Prediction of functional outcomes in aneurysmal subarachnoid hemorrhage using pre-/postoperative noncontrast CT within 3 days of admission.

Abstract

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition, and accurate prediction of functional outcomes is critical for optimizing patient management within the initial 3 days of presentation. However, existing clinical scoring systems and imaging assessments do not fully capture clinical variability in predicting outcomes. We developed a deep learning model integrating pre- and postoperative noncontrast CT (NCCT) imaging with clinical data to predict 3-month modified Rankin Scale (mRS) scores in aSAH patients. Using data from 1850 patients across four hospitals, we constructed and validated five models: preoperative, postoperative, stacking imaging, clinical, and fusion models. The fusion model significantly outperformed the others (all p<0.001), achieving a mean absolute error of 0.79 and an area under the curve of 0.92 in the external test. These findings demonstrate that this integrated deep learning model enables accurate prediction of 3-month outcomes and may serve as a prognostic support tool early in aSAH care.