Moriya Masamichi, Karako Kenji, Miyazaki Shogo, Minakata Shin, Satoh Shuhei, Abe Yoko, Suzuki Shota, Miyazato Shohei, Takara Hikaru
Department of Anesthesiology and Critical Care Medicine, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan.
Department of Human and Engineered Environmental Studies Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
Crit Care. 2025 Jan 20;29(1):36. doi: 10.1186/s13054-024-05245-y.
Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies.
A multicenter study was conducted using data collected from 718 patients with aSAH who were treated at five hospitals in Japan. A deep learning model was developed to predict outcomes based on modified Rankin Scale scores using pretherapy clinical data collected from admission to the initiation of physical therapy. The model's performance was assessed using the area under the curve, and interpretability was enhanced using SHapley Additive exPlanations (SHAP). Logistic regression analysis was also performed for further validation.
The area under the receiver operating characteristic curve of the model was 0.90, with age, World Federation of Neurosurgical Societies grade, and higher brain dysfunction identified as key predictors. SHAP analysis supported the importance of these features in the prediction model, and logistic regression analysis further confirmed the model's robustness.
The novel deep learning model demonstrated strong predictive performance in determining functional outcomes in patients with aSAH, making it a valuable tool for guiding early rehabilitation strategies.
动脉瘤性蛛网膜下腔出血（aSAH）是一种危急病症，死亡率高且康复挑战复杂。早期预测功能预后对于优化治疗策略至关重要。
开展了一项多中心研究，使用从日本五家医院接受治疗的718例aSAH患者收集的数据。开发了一种深度学习模型，基于改良Rankin量表评分，利用从入院到开始物理治疗期间收集的治疗前临床数据来预测预后。使用曲线下面积评估模型的性能，并使用SHapley加性解释（SHAP）增强可解释性。还进行了逻辑回归分析以进一步验证。
该模型的受试者操作特征曲线下面积为0.90，年龄、世界神经外科协会分级和高级脑功能障碍被确定为关键预测因素。SHAP分析支持了这些特征在预测模型中的重要性，逻辑回归分析进一步证实了该模型的稳健性。
这种新型深度学习模型在确定aSAH患者的功能预后方面表现出强大的预测性能，使其成为指导早期康复策略的有价值工具。
