Predicting major complications after laparoscopic cholecystectomy: a simple risk score

Melissa M. Murphy, University of Massachusetts Medical School
Shimul A. Shah, University of Massachusetts Medical School
Jessica P. Simons, University of Massachusetts Medical School
Nicholas G. Csikesz, University of Massachusetts Medical School
Theodore P. McDade, University of Massachusetts Medical School
Andreea Bodnari, University of Massachusetts Medical School
Sing Chau Ng, University of Massachusetts Medical School
Zheng Zhou, University of Massachusetts Medical School
Jennifer F. Tseng, University of Massachusetts Medical School

Abstract

INTRODUCTION: Reported morbidity varies widely for laparoscopic cholecystectomy (LC). A reliable method to determine complication risk may be useful to optimize care. We developed an integer-based risk score to determine the likelihood of major complications following LC.

METHODS: Using the Nationwide Inpatient Sample 1998-2006, patient discharges for LC were identified. Using previously validated methods, major complications were assessed. Preoperative covariates including patient demographics, disease characteristics, and hospital factors were used in logistic regression/bootstrap analyses to generate an integer score predicting postoperative complication rates. A randomly selected 80% was used to create the risk score, with validation in the remaining 20%.

RESULTS: Patient discharges (561,923) were identified with an overall complication rate of 6.5%. Predictive characteristics included: age, sex, Charlson comorbidity score, biliary tract inflammation, hospital teaching status, and admission type. Integer values were assigned and used to calculate an additive score. Three groups stratifying risk were assembled, with a fourfold gradient for complications ranging from 3.2% to 13.5%. The score discriminated well in both derivation and validation sets (c-statistic of 0.7).

CONCLUSION: An integer-based risk score can be used to predict complications following LC and may assist in preoperative risk stratification and patient counseling.