Division of Gynaecology and Obstetrics, Department of Human Reproduction, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
Gynecol Obstet Invest. 2023;88(5):294-301. doi: 10.1159/000533490. Epub 2023 Aug 21.
The objective of the study was to develop a clinically applicable prediction tool to early seek for postoperative major complications after laparoscopic surgery for benign pathologies.
Retrospective analysis of prospectively collected data was performed.
The study was conducted at Tertiary Care University Hospital.
The participants of this study were reproductive-aged women undergoing laparoscopy for benign conditions.
Anamnestic, intraoperative, and postoperative characteristics from January 2019 to December 2021 were retrospectively reviewed. Patients with postoperative complications (reintervention or postoperative bleeding) were matched in a 1:2 ratio with women with same surgical indications without complications. Cases and controls were matched for preoperative hemoglobin, hematocrit, weight, height, body mass index, age, and blood volume. A prediction model was created by inserting multiple independent modifying factors through logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the predictive accuracy of the model, and the Hosmer-Lemeshow (H-L) test was carried out to evaluate the goodness-of-fit, and a calibration curve was drawn to confirm the predictive performance. A nomogram was depicted to visualize the prediction model.
Thirty-nine complicated procedures were matched with 78 uncomplicated controls. According to the multivariate logistic regression analysis findings, the prediction model was developed using C-reactive protein (CRP), intraoperative blood loss, and 24 h postoperative urinary volume, therefore a nomogram was generated. The area under the ROC curve of the prediction model was 0.879, depicting good accuracy, the sensitivity was 60.00%, while specificity reached 93.59%. The H-L test (χ2 = 4.45, p = 0.931) and the calibration curve indicated a good goodness-of-fit and prediction stability.
The retrospective design, moderate sensitivity, and study population limit the generalization of the findings, requiring additional research.
This prediction model based on CRP, intraoperative blood loss, and 24 h postoperative urinary volume might be a potentially useful tool for predicting reintervention and postoperative bleeding in patients undergoing planned gynecological laparoscopy.
本研究旨在开发一种临床适用的预测工具，以早期发现腹腔镜良性病变手术后的主要并发症。
对前瞻性收集的数据进行回顾性分析。
该研究在三级保健大学医院进行。
本研究的参与者为接受腹腔镜治疗良性疾病的育龄妇女。
回顾性分析 2019 年 1 月至 2021 年 12 月的病史、术中及术后特征。将术后有并发症（再次干预或术后出血）的患者与具有相同手术指征且无并发症的女性按 1:2 比例匹配。病例组和对照组在术前血红蛋白、血细胞比容、体重、身高、体重指数、年龄和血容量方面进行匹配。通过逻辑回归插入多个独立的修正因素来创建预测模型。使用受试者工作特征（ROC）曲线评估模型的预测准确性，通过 Hosmer-Lemeshow（H-L）检验评估拟合优度，并绘制校准曲线以确认预测性能。绘制诺模图以可视化预测模型。
39 例复杂手术与 78 例无并发症手术相匹配。根据多变量逻辑回归分析结果，使用 C 反应蛋白（CRP）、术中出血量和术后 24 小时尿量建立预测模型，生成诺模图。预测模型的 ROC 曲线下面积为 0.879，表明准确性较高，敏感性为 60.00%，特异性为 93.59%。H-L 检验（χ2=4.45，p=0.931）和校准曲线表明拟合优度和预测稳定性良好。
回顾性设计、中度敏感性和研究人群限制了研究结果的推广，需要进一步研究。
基于 CRP、术中出血量和术后 24 小时尿量的预测模型可能是预测计划妇科腹腔镜手术患者再次干预和术后出血的一种有用工具。
