Shin Seyoung, Chang Won Hyuk, Kim Deog Young, Lee Jongmin, Sohn Min Kyun, Song Min-Keun, Shin Yong-Il, Lee Yang-Soo, Joo Min Cheol, Lee So Young, Han Junhee, Ahn Jeonghoon, Oh Gyung-Jae, Kim Young-Taek, Kim Kwangsu, Kim Yun-Hee
Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Front Neurol. 2023 Mar 8;14:1130236. doi: 10.3389/fneur.2023.1130236. eCollection 2023.
The purpose of this study was to cluster long-term multifaceted functional recovery patterns and to establish prediction models for functional outcome in first-time stroke patients using unsupervised machine learning.
This study is an interim analysis of the dataset from the Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO), a long-term, prospective, multicenter cohort study of first-time stroke patients. The KOSCO screened 10,636 first-time stroke patients admitted to nine representative hospitals in Korea during a three-year recruitment period, and 7,858 patients agreed to enroll. Early clinical and demographic features of stroke patients and six multifaceted functional assessment scores measured from 7 days to 24 months after stroke onset were used as input variables. K-means clustering analysis was performed, and prediction models were generated and validated using machine learning.
A total of 5,534 stroke patients (4,388 ischemic and 1,146 hemorrhagic; mean age 63·31 ± 12·86; 3,253 [58.78%] male) completed functional assessments 24 months after stroke onset. Through K-means clustering, ischemic stroke (IS) patients were clustered into five groups and hemorrhagic stroke (HS) patients into four groups. Each cluster had distinct clinical characteristics and functional recovery patterns. The final prediction models for IS and HS patients achieved relatively high prediction accuracies of 0.926 and 0.887, respectively.
The longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.
本研究旨在对首次卒中患者的长期多方面功能恢复模式进行聚类，并使用无监督机器学习建立功能结局预测模型。
本研究是对韩国卒中功能与康复队列（KOSCO）数据集的中期分析，这是一项针对首次卒中患者的长期、前瞻性、多中心队列研究。KOSCO在三年的招募期内筛查了韩国九家代表性医院收治的10636例首次卒中患者，7858例患者同意入组。卒中患者的早期临床和人口统计学特征以及卒中发病后7天至24个月测量的六个多方面功能评估分数被用作输入变量。进行了K均值聚类分析，并使用机器学习生成和验证了预测模型。
共有5534例卒中患者（4388例缺血性卒中和1146例出血性卒中；平均年龄63.31±12.86；3253例[58.78%]为男性）在卒中发病后24个月完成了功能评估。通过K均值聚类，缺血性卒中（IS）患者被分为五组，出血性卒中（HS）患者被分为四组。每个聚类都有独特的临床特征和功能恢复模式。IS和HS患者的最终预测模型分别达到了相对较高的预测准确率，分别为0.926和0.887。
首次卒中患者的纵向、多维度功能评估数据成功聚类，预测模型显示出相对较好的准确率。早期识别和预测长期功能结局将有助于临床医生制定个性化的治疗策略。
