Department of Minimally Invasive Gynecologic Surgery, Brigham and Women's Hospital (Drs. Pepin and Cohen, and Ms. Maghsoudlou); Department of Minimally Invasive Gynecologic Surgery, Weill Cornell Medicine, New York, New York (Dr. Pepin).
Department of Epidemiology, Harvard School of Public Health (Dr. Cook), Boston, Massachusetts.
J Minim Invasive Gynecol. 2021 Oct;28(10):1751-1758.e1. doi: 10.1016/j.jmig.2021.03.001. Epub 2021 Mar 10.
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.
为良性指征的腹腔镜子宫切除术（LH）时的不良结局建立预测模型。
回顾性队列研究。
大型学术中心。
在我院于 2009 年至 2017 年期间因良性指征接受 LH 的所有患者。
LH（包括机器人辅助和腹腔镜辅助阴道子宫切除术）按标准技术进行。回顾性收集有关患者、外科医生、围手术期不良结局（术中并发症、再入院、再次手术、手术时间＞4 小时和术后医疗并发症或住院时间＞2 天）和子宫重量的数据。病理子宫重量被用作预测术前子宫重量的替代物。使用随机序列发生器将样本随机分为两个队列，一个用于推导模型，另一个用于验证模型。
共纳入 3441 例患者。复合不良结局的发生率为 14.1%。最终的逻辑回归风险预测模型确定了 6 个预测 LH 时不良结局的变量：种族、剖腹术史、腹腔镜检查史、预测术前子宫重量、体重指数和外科医生的年手术量。具体包括种族（黑人女性发生不良结局的可能性增加 97%[95%置信区间（CI），34%-110%]，其他种族女性发生不良结局的可能性增加 34%[95%CI，-11%至 104%]，与白人女性相比）、剖腹术史（不良结局的可能性增加 69%[95%CI，26%-128%]）、腹腔镜检查史（不良结局的可能性增加 65%[95%CI，21%-124%]）和预测术前子宫重量（预测体重每增加 100g，不良结局的可能性增加 2.9%[95%CI，2%-4%]）。体重指数和外科医生的年手术量也与不良结局的风险存在统计学上的非线性关系。推导队列和验证队列的 c 统计值分别为 0.74 和 0.72。该模型最适合低风险（＜20%）患者。
LH 风险预测模型是预测计划行子宫切除术患者不良结局的一种潜在强大工具。
