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MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model
Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of pat...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073544/ https://www.ncbi.nlm.nih.gov/pubmed/32012659 http://dx.doi.org/10.3390/jcm9020343 |
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author | Kia, Arash Timsina, Prem Joshi, Himanshu N. Klang, Eyal Gupta, Rohit R. Freeman, Robert M. Reich, David L Tomlinson, Max S Dudley, Joel T Kohli-Seth, Roopa Mazumdar, Madhu Levin, Matthew A |
author_facet | Kia, Arash Timsina, Prem Joshi, Himanshu N. Klang, Eyal Gupta, Rohit R. Freeman, Robert M. Reich, David L Tomlinson, Max S Dudley, Joel T Kohli-Seth, Roopa Mazumdar, Madhu Levin, Matthew A |
author_sort | Kia, Arash |
collection | PubMed |
description | Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions. |
format | Online Article Text |
id | pubmed-7073544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70735442020-03-20 MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model Kia, Arash Timsina, Prem Joshi, Himanshu N. Klang, Eyal Gupta, Rohit R. Freeman, Robert M. Reich, David L Tomlinson, Max S Dudley, Joel T Kohli-Seth, Roopa Mazumdar, Madhu Levin, Matthew A J Clin Med Article Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions. MDPI 2020-01-27 /pmc/articles/PMC7073544/ /pubmed/32012659 http://dx.doi.org/10.3390/jcm9020343 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kia, Arash Timsina, Prem Joshi, Himanshu N. Klang, Eyal Gupta, Rohit R. Freeman, Robert M. Reich, David L Tomlinson, Max S Dudley, Joel T Kohli-Seth, Roopa Mazumdar, Madhu Levin, Matthew A MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title | MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title_full | MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title_fullStr | MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title_full_unstemmed | MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title_short | MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model |
title_sort | mews++: enhancing the prediction of clinical deterioration in admitted patients through a machine learning model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073544/ https://www.ncbi.nlm.nih.gov/pubmed/32012659 http://dx.doi.org/10.3390/jcm9020343 |
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