Abstract
Background:
Automated frailty assessment predicts postoperative mortality, but routinely acquired CT imaging may provide complementary physiologic information. We evaluated whether AI-derived CT biomarkers improve one-year mortality prediction beyond an ICD-based Risk Analysis Index (RAI-ICD).
Study Design:
This retrospective cohort linked abdominopelvic CT scans obtained from 2013–2018 at a tertiary academic center to non-emergent surgery within 6 months. RAI-ICD was integrated with automated CT-derived muscle, adiposity, bone, and aortic calcification biomarkers using an XGBoost binary classification model, which we deemed the Unified Multimodal Model (UMM). The primary outcome was one-year all-cause mortality; discrimination, calibration, and decision-curve net benefit were evaluated.
Results:
Among 7,672 patients, one-year mortality was 12.3%; 7,638 patients were included in primary mortality models. RAI-ICD alone achieved an AUROC of 0.75 and outperformed every individual imaging biomarker. Adding all imaging biomarkers increased AUROC to 0.79 (ΔAUROC +0.04; P
Conclusions:
Integrating automated CT biomarkers with RAI-ICD improved one-year postoperative mortality prediction, high-risk calibration, and clinical net benefit compared with frailty assessment alone. External validation is required before clinical implementation.