Predicting mortality among hospitalized children with respiratory illness in Western Kenya, 2009-2012.
<h4>Background</h4>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...
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2014
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oai:doaj.org-article:3bcbcbae6496481e9f74027db0c4f6ff2021-11-18T08:26:16ZPredicting mortality among hospitalized children with respiratory illness in Western Kenya, 2009-2012.1932-620310.1371/journal.pone.0092968https://doaj.org/article/3bcbcbae6496481e9f74027db0c4f6ff2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24667695/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.Gideon O EmukuleMeredith McMorrowChulie UlloaSammy KhagayiHenry N NjugunaDeron BurtonJoel M MontgomeryPhilip MuthokaMark A KatzRobert F BreimanJoshua A MottPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e92968 (2014) |
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Medicine R Science Q Gideon O Emukule Meredith McMorrow Chulie Ulloa Sammy Khagayi Henry N Njuguna Deron Burton Joel M Montgomery Philip Muthoka Mark A Katz Robert F Breiman Joshua A Mott Predicting mortality among hospitalized children with respiratory illness in Western Kenya, 2009-2012. |
| description |
<h4>Background</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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 |
article |
| author |
Gideon O Emukule Meredith McMorrow Chulie Ulloa Sammy Khagayi Henry N Njuguna Deron Burton Joel M Montgomery Philip Muthoka Mark A Katz Robert F Breiman Joshua A Mott |
| author_facet |
Gideon O Emukule Meredith McMorrow Chulie Ulloa Sammy Khagayi Henry N Njuguna Deron Burton Joel M Montgomery Philip Muthoka Mark A Katz Robert F Breiman Joshua A Mott |
| author_sort |
Gideon O Emukule |
| title |
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_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_sort |
predicting mortality among hospitalized children with respiratory illness in western kenya, 2009-2012. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2014 |
| url |
https://doaj.org/article/3bcbcbae6496481e9f74027db0c4f6ff |
| work_keys_str_mv |
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