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

Authors

Pengzhan Yin, Jiaqi Wang, Chao Zhang, Yongxiang Tang, Xiankuo Hu, Hongmin Shu, Jun Wang, Bin Liu, Yongqiang Yu, Yunfeng Zhou, Xiaohu Li

Published in

NPJ digital medicine. Volume 8. Issue 1. Pages 542. Aug 24, 2025. Epub Aug 24, 2025.

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.

PMID:
40849351
Bibliographic data and abstract were imported from PubMed on 24 Aug 2025.

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