Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain.
CMAJ. 2022 Oct 3;194(38):E1306-E1317. doi: 10.1503/cmaj.220914.
Hysterectomy, the most common gynecological operation, requires surgeons to counsel women about their operative risks. We aimed to develop and validate multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions.
We obtained routinely collected health administrative data from the English National Health Service (NHS) from 2011 to 2018. We defined major complications based on core outcomes for postoperative complications including ureteric, gastrointestinal and vascular injury, and wound complications. We specified 11 predictors a priori. We used internal-external cross-validation to evaluate discrimination and calibration across 7 NHS regions in the development cohort. We validated the final models using data from an additional NHS region.
We found that major complications occurred in 4.4% (3037/68 599) of laparoscopic and 4.9% (6201/125 971) of abdominal hysterectomies. Our models showed consistent discrimination in the development cohort (laparoscopic, C-statistic 0.61, 95% confidence interval [CI] 0.60 to 0.62; abdominal, C-statistic 0.67, 95% CI 0.64 to 0.70) and similar or better discrimination in the validation cohort (laparoscopic, C-statistic 0.67, 95% CI 0.65 to 0.69; abdominal, C-statistic 0.67, 95% CI 0.65 to 0.69). Adhesions were most predictive of complications in both models (laparoscopic, odds ratio [OR] 1.92, 95% CI 1.73 to 2.13; abdominal, OR 2.46, 95% CI 2.27 to 2.66). Other factors predictive of complications included adenomyosis in the laparoscopic model, and Asian ethnicity and diabetes in the abdominal model. Protective factors included age and diagnoses of menstrual disorders or benign adnexal mass in both models and diagnosis of fibroids in the abdominal model.
Personalized risk estimates from these models, which showed moderate discrimination, can inform clinical decision-making for people with benign conditions who may require hysterectomy.
子宫切除术是最常见的妇科手术,需要外科医生向女性告知手术风险。我们旨在开发和验证多变量逻辑回归模型,以预测腹腔镜或剖腹子宫切除术治疗良性疾病的主要并发症。
我们从 2011 年至 2018 年从英国国家医疗服务体系(NHS)获得常规收集的健康行政数据。我们根据术后并发症的核心结果定义了主要并发症,包括输尿管、胃肠道和血管损伤以及伤口并发症。我们预先指定了 11 个预测因素。我们使用内部-外部交叉验证在开发队列的 7 个 NHS 地区评估区分度和校准度。我们使用来自额外 NHS 地区的数据验证最终模型。
我们发现,腹腔镜手术中主要并发症发生率为 4.4%(3037/68599),剖腹手术中主要并发症发生率为 4.9%(6201/125971)。我们的模型在开发队列中显示出一致的区分度(腹腔镜手术,C 统计量 0.61,95%置信区间[CI]为 0.60 至 0.62;剖腹手术,C 统计量 0.67,95%CI 为 0.64 至 0.70),在验证队列中也显示出相似或更好的区分度(腹腔镜手术,C 统计量 0.67,95%CI 为 0.65 至 0.69;剖腹手术,C 统计量 0.67,95%CI 为 0.65 至 0.69)。粘连在两个模型中都是预测并发症的最主要因素(腹腔镜手术,优势比[OR]为 1.92,95%CI 为 1.73 至 2.13;剖腹手术,OR 为 2.46,95%CI 为 2.27 至 2.66)。其他预测并发症的因素包括腹腔镜模型中的子宫腺肌病,以及剖腹模型中的亚洲人种和糖尿病。保护因素包括年龄以及两种模型中的月经失调或良性附件包块诊断和剖腹模型中的子宫肌瘤诊断。
这些模型的个性化风险估计具有中等区分度,可以为可能需要子宫切除术的良性疾病患者提供临床决策信息。