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

列线图 医学 接收机工作特性 逻辑回归 红细胞压积 回顾性队列研究 腹腔镜检查 外科 体质指数 试验预测值 前瞻性队列研究 内科学
作者
Vesna Šalamun,Gaetano Riemma,Manca Pavec,Antonio Simone Laganà,Helena Ban Frangež
出处
期刊:Gynecologic and Obstetric Investigation [S. Karger AG]
卷期号:88 (5): 294-301 被引量:4
标识
DOI:10.1159/000533490
摘要

<b><i>Objective:</i></b> 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. <b><i>Design:</i></b> Retrospective analysis of prospectively collected data was performed. <b><i>Setting:</i></b> The study was conducted at Tertiary Care University Hospital. <b><i>Participants:</i></b> The participants of this study were reproductive-aged women undergoing laparoscopy for benign conditions. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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 (χ<sup>2</sup> = 4.45, <i>p</i> = 0.931) and the calibration curve indicated a good goodness-of-fit and prediction stability. <b><i>Limitations:</i></b> The retrospective design, moderate sensitivity, and study population limit the generalization of the findings, requiring additional research. <b><i>Conclusions:</i></b> 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.
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