Risk-prediction Model for Patients Undergoing Laparoscopic Hysterectomy.
Academic Article
Overview
abstract
STUDY OBJECTIVE: Develop a model for predicting adverse outcomes at the time of laparoscopic hysterectomy (LH) for benign indications. DESIGN: Retrospective cohort study. SETTING: Large academic center. PATIENTS: All patients undergoing LH for benign indications at our institution between 2009 and 2017. INTERVENTIONS: 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. MEASUREMENTS AND MAIN RESULTS: 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%). CONCLUSION: The LH risk-prediction model is a potentially powerful tool for predicting adverse outcomes in patients planning hysterectomy.
