaly group, was associated with increased risk of prevalent HF (OR = 6.17, 95% CI = 5.27-7.23) and all-cause mortality (OR = 1.66, 95% CI = 1.47-1.87). Higher ADI scores were linked to increased risk of HF and mortality, although there was inconsistent interaction between elevated CTR and ADI regarding mortality. AI-derived CTR on non-cardiac chest CT may provide a cost-effective and efficient method for identifying patients at increased risk for HF and mortality. The utility of this technology proves to be efficacious amongst patients with high socioeconomic burden.