Composición Corporal 3D e Inteligencia Artificial: Una Nueva Herramienta para Evaluar la Sarcopenia y Predecir...

Sarcopenia is associated with higher mortality and morbidity in emergency laparotomies. Sarcopenia is traditionally measured with single 2D axial computed tomography (CT) slice at the L3 level, which is time-consuming and provide limited data. This study aims to determine if sarcopenia, measured using Artificial intelligence (AI) 3D-derived body composition (BC), can predict adverse outcomes after emergency abdominal surgery.

Retrospective analysis of Australian and New Zealand Emergency Laparotomy Audit-Quality Improvement (ANZELA-QI) patients treated at a tertiary Australian hospital from 2018 to 2023 was conducted. Multiple CT slices from lumbosacral regions were used for 3D BC analysis using a validated AI segmentation model. Sarcopenia was defined based on the lowest quartile for skeletal muscle radiodensity.

408 patients were included. Sarcopenic patients had lower skeletal muscle mass (< 0.001) with higher volumes of visceral adipose tissue (p < 0.001) and subcutaneous adipose tissue (p < 0.02). Sarcopenia was associated with age (73 vs. 57 years; p < 0.001), increased length of stay (26 vs. 15 days; p = 0.041) and intensive care unit admission (p < 0.001). Sarcopenia was not associated with significant post-operative complications (Clavien-Dindo ≥ 3) (p = 0.903) or worse discharge status (p = 0.138).

Sarcopenia is a significant predictor of adverse postoperative outcomes in patients undergoing emergency abdominal surgery. CT-derived 3D lumbosacral BC may help identify high-risk patients to guide risk stratification. AI has the potential to aid future implementation of 3D BC into routine clinical application.