A CAP-ALT-HbA1c Model for Biopsy-Confirmed Moderate-to-Severe Hepatic Steatosis in Bariatric Surgery Candidates: Development and Internal Validation.

Background/

Objectives

The controlled attenuation parameter (CAP) is widely used for non-invasive assessment of hepatic steatosis, but its discrimination for moderate-to-severe steatosis may be limited in patients with obesity. We developed and internally validated a model combining CAP, alanine aminotransferase (ALT), and glycated haemoglobin (HbA1c) to identify biopsy-confirmed S ≥ 2 steatosis in bariatric surgery candidates.

Methods

This retrospective single-centre study included 320 bariatric surgery candidates with preoperative CAP and laboratory data and intraoperative wedge liver biopsy as the reference standard. Candidate predictors were evaluated using univariable logistic regression, least absolute shrinkage and selection operator (LASSO) screening, and multivariable logistic regression. Model performance was assessed using receiver operating characteristic analysis, paired DeLong testing, bootstrap internal validation, calibration, Brier score, and decision curve analysis.

Results

S ≥ 2 steatosis was present in 182 patients (56.9%). The combined model showed higher discrimination than CAP alone (area under the curve [AUC] 0.768, 95% CI 0.717-0.819 vs. 0.671, 95% CI 0.612-0.731), with an AUC difference of 0.096 (95% CI 0.049-0.144; paired DeLong p < 0.001). At the internally derived Youden threshold, sensitivity was 53.8% and specificity was 86.2%. The combined model also showed a lower Brier score than CAP alone (0.1941 vs. 0.2234). Bootstrap internal validation yielded an optimism-corrected AUC of 0.760 and a mean absolute calibration error of 0.017. Decision curve analysis suggested modest incremental net benefit over CAP alone.

Conclusions

The CAP-ALT-HbA1c model showed higher discrimination and lower overall prediction error than CAP alone. It may complement CAP as an adjunctive tool for preoperative risk stratification, but external validation and assessment of clinically appropriate decision thresholds are required before routine clinical implementation.