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

Eric D McCollum, Siobhan P Brown, Evangelyn Nkwopara, Tisungane Mvalo, Susanne May and Amy Sarah Ginsburg

PLOS ONE, 2019, vol. 14, issue 6, 1-13

Abstract: 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 (R2, 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0214583

DOI: 10.1371/journal.pone.0214583

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