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Statistical models to preoperatively predict operative difficulty in laparoscopic cholecystectomy: A systematic
Maria Vannucci1, Giovanni Guglielmo Laracca2, Paolo Mercantini3
1University of Tor Vergata, Rome, Italy; Institute for Research against Digestive Cancer (IRCAD), Strasbourg, France.
Surgery
|November 15, 2021
Summary
Predicting laparoscopic cholecystectomy difficulty is crucial. Statistical models show promise for preoperative prediction, but clearer definitions and clinical studies are needed for patient stratification.
Area of Science:
- Surgical Outcomes Research
- Predictive Analytics in Medicine
- Minimally Invasive Surgery
Background:
- Laparoscopic cholecystectomy operative difficulty varies significantly, impacting patient outcomes.
- Accurate prediction of surgical difficulty is essential for patient stratification and surgical planning.
Purpose of the Study:
- To systematically review and analyze statistical models for predicting laparoscopic cholecystectomy operative difficulty.
- To assess the performance and clinical value of these preoperative predictive models.
Main Methods:
- Systematic review following PRISMA guidelines, searching PubMed, Embase, and Cochrane Library.
- Inclusion of studies developing or validating predictive models in cohorts >100 patients.
- Data extraction using CHARMS and quality assessment with PROBAST tool.
Main Results:
- 22 studies met eligibility criteria; 18 focused on model development, 4 on validation.
- Most models predict conversion to open surgery or operating time; validated models predict intraoperative difficulty (AUC >0.70) and long procedures (AUC >0.76).
- Common predictors include demographics and ultrasound findings; clinical implementation was not studied.
Conclusions:
- Preoperative models for laparoscopic cholecystectomy difficulty show generally good performance and applicability.
- Further research is needed on unambiguous definitions of operative difficulty, robust validation, and clinical implementation studies for patient stratification.
