Brigham and Women's Hospital, Boston, MA.
Harvard School of Public Health, Boston, MA.
Am J Obstet Gynecol. 2020 Oct;223(4):555.e1-555.e7. doi: 10.1016/j.ajog.2020.03.023. Epub 2020 Apr 2.
Although laparoscopic hysterectomy is well established as a favorable mode of hysterectomy owing to decreased perioperative complications, there is still room for improvement in quality of care. Previous studies have described laparoscopic hysterectomy risk, but there is currently no tool for predicting risk of complication at the time of laparoscopic hysterectomy.
This study aimed to create a prediction model for complications at the time of laparoscopic hysterectomy for benign conditions.
This is a retrospective cohort study that included patients who underwent laparoscopic hysterectomy for benign indications between 2014 and 2017 in US hospitals contributing to the American College of Surgeons - National Surgical Quality Improvement Program database. Data about patient baseline characteristics, perioperative complications (intraoperative complications, readmission, reoperation, need for transfusion, operative time greater than 4 hours, or postoperative medical complication), and uterine weight at the time of pathologic examination were collected retrospectively. Postoperative uterine weight was used as a proxy for preoperative uterine weight estimate. The sample was randomly divided into 2 patient populations, one for deriving the model and the other to validate the model.
A total of 33,123 women met the inclusion criteria. The rate of composite complication was 14.1%. Complication rates were similar in the derivation and validation cohorts (14.1% [2306 of 14,051] vs 13.9% [2289 of 14,107], P=.7207). The logistic regression risk prediction tool for hysterectomy complication identified 7 variables predictive of complication: history of laparotomy (21% increased odds of complication), age (2% increased odds of complication per year of life), body mass index (0.2% increased odds of complication per each unit increase in body mass index), parity (7% increased odds of complication per delivery), race (when compared with white women, black women had 34% increased odds and women of other races had 18% increased odds of complication), and American Society of Anesthesiologists score (when compared with American Society of Anesthesiologists 1, American Society of Anesthesiologists 2 had 31% increased odds, American Society of Anesthesiologists 3 had 62% increased odds, and American Society of Anesthesiologists 4 had 172% increased odds of complication). Predicted preoperative uterine weight also had a statistically significant nonlinear relationship with odds of complication. The c-statistics for the derivation and validation cohorts were 0.62 and 0.62, respectively. The model is well calibrated for women at all levels of risk.
The laparoscopic hysterectomy complication predictor model is a tool for predicting complications in patients planning to undergo hysterectomy.
尽管腹腔镜子宫切除术因其减少围手术期并发症而被广泛认为是一种有利的子宫切除术方式，但在护理质量方面仍有改进的空间。先前的研究已经描述了腹腔镜子宫切除术的风险，但目前还没有预测腹腔镜子宫切除术时并发症风险的工具。
本研究旨在为良性疾病的腹腔镜子宫切除术创建一种并发症预测模型。
这是一项回顾性队列研究，纳入了 2014 年至 2017 年期间在美国医院接受腹腔镜子宫切除术治疗良性疾病的患者，这些医院均为美国外科医师学会-国家手术质量改进计划数据库的贡献者。收集了患者的基线特征、围手术期并发症（术中并发症、再入院、再次手术、需要输血、手术时间超过 4 小时或术后医疗并发症）和术后病理检查时的子宫重量等数据。术后子宫重量用作术前子宫重量估计的替代物。样本被随机分为两个患者群体，一个用于建立模型，另一个用于验证模型。
共有 33123 名女性符合纳入标准。复合并发症的发生率为 14.1%。在推导和验证队列中，并发症发生率相似（14.1%[2306/14051]vs13.9%[2289/14107]，P=0.7207）。腹腔镜子宫切除术并发症的逻辑回归风险预测工具确定了 7 个预测并发症的变量：剖腹术史（并发症风险增加 21%）、年龄（每增加 1 岁，并发症风险增加 2%）、体重指数（体重指数每增加 0.2%，并发症风险增加 0.2%）、产次（每分娩一次，并发症风险增加 7%）、种族（与白人女性相比，黑人女性的并发症风险增加 34%，其他种族的女性增加 18%）和美国麻醉师协会评分（与美国麻醉师协会 1 级相比，美国麻醉师协会 2 级的并发症风险增加 31%，美国麻醉师协会 3 级的并发症风险增加 62%，美国麻醉师协会 4 级的并发症风险增加 172%）。美国麻醉师协会评分也与并发症的几率呈统计学上显著的非线性关系。推导和验证队列的 c 统计量分别为 0.62 和 0.62。该模型对所有风险水平的女性都具有良好的校准能力。
腹腔镜子宫切除术并发症预测模型是一种预测计划接受子宫切除术的患者发生并发症的工具。
