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Development of a prognostic risk score to aid antibiotic decision-making for children aged 2-59 months with World Health Organization fast breathing pneumonia in Malawi: An Innovative Treatments in Pneumonia (ITIP) secondary analysis

BACKGROUND: Due to increasing antimicrobial resistance in low-resource settings, strategies to rationalize antibiotic treatment of children unlikely to have a bacterial infection are needed. This study’s objective was to utilize a database of placebo treated Malawian children with World Health Organ...

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Detalles Bibliográficos
Autores principales: McCollum, Eric D., Brown, Siobhan P., Nkwopara, Evangelyn, Mvalo, Tisungane, May, Susanne, Ginsburg, Amy Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586284/
https://www.ncbi.nlm.nih.gov/pubmed/31220085
http://dx.doi.org/10.1371/journal.pone.0214583
Descripción
Sumario:BACKGROUND: Due to increasing antimicrobial resistance in low-resource settings, strategies to rationalize antibiotic treatment of children unlikely to have a bacterial infection are needed. This study’s objective was to utilize a database of placebo treated Malawian children with World Health Organization (WHO) fast breathing pneumonia to develop a prognostic risk score that could aid antibiotic decision making. METHODS: We conducted a secondary analysis of children randomized to the placebo group of the Innovative Treatments in Pneumonia (ITIP) fast breathing randomized, controlled, noninferiority trial. Participants were low-risk HIV-uninfected children 2–59 months old with WHO fast breathing pneumonia in Lilongwe, Malawi. Study endpoints were treatment failure, defined as either disease progression at any time on or before Day 4 of treatment or disease persistence on Day 4, or relapse, considered as the recurrence of pneumonia or severe disease among previously cured children between Days 5 and 14. We utilized multivariable linear regression and stepwise model selection to develop a model to predict the probability of treatment failure or relapse. RESULTS: Treatment failure or relapse occurred in 11.5% (61/526) of children included in this analysis. The final model incorporated the following predictors: heart rate terms, mid-upper arm circumference, malaria status, water source, family income, and whether or not a sibling or other child in the household received childcare outside the home. The model’s area under the receiver operating characteristic score was 0.712 (95% confidence interval 0.66, 0.78) and it explained 6.1% of the variability in predicting treatment failure or relapse (R(2), 0.061). For the model to categorize all children with treatment failure or relapse correctly, 77% of children without treatment failure or relapse would require antibiotics. CONCLUSION: The model had inadequate discrimination to be appropriate for clinical application. Different strategies will likely be required for models to perform accurately among similar pediatric populations.