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Author Spotlight: Utilizing Venoplasty Balloon Model in Rodents to Simulate Surgical Interventions for Deep Veins
Published on: May 24, 2024
Development and Internal Validation of a Predictive Model for Deep Venous Thrombosis Following Colpocleisis in
Qi Wang1,2, Stefano Manodoro3, Xiaoxiang Jiang1,2
1Department of Gynecology, Fujian Maternity and Child Health Hospital, Fuzhou, People's Republic of China.
Purpose:
Colpocleisis is a surgical option for elderly women with advanced pelvic organ prolapse (POP), often complicated by comorbidities that heighten postoperative deep venous thrombosis (DVT) risk. Effective tools for predicting postoperative DVT in these patients are lacking. This study aimed to develop a predictive model for the risk of DVT following colpocleisis and to validate its performance.
Patients And Methods:
This retrospective study included elderly patients who underwent colpocleisis for advanced POP between August 2019 and December 2024. Demographics, obstetric history, comorbidities, preoperative tests, and surgical details were analyzed. The primary endpoint was postoperative DVT, confirmed by ultrasound examination. Univariate and multivariable logistic regression analyses identified risk factors, which informed the development of a predictive nomogram-a graphical tool that translates statistical risk into a user-friendly format for individual prediction. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), which evaluates the net clinical benefit across threshold probabilities.
Results:
Of 307 patients, 8.8% (27/307) developed postoperative DVT. Multivariable analysis identified insulin-dependent diabetes, elevated preoperative cholesterol, and D-dimer levels as independent risk factors. The nomogram demonstrated strong discriminatory ability, with AUCs of 0.809 (95% confidence interval [CI]: 0.760-0.857) in the training set and 0.802 (95% CI: 0.752-0.852) in the validation set. At the optimal threshold (0.494), sensitivity was 0.725, specificity 0.848, positive predictive value (PPV) 0.805, and negative predictive value (NPV) 0.728. Calibration curves showed alignment between predicted and observed outcomes, while DCA demonstrated significant net benefit.
Conclusion:
This nomogram is a valuable tool for early DVT risk stratification in elderly colpocleisis patients. External validation in prospective multicenter studies is warranted.
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