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Published on: December 9, 2022
Risk Factors and Prediction Model for Preoperative Lower-Extremity Venous Thrombosis in Elderly Women Undergoing
Xiaoxiang Jiang1,2, Stefano Manodoro3,4, Chaoqin Lin5,6
1Department of Gynecology, Fujian Maternity and Child Health Hospital, 18 Dao-shan street, Gu-lou District, Fuzhou, 350000, China.
Introduction And Hypothesis:
Preoperative lower-extremity venous thrombosis (LEVT) is a potential concern in elderly women undergoing colpocleisis for advanced pelvic organ prolapse (POP), given their increased risk owing to age, limited mobility, and comorbidities. This study is aimed at determining the prevalence of LEVT, identifying independent risk factors, and developing a predictive model to assist in risk stratification.
Methods:
A retrospective study was conducted on elderly women scheduled for colpocleisis owing to advanced POP between August 2019 and September 2024. Doppler ultrasonography was used to diagnose LEVT. Independent risk factors were identified using univariate and multivariate logistic regression. A nomogram-based predictive model was developed and validated using five-fold cross-validation.
Results:
Among 340 patients, 52 (15.3%) had preoperative LEVT. Advanced age, history of venous thromboembolism, current or past malignancies, elevated cholesterol levels, and high D-dimer levels were independently associated with LEVT. The predictive model showed good discrimination, with an area under the curve for the receiver operating characteristic of 0.824 in the training set and 0.806 in the validation set. At the optimal cutoff (Youden index = 0.121), the sensitivity was 80.8%, specificity was 69.1%, positive predictive value was 32.1%, and negative predictive value was 95.2%. Decision curve analysis confirmed the model's clinical utility.
Conclusions:
A substantial proportion of elderly women undergoing colpocleisis have preoperative LEVT. The developed predictive model provides a practical tool for early risk assessment, potentially improving perioperative management and patient outcomes.
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