Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
Objective: To evaluate whether preoperative body composition metrics
automatically extracted from CT scans can predict postoperative outcomes after
colectomy, either alone or combined with clinical variables or existing risk
predictors. Main outcomes and measures: The primary outcome was the predictive
performance for 1-year all-cause mortality following colectomy. A Cox
proportional hazards model with 1-year follow-up was used, and performance was
evaluated using the concordance index (C-index) and Integrated Brier Score
(IBS). Secondary outcomes included postoperative complications, unplanned
readmission, blood transfusion, and severe infection, assessed using AUC and
Brier Score from logistic regression. Odds ratios (OR) described associations
between individual CT-derived body composition metrics and outcomes. Over 300
features were extracted from preoperative CTs across multiple vertebral levels,
including skeletal muscle area, density, fat areas, and inter-tissue metrics.
NSQIP scores were available for all surgeries after 2012.
