Šalamun V, Riemma G, Pavec M, Laganà AS, Ban Frangež H. Risk of Reintervention or Postoperative Bleeding after Laparoscopy for Benign Gynecological Disease: A Clinical Prediction Model.
Gynecol Obstet Invest 2023;
88:294-301. [PMID:
37604136 DOI:
10.1159/000533490]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE
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.
DESIGN
Retrospective analysis of prospectively collected data was performed.
SETTING
The study was conducted at Tertiary Care University Hospital.
PARTICIPANTS
The participants of this study were reproductive-aged women undergoing laparoscopy for benign conditions.
METHODS
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.
RESULTS
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.
LIMITATIONS
The retrospective design, moderate sensitivity, and study population limit the generalization of the findings, requiring additional research.
CONCLUSIONS
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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