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

Abstract

<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.

Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer

“…In addition to radiomics, many other omics methods are used to guide risk stratification and assess the prognosis of gynecologic diseases, such as genomics, transcriptomics, proteomics, and metabolomics [ 36 ]. Apart from omics models, various clinical nomograms have also been developed to predict disease progression [ 37 ]. A previous study developed a model to predict the probability of pelvic inflammatory disease progressing to sepsis, including dialysis, reduced platelet counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts [ 38 ].…”