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The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients
OBJECTIVES: Some medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome for patients. Study results may be used to admit patients into planned tiered care un...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401845/ https://www.ncbi.nlm.nih.gov/pubmed/25869679 http://dx.doi.org/10.1136/bmjopen-2014-005501 |
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author | Roberts, Daniel Patrick, Ward Mojica, Julie Ostryzniuk, Patricia Patrick, Margaret MacKnight, Chris Kraut, Allen Shafer, Leigh Anne |
author_facet | Roberts, Daniel Patrick, Ward Mojica, Julie Ostryzniuk, Patricia Patrick, Margaret MacKnight, Chris Kraut, Allen Shafer, Leigh Anne |
author_sort | Roberts, Daniel |
collection | PubMed |
description | OBJECTIVES: Some medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome for patients. Study results may be used to admit patients into planned tiered care units. Adverse outcome comprised death or cardiac arrest during the first 30 days of hospitalisation, or transfer to intensive care within the first 48 h of admission. SETTING: The study took place at two tertiary teaching hospitals and two community hospitals in Winnipeg, Manitoba, Canada. PARTICIPANTS: We analysed data from 4883 consecutive admissions at a tertiary teaching hospital to construct the Early Prediction of Adverse Hospital Outcome for Medical Patients (ALERT) model using logistic regression. Robustness of the model was assessed through validation performed across four hospitals over two time periods, including 65 640 consecutive admissions. OUTCOME: Receiver-operating characteristic curves (ROC) and sensitivity and specificity analyses were used to assess the usefulness of the model. RESULTS: 9.3% of admitted patients experienced adverse outcomes. The final model included gender, age, Charlson Comorbidity Index, Activities of Daily Living Score, Glasgow Coma Score, systolic blood pressure, respiratory rate, heart rate and white cell count. The model was discriminative (ROC=0.83) in predicting adverse outcome. ALERT accurately predicted 75% of the adverse outcomes (sensitivity) and 75% of the non-adverse outcomes (specificity). Applying the same model to each validation hospital and time period produced similar accuracy and discrimination to that in the development hospital. CONCLUSIONS: Used during initial assessment of patients admitted to general medical wards, the ALERT scale may complement other assessment measures to better screen patients. Those considered as higher risk by the ALERT scale may then be provided more effective care from action such as planned tiered care units. |
format | Online Article Text |
id | pubmed-4401845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-44018452015-04-29 The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients Roberts, Daniel Patrick, Ward Mojica, Julie Ostryzniuk, Patricia Patrick, Margaret MacKnight, Chris Kraut, Allen Shafer, Leigh Anne BMJ Open General practice / Family practice OBJECTIVES: Some medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome for patients. Study results may be used to admit patients into planned tiered care units. Adverse outcome comprised death or cardiac arrest during the first 30 days of hospitalisation, or transfer to intensive care within the first 48 h of admission. SETTING: The study took place at two tertiary teaching hospitals and two community hospitals in Winnipeg, Manitoba, Canada. PARTICIPANTS: We analysed data from 4883 consecutive admissions at a tertiary teaching hospital to construct the Early Prediction of Adverse Hospital Outcome for Medical Patients (ALERT) model using logistic regression. Robustness of the model was assessed through validation performed across four hospitals over two time periods, including 65 640 consecutive admissions. OUTCOME: Receiver-operating characteristic curves (ROC) and sensitivity and specificity analyses were used to assess the usefulness of the model. RESULTS: 9.3% of admitted patients experienced adverse outcomes. The final model included gender, age, Charlson Comorbidity Index, Activities of Daily Living Score, Glasgow Coma Score, systolic blood pressure, respiratory rate, heart rate and white cell count. The model was discriminative (ROC=0.83) in predicting adverse outcome. ALERT accurately predicted 75% of the adverse outcomes (sensitivity) and 75% of the non-adverse outcomes (specificity). Applying the same model to each validation hospital and time period produced similar accuracy and discrimination to that in the development hospital. CONCLUSIONS: Used during initial assessment of patients admitted to general medical wards, the ALERT scale may complement other assessment measures to better screen patients. Those considered as higher risk by the ALERT scale may then be provided more effective care from action such as planned tiered care units. BMJ Publishing Group 2015-04-13 /pmc/articles/PMC4401845/ /pubmed/25869679 http://dx.doi.org/10.1136/bmjopen-2014-005501 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions 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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
spellingShingle | General practice / Family practice Roberts, Daniel Patrick, Ward Mojica, Julie Ostryzniuk, Patricia Patrick, Margaret MacKnight, Chris Kraut, Allen Shafer, Leigh Anne The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title | The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title_full | The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title_fullStr | The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title_full_unstemmed | The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title_short | The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients |
title_sort | alert scale: an observational study of early prediction of adverse hospital outcome for medical patients |
topic | General practice / Family practice |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401845/ https://www.ncbi.nlm.nih.gov/pubmed/25869679 http://dx.doi.org/10.1136/bmjopen-2014-005501 |
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