A nomogram model for predicting risk of lower extremity deep venous thrombosis in patients after heart valve replacement

Ayu Sun, Hanjun Zhang, Tiansheng Tang, Dafa Zhang

Abstract

We aimed to construct and validate a nomogram model for predicting the risk of lower extremity deep venous thrombosis (DVT) in patients after heart valve replacement. The data of patients hospitalised for heart valve replacement between January 2021 and March 2024 were retrospectively analysed. The independent risk factors for postoperative DVT were screened through univariate and multivariate logistic regression analyses, based on which a prediction model was established. The results of univariate analysis indicated that smoking history, history of alcohol intake, operation time, intraoperative blood loss, postoperative bed rest time, postoperative DVT screening time, postoperative processing result, D-dimer level, postoperative pain score, postoperative hospitalisation time, postoperative complication, pulmonary infection, cardiac insufficiency, platelet count, prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), and international normalised ratio (INR) were associated with the occurrence of postoperative DVT (P < 0.05). The AUC, optimal cutoff value, sensitivity, and specificity reached 0.884 (95% CI: 0.817–0.947), 0.825, 0.853, and 0.756 respectively in the validation group. A risk prediction model applicable to DVT in patients undergoing heart valve replacement was successfully constructed, with satisfactory predictive efficiency.