TITLE:
Risk Prediction Model for Surgical Treatment of Ruptured Corpus Luteum in the Ovary
AUTHORS:
Yuqi Qiu, Sufei Wang, Yong Chen, Wenrong He, Xiaowen Wang
KEYWORDS:
Corpus Luteum Rupture, Surgical Treatment, Prediction Model
JOURNAL NAME:
Yangtze Medicine,
Vol.7 No.2,
May
11,
2023
ABSTRACT: Objective: To explore the related factors of surgical treatment of patients with corpus luteum rupture and establish a risk prediction model of surgical treatment of corpus luteum rupture. Methods: 222 patients with corpus luteum rupture treated in Jingzhou First People’s Hospital from January 2015 to March 2022 were analyzed retrospectively, including 45 cases of surgery and 177 cases of conservative treatment. The training set and validation set were randomly assigned according to 7:3. We collected the basic information, laboratory and ultrasonic examination data of 222 patients. Logistic regression analysis was used to determine the independent risk factors and combined predictors of surgical treatment of corpus luteum rupture. The risk prediction model was established and the nomogram was drawn. The discrimination and calibration of the prediction model were verified and evaluated by receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test; Decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the prediction model. Results: Univariate logistic regression showed that whole abdominal pain (OR: 2.314, 95% CI: 1.090 - 4.912), abdominal muscle tension (OR: 2.379, 95% CI: 1.112 - 5.089), adnexal mass ≥ 4 cm (OR: 3.926, 95% CI: 1.771 - 8.266), hemoglobin Conclusion: The nomogram prediction model containing three predictive variables (hemoglobin, depth of pelvic effusion under ultrasound and cervical lifting pain) can be used to predict the risk of surgical treatment in patients with corpus luteum rupture.