Cargando…
Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests
BACKGROUNDS: The timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine lear...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067264/ https://www.ncbi.nlm.nih.gov/pubmed/34702747 http://dx.doi.org/10.1136/svn-2021-001096 |
_version_ | 1784699970469756928 |
---|---|
author | Wang, Jianan Zhang, Jungen Gong, Xiaoxian Zhang, Wenhua Zhou, Ying Lou, Min |
author_facet | Wang, Jianan Zhang, Jungen Gong, Xiaoxian Zhang, Wenhua Zhou, Ying Lou, Min |
author_sort | Wang, Jianan |
collection | PubMed |
description | BACKGROUNDS: The timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine learning (ML) model to predict LVO using prehospital accessible data. METHODS: Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included. We developed eight ML models to integrate National Institutes of Health Stroke Scale (NIHSS) items with demographics, medical history and vascular risk factors to identify LVO and validate its efficiency. RESULTS: Finally, 15 365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RF), gradient boosting machine and extreme gradient boosting presented area under the curve (AUC) of 0.831 (95% CI 0.819 to 0.843), which were higher than other models, and RF presented the highest specificity (0.827). In addition, the AUC of RF was higher than other scales, and the accuracy of the model was improved by 6.4% compared with NIHSS. We also found the top five items of identifying LVO were total NIHSS score, gaze deviation, level of consciousness (LOC), LOC commands and motor left leg. CONCLUSIONS: Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data. TRIAL REGISTRATION NUMBER: NCT04487340. |
format | Online Article Text |
id | pubmed-9067264 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-90672642022-05-12 Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests Wang, Jianan Zhang, Jungen Gong, Xiaoxian Zhang, Wenhua Zhou, Ying Lou, Min Stroke Vasc Neurol Original Research BACKGROUNDS: The timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine learning (ML) model to predict LVO using prehospital accessible data. METHODS: Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included. We developed eight ML models to integrate National Institutes of Health Stroke Scale (NIHSS) items with demographics, medical history and vascular risk factors to identify LVO and validate its efficiency. RESULTS: Finally, 15 365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RF), gradient boosting machine and extreme gradient boosting presented area under the curve (AUC) of 0.831 (95% CI 0.819 to 0.843), which were higher than other models, and RF presented the highest specificity (0.827). In addition, the AUC of RF was higher than other scales, and the accuracy of the model was improved by 6.4% compared with NIHSS. We also found the top five items of identifying LVO were total NIHSS score, gaze deviation, level of consciousness (LOC), LOC commands and motor left leg. CONCLUSIONS: Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data. TRIAL REGISTRATION NUMBER: NCT04487340. BMJ Publishing Group 2021-10-26 /pmc/articles/PMC9067264/ /pubmed/34702747 http://dx.doi.org/10.1136/svn-2021-001096 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Wang, Jianan Zhang, Jungen Gong, Xiaoxian Zhang, Wenhua Zhou, Ying Lou, Min Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title | Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title_full | Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title_fullStr | Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title_full_unstemmed | Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title_short | Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
title_sort | prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067264/ https://www.ncbi.nlm.nih.gov/pubmed/34702747 http://dx.doi.org/10.1136/svn-2021-001096 |
work_keys_str_mv | AT wangjianan predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests AT zhangjungen predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests AT gongxiaoxian predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests AT zhangwenhua predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests AT zhouying predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests AT loumin predictionoflargevesselocclusionforischaemicstrokebyusingthemachinelearningmodelrandomforests |