Development of a Predictive Nomogram Model for Early Deep Vein Thrombosis in Postoperative Spontaneous Intracerebral Hemorrhage Patients.

Abstract

This study explores risk determinants for participants' lower extremities deep vein thrombosis (DVT) in the perioperative phase after spontaneous intracerebral hemorrhage (SICH), thereby informing more effective clinical prevention and treatment strategies. During the period spanning October 2021 to March 2024, clinical data from 96 participants who received surgical treatment for spontaneous cerebral hemorrhage was analyzed in a retrospective study. Participants were classified into DVT and negative-DVT groups within the first week post-surgery. We used univariate logistic regression and multivariate logistic regression analyses to assess the impact of various clinical variables on DVT. A nomogram model was constructed to forecast the occurrence of early DVT following SICH surgery. The model's performance was assessed and validated using receiver operating characteristic (ROC) curves and bootstrap resampling. Among the 96 participants, 46 developed DVT. Significant differences were noted in age, D-dimer levels, fibrinogen degradation products, Caprini scores, and total surgical bleeding volume between the groups. Multivariate analysis revealed that Caprini score (the values of OR, 95% CI, and P are 1.962, 1.124-3.424, and 0.018, respectively) and total surgical bleeding volume (the values of OR, 95% CI, and P are 1.010, 1.002-1.018, and 0.017, respectively) were risk variables contributing to DVT occurrence. The area under the receiver operating characteristic curve was 0.918 (95% CI, 0.821-0.988). The calibration curve showed good prediction accuracy. The Caprini score and total surgical bleeding volume are meaningful self-reliant risk variables contributing to DVT occurrence in postoperative participants with SICH. We have created a straightforward and efficient model to predict early DVT post-SICH surgery. This model serves as a valuable clinical tool for evaluating individual risk and enhancing decision-making processes.