tetano
Editor, Senior Moderator
PLoS One. 2014 Mar 25;9(3):e92968. doi: 10.1371/journal.pone.0092968. eCollection 2014.
Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009-2012.
Emukule GO1, McMorrow M2, Ulloa C3, Khagayi S1, Njuguna HN1, Burton D1, Montgomery JM1, Muthoka P4, Katz MA5, Breiman RF6, Mott JA1.
Author information
Abstract
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.
PMID:
24667695
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24667695
Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009-2012.
Emukule GO1, McMorrow M2, Ulloa C3, Khagayi S1, Njuguna HN1, Burton D1, Montgomery JM1, Muthoka P4, Katz MA5, Breiman RF6, Mott JA1.
Author information
Abstract
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.
PMID:
24667695
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24667695