Objective To investigate the construction and application of a risk prediction model for deep vein thrombosis (DVT) in patients with aneurysmal subarachnoid hemorrhage (aSAH).

Methods A total of 250 patients with aSAH were enrolled in this study and divided into DVT (n=45) and non-DVT groups (n=205) based on the occurrence of DVT. General information and routinelaboratory indicators were collected and compared between the two groups. Multivariate Logistic regression analysis was performed to identify risk factors for DVT in aSAH patients. A nomogram prediction model for DVT after aSAH was established and its discriminative ability was evaluated using the receiver operating characteristic (ROC) curve. The clinical utility of the nomogram was assessed by decision curve analysis.

Results Statistically significant differences were observed in age, history of hypertension, Glasgow Coma Scale (GCS) score, Hunt-Hess grade and length of hospital stay between the DVT and non-DVT groups (P < 0.05). The plasma fibrinogen level was significantly higher in the DVT group compared to the non-DVT group (P < 0.05), while no significant differences were found for other indicators (P>0.05). Multivariate Logistic regression analysis revealed that age, Hunt-Hess grade, length of hospital stay, and plasma fibrinogen level were influencing factors of the occurrence of DVT in aSAH patients(P < 0.05). ROC analysis showed that the nomogram exhibited good predictive performance in both the modeling and validation groups, with areas under the curve (AUCs) of 0.875 (95%CI, 0.802 to 0.948) and 0.872 (95%CI, 0.757 to 0.987), respectively. Decision curve analysis indicated that the nomogram provided a high net benefit for predicting DVT in aSAH patients across a wide range of threshold probabilities.

Conclusion Age, Hunt-Hess grade, length of hospital stay, and plasma fibrinogen level are influential factors for DVT in aSAH patients. The nomogram in predicting DVT after aSAH demonstrates good discriminative ability, calibration degree, and clinical application value. This model can better identify high-risk patients and provide individualized prevention and treatment strategies.