Risk-prediction Model for Patients Undergoing Laparoscopic Hysterectomy

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.

Identifying Risk Factors for Complications in Gynecologic Laparoscopic Surgery: A Retrospective Study

“…Notably, the incidence of complications was higher during the period of 2005-2011 compared to 2012-2021, suggesting a progressive improvement in the skill level of our surgeons over time. This finding is consistent with previous research indicating that the surgeon's annual case load influences the rate of complications [8].…”

“…These difficulties can lead to an increased risk of bleeding and potential damage to surrounding organs. The above findings agree well with prior research [3,6,8]. Other studies have also suggested that operation type, surgical complexity and degree of pelvic adhesions may affect the incidence of complications [13][14][15][16][17].…”

“…Factors known to increase the risk of major complications in patients undergoing gynecologic laparoscopic surgery include advanced age, higher body mass index (BMI), smoking, use of carbon dioxide, previous abdominal surgery, preoperative diseases, American Society of Anesthesiologist score of III or higher, higher predicted preoperative uterine weight (in cases of hysterectomy), and a postoperative increase in fibrinogen level of ≥20 [3,[6][7][8][9]. Understanding the factors associated with major com-plications after gynecologic laparoscopic surgery is crucial for surgeons to identify patients at higher risk and implement appropriate management strategies to mitigate these risk factors.…”

Risk of Reintervention or Postoperative Bleeding after Laparoscopy for Benign Gynecological Disease: A Clinical Prediction Model

“…This study showed that a prediction model based on intraoperative blood loss, CRP, and urinary volume 24 h after surgery has a good accuracy to predict complications (reintervention and moderate/high postoperative blood loss) in women undergoing laparoscopic surgery for benign gynecological pathologies. Several authors have tried to define a risk model for gynecological procedures [16][17][18]. Pepin et al [18] described a model for laparoscopic hysterectomy for benign conditions based on race, history of laparotomy, history of laparoscopy, predicted preoperative uterine weight, BMI, and surgeon annual case volume.…”

“…Several authors have tried to define a risk model for gynecological procedures [16][17][18]. Pepin et al [18] described a model for laparoscopic hysterectomy for benign conditions based on race, history of laparotomy, history of laparoscopy, predicted preoperative uterine weight, BMI, and surgeon annual case volume. Their model was successful in predicting adverse outcomes in women undergoing planned hysterectomy [18].…”