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Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm
BACKGROUND: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likel...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823366/ https://www.ncbi.nlm.nih.gov/pubmed/35145468 http://dx.doi.org/10.3389/fneur.2021.784250 |
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author | Maharjan, Jenish Ektefaie, Yasha Ryan, Logan Mataraso, Samson Barnes, Gina Shokouhi, Sepideh Green-Saxena, Abigail Calvert, Jacob Mao, Qingqing Das, Ritankar |
author_facet | Maharjan, Jenish Ektefaie, Yasha Ryan, Logan Mataraso, Samson Barnes, Gina Shokouhi, Sepideh Green-Saxena, Abigail Calvert, Jacob Mao, Qingqing Das, Ritankar |
author_sort | Maharjan, Jenish |
collection | PubMed |
description | BACKGROUND: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety. METHODS: A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke. RESULTS: After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure. CONCLUSION: MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment. |
format | Online Article Text |
id | pubmed-8823366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88233662022-02-09 Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm Maharjan, Jenish Ektefaie, Yasha Ryan, Logan Mataraso, Samson Barnes, Gina Shokouhi, Sepideh Green-Saxena, Abigail Calvert, Jacob Mao, Qingqing Das, Ritankar Front Neurol Neurology BACKGROUND: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety. METHODS: A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke. RESULTS: After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure. CONCLUSION: MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment. Frontiers Media S.A. 2022-01-25 /pmc/articles/PMC8823366/ /pubmed/35145468 http://dx.doi.org/10.3389/fneur.2021.784250 Text en Copyright © 2022 Maharjan, Ektefaie, Ryan, Mataraso, Barnes, Shokouhi, Green-Saxena, Calvert, Mao and Das. 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 Maharjan, Jenish Ektefaie, Yasha Ryan, Logan Mataraso, Samson Barnes, Gina Shokouhi, Sepideh Green-Saxena, Abigail Calvert, Jacob Mao, Qingqing Das, Ritankar Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title | Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title_full | Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title_fullStr | Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title_full_unstemmed | Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title_short | Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm |
title_sort | enriching the study population for ischemic stroke therapeutic trials using a machine learning algorithm |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823366/ https://www.ncbi.nlm.nih.gov/pubmed/35145468 http://dx.doi.org/10.3389/fneur.2021.784250 |
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