Development and validation of a clinical decision tool for predicting long-term pain reduction following laparoscopic cholecystectomy in patients with symptomatic cholecystolithiasis: a prospective cohort study.

BACKGROUND

The applicability of the recently reported predictors to predict pain reduction after laparoscopic cholecystectomy (LC) is limited by only predicting short-term outcomes. The aim of this study was to develop and externally validate a model to predict clinically relevant pain reduction 5 years after LC.

MATERIALS AND

METHODS

This study reports on 5-year follow-up data of two multicenter prospective trials in 25 hospitals. Only patients who underwent LC were included. The model was developed using data from the SUCCESS cohort ( n = 494) and externally validated using data from the SECURE trial ( n = 1067). Both studies applied identical inclusion criteria for patients with uncomplicated cholecystolithiasis. The model was developed using multivariable logistic regression and internally validated using bootstrapping and subsequently validated externally. Model performance was evaluated using discrimination, calibration, and decision curve analysis.

RESULTS

A total of 1561 patients were included of which 1240 (79.4%) who had received LC after 5-year follow-up; 746 of 1240 patients (70.2%) reported a clinically relevant pain reduction. A multivariable model showed that male sex, increased Visual Analog Scale pain score at baseline, pain radiating to the back, nausea, with absence of obstipation, diarrhea, and postprandial bloating were independent predictors for long-term clinically relevant pain reduction. External validation indicated good discrimination and calibration between patients with and without clinically relevant pain reduction (C-statistic, 0.75; 95% CI, 0.70-0.79).

CONCLUSION

In this study, we developed and validated a model for predicting long-term clinically relevant pain reduction after LC. The model may serve as an instrument to improve patient selection for surgery and may enhance shared decision making.