A Linear Regression Equation for Predicting the Residual Volume of Chronic Subdural Hematoma 1 Week After Surgery

Abstract

The outcome of chronic subdural hematoma (CSDH) is influenced not only by the choice of treatment but also by various baseline characteristics. The main objective of this study is to identify the risk factors that can affect the prognosis of CSDH and develop a regression equation based on these risk factors. Methods: A total of 212 patients with CSDH were included in the study. We collected clinical data including age, gender, and so on, and radiological data including preoperative hematoma volume (V 1 ), effusion volume 1 day after surgery (V 2 ), gas volume 1 day after surgery (V 3 ), and so on. These were considered independent variables, while residual volume 1 week after surgery (V 4 ) was the dependent variable. Univariate linear regression analysis was performed to identify factors that were significantly related. Subsequently, multivariate linear regression analysis was conducted to determine the relationship between each independent variable and the dependent variable. Multiple linear regression analysis was used to obtain a regression equation predicting V 4 . Results: We have found that age (t = 3.109, P = 0.002), aspirin (t = 2.762, P = 0.006), hemostatic agents (haemocoagulase, t = 3.731, P < 0.001; vitamin K, t = 2.824, P = 0.005 < 0.05), V 2 (t = 8.73, P < 0.001), and V 3 (t = 5.968, P < 0.001) are significantly associated with V 4 . Furthermore, we have developed a regression equation that can predict this volume with CSDH. The fit of the model is robust with an R-squared value of 65.2% > 50%. Conclusion: Age, aspirin, hemostatic agent, V 2 , and V 3 are significantly associated with V 4. We developed a regression equation to predict this volume with CSDH.