MedElevator
Войти в исследования The American journal of the medical sciences · 2026
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.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
LaForge John, Saleh Ahmed, Parcha Vibhu, Gonuguntla Akhilesh, Iragavarapu Shrikar, Soto Salomón Román, Hasnie Usman A, Josey Gina, Nordberg Megan, Smith Andrew, Rothenberg Steven, Clarkson Stephen A
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.