Cargando…
Clustering and prediction of long-term functional recovery patterns in first-time stroke patients
OBJECTIVES: 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. METHODS: This study is an interim analysis of the dataset from the Kor...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031095/ https://www.ncbi.nlm.nih.gov/pubmed/36970541 http://dx.doi.org/10.3389/fneur.2023.1130236 |
_version_ | 1784910528276070400 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Shin, Seyoung |
collection | PubMed |
description | OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-10031095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100310952023-03-23 Clustering and prediction of long-term functional recovery patterns in first-time stroke patients 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 Front Neurol Neurology OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Frontiers Media S.A. 2023-03-08 /pmc/articles/PMC10031095/ /pubmed/36970541 http://dx.doi.org/10.3389/fneur.2023.1130236 Text en Copyright © 2023 Shin, Chang, Kim, Lee, Sohn, Song, Shin, Lee, Joo, Lee, Han, Ahn, Oh, Kim, Kim and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology 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 Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title | Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title_full | Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title_fullStr | Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title_full_unstemmed | Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title_short | Clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
title_sort | clustering and prediction of long-term functional recovery patterns in first-time stroke patients |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031095/ https://www.ncbi.nlm.nih.gov/pubmed/36970541 http://dx.doi.org/10.3389/fneur.2023.1130236 |
work_keys_str_mv | AT shinseyoung clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT changwonhyuk clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT kimdeogyoung clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT leejongmin clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT sohnminkyun clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT songminkeun clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT shinyongil clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT leeyangsoo clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT joomincheol clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT leesoyoung clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT hanjunhee clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT ahnjeonghoon clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT ohgyungjae clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT kimyoungtaek clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT kimkwangsu clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients AT kimyunhee clusteringandpredictionoflongtermfunctionalrecoverypatternsinfirsttimestrokepatients |