RESUMEN
Background: To develop and validate a nomogram for predicting the probability of deep venous thrombosis (DVT) in patients with aneurysmal subarachnoid hemorrhage (aSAH) during the perioperative period, using clinical features and readily available biochemical parameters. Methods: The least absolute shrinkage and selection operator (LASSO) regression technique was employed for data dimensionality reduction and selection of predictive factors. A multivariable logistic regression analysis was conducted to establish a predictive model and nomogram for post-aSAH DVT. The discriminative ability of the model was determined by calculating the area under the curve (AUC). Results: A total of 358 aSAH patients were included in the study, with an overall incidence of DVT of 20.9%. LASSO regression identified four variables, including age, modified Fisher grade, total length of hospital stay, and anticoagulation therapy, as highly predictive factors for post-aSAH DVT. The patients were randomly divided into a modeling group and a validation group in a 6:4 ratio to construct the nomogram. The AUCs of the modeling and validation groups were 0.8511 (95% CI, 0.7922-0.9099) and 0.8633 (95% CI, 0.7968-0.9298), respectively. Conclusions: The developed nomogram exhibits good accuracy, discriminative ability, and clinical utility in predicting DVT, aiding clinicians in identifying high-risk individuals and implementing appropriate preventive and treatment measures.