Risk-prediction Model for Patients Undergoing Laparoscopic Hysterectomy

Develop a model for predicting adverse outcomes at the time of laparoscopic hysterectomy (LH) for benign indications.Retrospective cohort study.Large academic center.All patients undergoing LH for benign indications at our institution between 2009 and 2017.LH (including robot-assisted and laparoscopically assisted vaginal hysterectomy) was performed per standard technique. Data about the patient, surgeon, perioperative adverse outcomes (intraoperative complications, readmission, reoperation, operative time >4 hours, and postoperative medical complications or length of stay >2 days), and uterine weight were collected retrospectively. Pathologic uterine weight was used as a surrogate for predicted preoperative uterine weight. The sample was randomly split, using a random sequence generator, into 2 cohorts, one for deriving the model and the other to validate the model.A total of 3441 patients were included. The rate of composite adverse outcomes was 14.1%. The final logistic regression risk-prediction model identified 6 variables predictive of an adverse outcome at the time of LH: race, history of laparotomy, history of laparoscopy, predicted preoperative uterine weight, body mass index, and surgeon annual case volume. Specifically included were race (97% increased odds of an adverse outcome for black women [95% confidence interval (CI), 34%-110%] and 34% increased odds of an adverse outcome for women of other races [95% CI, -11% to 104%] when compared with white women), history of laparotomy (69% increased odds of an adverse outcome [95% CI, 26%-128%]), history of laparoscopy (65% increased odds of an adverse outcome [95% CI, 21%-124%]), and predicted preoperative uterine weight (2.9% increased odds of an adverse outcome for each 100-g increase in predicted weight [95% CI, 2%-4%]). Body mass index and surgeon annual case volume also had a statistically significant nonlinear relationship with the risk of an adverse outcome. The c-statistic values for the derivation and validation cohorts were 0.74 and 0.72, respectively. The model is best calibrated for patients at lower risk (<20%).The LH risk-prediction model is a potentially powerful tool for predicting adverse outcomes in patients planning hysterectomy.