Modeling Post-cholecystitis Complication Risk from Perioperative Liver Function and Immune-inflammation Indicators.

Area of Science:

• Surgical Oncology
• Hepatobiliary Surgery
• Inflammatory Biomarkers

Background:

• Acute calculous cholecystitis (ACC) surgery can lead to complications.
• Current tools for predicting complications after ACC surgery often overlook dynamic biomarker changes.
• Perioperative liver enzyme elevations and systemic inflammation are common in ACC.

Purpose of the Study:

• To develop and validate a multivariable risk model for predicting Clavien-Dindo grade ≥II complications after cholecystectomy for ACC.
• To incorporate dynamic perioperative changes in liver function tests (LFTs) and the Systemic Immune-Inflammation Index (SII) into the risk model.
• To assess the predictive performance and clinical utility of the developed risk model.

Main Methods:

• Retrospective cohort study of 260 adult patients undergoing cholecystectomy for ACC.
• Calculation of Δ-values (day 1 minus baseline) for ALT, AST, total bilirubin, and SII.
• Multivariable logistic regression with internal validation using bootstrap replications.

Main Results:

• The final model included ΔALT, ΔAST, Δbilirubin, ΔSII, age, ASA status, and operative duration.
• The model showed good discrimination (AUC 0.82) and excellent calibration.
• Dynamic biomarker changes (ΔALT, ΔAST, Δbilirubin, ΔSII) were significant predictors of complications.

Conclusions:

• Integrating dynamic perioperative changes in LFTs and SII with clinical factors provides a robust risk prediction model for postoperative complications after ACC surgery.
• The developed model demonstrates clinical utility in identifying patients at higher risk for complications.
• This approach enhances preoperative risk stratification and potentially improves patient management strategies.