Division of Intramural Research, Disorders and Stroke, National Institute of Neurological, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA.
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Med Biol Eng Comput. 2024 Aug;62(8):2343-2354. doi: 10.1007/s11517-024-03073-4. Epub 2024 Apr 5.
Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors. To properly evaluate the best predictive approaches currently available and avoid prejudice, this study employed three different prognosis prediction models including a statistical logistic regression model, commonly used clinical-based scores, and a latest high-performance ML-based XGBoost model. The study revealed that the XGBoost model outperformed other two traditional models, achieving an AUROC of 0.929 in predicting the prognosis changes of stroke patients followed for 3 months. In addition, the XGBoost model maintained remarkably high precision even when using only selected 20 most relevant clinical features compared to full clinical datasets used in the study. These selected features closely correlated with significant changes in clinical outcomes for stroke patients and showed to be effective for predicting prognosis changes after discharge, allowing physicians to make optimal decisions regarding their patients' recovery.
准确预测缺血性脑卒中患者出院后的预后对于医生规划长期医疗保健至关重要。尽管先前的研究表明，机器学习（ML）在有限的数据集下可以实现相当准确的中风预后预测，但确定与中风后预后变化相关的具体临床特征，以帮助医生和患者制定改进的康复护理计划一直具有挑战性。本研究旨在通过利用大型国家卒中登记数据库来评估各种预测模型，这些模型可以评估与相关临床因素相关的患者预后随时间的变化，从而克服这些差距。为了正确评估目前可用的最佳预测方法并避免偏见，本研究采用了三种不同的预后预测模型，包括统计逻辑回归模型、常用的临床评分和最新的高性能基于 ML 的 XGBoost 模型。研究表明，XGBoost 模型优于其他两种传统模型，在预测 3 个月随访的中风患者预后变化方面的 AUC 为 0.929。此外，与研究中使用的完整临床数据集相比，XGBoost 模型即使仅使用 20 个最相关的临床特征也能保持极高的精度。这些精选特征与中风患者临床结局的显著变化密切相关，并且对预测出院后预后变化有效，使医生能够针对患者的康复做出最佳决策。
