Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China.
China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae487.
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventions. We developed a predictive model integrating genetic, environmental, and clinical factors using data from 7819 IS patients in the Third China National Stroke Registry. Employing an 80:20 split, we randomly divided the dataset into development and internal validation cohorts. The discrimination and calibration performance of models were evaluated using the area under the receiver operating characteristic curves (AUC) for discrimination and Brier score with calibration curve in the internal validation cohort. We conducted genome-wide association studies (GWAS) in the development cohort, identifying rs11109607 (ANKS1B) as the most significant variant associated with IS functional outcome. We employed principal component analysis to reduce dimensionality on the top 100 significant variants identified by the GWAS, incorporating them as genetic factors in the predictive model. We employed a machine learning algorithm capable of identifying nonlinear relationships to establish predictive models for IS patient functional outcome. The optimal model was the XGBoost model, which outperformed the logistic regression model (AUC 0.818 versus 0.756, P < .05) and significantly improved reclassification efficiency. Our study innovatively incorporated genetic, environmental, and clinical factors for predicting the IS functional outcome in East Asian populations, thereby offering novel insights into IS functional outcome.
缺血性脑卒中（IS）是导致成年人残疾的主要原因之一，可严重影响患者的生活质量。准确预测 IS 的功能结局对于精确的风险分层和有效的治疗干预至关重要。我们使用来自第三中国国家脑卒中登记研究的 7819 例 IS 患者的数据，开发了一个整合遗传、环境和临床因素的预测模型。我们采用 80:20 的比例将数据集随机分为开发和内部验证队列。我们使用内部验证队列中的接收者操作特征曲线（ROC）下面积（AUC）来评估模型的区分度和校准性能，以及 Brier 评分和校准曲线来评估模型的校准性能。我们在开发队列中进行了全基因组关联研究（GWAS），确定 rs11109607（ANKS1B）是与 IS 功能结局最显著相关的变体。我们采用主成分分析对 GWAS 中确定的前 100 个显著变体进行降维处理，将它们作为遗传因素纳入预测模型。我们采用能够识别非线性关系的机器学习算法来建立 IS 患者功能结局的预测模型。最优模型是 XGBoost 模型，其表现优于逻辑回归模型（AUC 0.818 与 0.756，P < .05），并显著提高了再分类效率。我们的研究创新性地整合了遗传、环境和临床因素，用于预测东亚人群的 IS 功能结局，从而为 IS 功能结局提供了新的见解。
