Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP).A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis.Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions.This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.
基金：
This study is supported by the National Natural Science
Foundation of China (Nos.81960456 and 82172989
for XZG), and Natural Science Foundation of Jiangxi
Province Project (Nos. 20202BAB206053 for MJW
and 20224BAB216074 for TFY), Training Program for
Academic and Technical Leaders in Major Disciplines
in Jiangxi Province - Young Talents Project (No.
20225BCJ23024 for MJW), Postdoctoral Research
Foundation of China (No. 2022M721452 for TFY) which
play a role in supporting data collection, data processing,
and data analysis. Key Research and Development
projects in Jiangxi (number 20212BBG71012 for XZG),and Graduate Student Innovation Special Fund Project
of Jiangxi Province (YC2023-B081 for PH) play a role
in supporting model development and validation.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类|3 区医学
小类|3 区细胞生物学3 区老年医学
最新[2023]版：
大类|3 区医学
小类|3 区细胞生物学3 区老年医学
第一作者：
第一作者机构：[1]Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China.[2]Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang 330006, Jiangxi, China.[3]Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang 330006, Jiangxi, China.[4]Institute of Neuroscience, Nanchang University, Nanchang 330006, Jiangxi, China.
共同第一作者：
通讯作者：
通讯机构：[1]Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China.[2]Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang 330006, Jiangxi, China.[3]Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang 330006, Jiangxi, China.[4]Institute of Neuroscience, Nanchang University, Nanchang 330006, Jiangxi, China.
推荐引用方式(GB/T 7714)：
Shu Lei,Yan Hua,Wu Yanze,et al.Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage[J].Aging.2024,16:doi:10.18632/aging.205621.
APA：
Shu Lei,Yan Hua,Wu Yanze,Yan Tengfeng,Yang Li...&Hu Ping.(2024).Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.Aging,16,
MLA：
Shu Lei,et al."Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage".Aging 16.(2024)
