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Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012
BACKGROUND: Pediatric respiratory disease is a major cause of morbidity and mortality in the developing world. We evaluated a modified respiratory index of severity in children (mRISC) scoring system as a standard tool to identify children at greater risk of death from respiratory illness in Kenya....
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3965502/ https://www.ncbi.nlm.nih.gov/pubmed/24667695 http://dx.doi.org/10.1371/journal.pone.0092968 |
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author | Emukule, Gideon O. McMorrow, Meredith Ulloa, Chulie Khagayi, Sammy Njuguna, Henry N. Burton, Deron Montgomery, Joel M. Muthoka, Philip Katz, Mark A. Breiman, Robert F. Mott, Joshua A. |
author_facet | Emukule, Gideon O. McMorrow, Meredith Ulloa, Chulie Khagayi, Sammy Njuguna, Henry N. Burton, Deron Montgomery, Joel M. Muthoka, Philip Katz, Mark A. Breiman, Robert F. Mott, Joshua A. |
author_sort | Emukule, Gideon O. |
collection | PubMed |
description | BACKGROUND: Pediatric respiratory disease is a major cause of morbidity and mortality in the developing world. We evaluated a modified respiratory index of severity in children (mRISC) scoring system as a standard tool to identify children at greater risk of death from respiratory illness in Kenya. MATERIALS AND METHODS: We analyzed data from children <5 years old who were hospitalized with respiratory illness at Siaya District Hospital from 2009–2012. We used a multivariable logistic regression model to identify patient characteristics predictive for in-hospital mortality. Model discrimination was evaluated using the concordance statistic. Using bootstrap samples, we re-estimated the coefficients and the optimism of the model. The mRISC score for each child was developed by adding up the points assigned to each factor associated with mortality based on the coefficients in the multivariable model. RESULTS: We analyzed data from 3,581 children hospitalized with respiratory illness; including 218 (6%) who died. Low weight-for-age [adjusted odds ratio (aOR) = 2.1; 95% CI 1.3–3.2], very low weight-for-age (aOR = 3.8; 95% CI 2.7–5.4), caretaker-reported history of unconsciousness (aOR = 2.3; 95% CI 1.6–3.4), inability to drink or breastfeed (aOR = 1.8; 95% CI 1.2–2.8), chest wall in-drawing (aOR = 2.2; 95% CI 1.5–3.1), and being not fully conscious on physical exam (aOR = 8.0; 95% CI 5.1–12.6) were independently associated with mortality. The positive predictive value for mortality increased with increasing mRISC scores. CONCLUSIONS: A modified RISC scoring system based on a set of easily measurable clinical features at admission was able to identify children at greater risk of death from respiratory illness in Kenya. |
format | Online Article Text |
id | pubmed-3965502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39655022014-03-27 Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 Emukule, Gideon O. McMorrow, Meredith Ulloa, Chulie Khagayi, Sammy Njuguna, Henry N. Burton, Deron Montgomery, Joel M. Muthoka, Philip Katz, Mark A. Breiman, Robert F. Mott, Joshua A. PLoS One Research Article BACKGROUND: Pediatric respiratory disease is a major cause of morbidity and mortality in the developing world. We evaluated a modified respiratory index of severity in children (mRISC) scoring system as a standard tool to identify children at greater risk of death from respiratory illness in Kenya. MATERIALS AND METHODS: We analyzed data from children <5 years old who were hospitalized with respiratory illness at Siaya District Hospital from 2009–2012. We used a multivariable logistic regression model to identify patient characteristics predictive for in-hospital mortality. Model discrimination was evaluated using the concordance statistic. Using bootstrap samples, we re-estimated the coefficients and the optimism of the model. The mRISC score for each child was developed by adding up the points assigned to each factor associated with mortality based on the coefficients in the multivariable model. RESULTS: We analyzed data from 3,581 children hospitalized with respiratory illness; including 218 (6%) who died. Low weight-for-age [adjusted odds ratio (aOR) = 2.1; 95% CI 1.3–3.2], very low weight-for-age (aOR = 3.8; 95% CI 2.7–5.4), caretaker-reported history of unconsciousness (aOR = 2.3; 95% CI 1.6–3.4), inability to drink or breastfeed (aOR = 1.8; 95% CI 1.2–2.8), chest wall in-drawing (aOR = 2.2; 95% CI 1.5–3.1), and being not fully conscious on physical exam (aOR = 8.0; 95% CI 5.1–12.6) were independently associated with mortality. The positive predictive value for mortality increased with increasing mRISC scores. CONCLUSIONS: A modified RISC scoring system based on a set of easily measurable clinical features at admission was able to identify children at greater risk of death from respiratory illness in Kenya. Public Library of Science 2014-03-25 /pmc/articles/PMC3965502/ /pubmed/24667695 http://dx.doi.org/10.1371/journal.pone.0092968 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Emukule, Gideon O. McMorrow, Meredith Ulloa, Chulie Khagayi, Sammy Njuguna, Henry N. Burton, Deron Montgomery, Joel M. Muthoka, Philip Katz, Mark A. Breiman, Robert F. Mott, Joshua A. Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title | Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title_full | Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title_fullStr | Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title_full_unstemmed | Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title_short | Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012 |
title_sort | predicting mortality among hospitalized children with respiratory illness in western kenya, 2009–2012 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3965502/ https://www.ncbi.nlm.nih.gov/pubmed/24667695 http://dx.doi.org/10.1371/journal.pone.0092968 |
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