Predicting illness progression for children with lower respiratory infections in primary care: a prospective cohort and observational study

Abstract

Background Antibiotics are commonly prescribed for children with lower respiratory tract infections (LRTIs), fuelling antibiotic resistance, and there are few prognostic tools available to inform management. Aim To externally validate an existing prognostic model (STARWAVe) to identify children at low risk of illness progression, and if model performance was limited to develop a new internally validated prognostic model. Design and setting Prospective cohort study with a nested trial in a primary care setting. Method Children aged 6 months to 12 years presenting with uncomplicated LRTI were included in the cohort. Children were randomised to receive amoxicillin 50 mg/kg per day for 7 days or placebo, or if not randomised they participated in a parallel observational study to maximise generalisability. Baseline clinical data were used to predict adverse outcome (illness progression requiring hospital assessment). Results A total of 758 children participated (n= 432 trial, n= 326 observational). For predicting illness progression the STARWAVe prognostic model had moderate performance (area under the receiver operating characteristic [AUROC] 0.66, 95% confidence interval [CI] = 0.50 to 0.77), but a new, internally validated model (seven items: baseline severity; respiratory rate; duration of prior illness; oxygen saturation; sputum or a rattly chest; passing urine less often; and diarrhoea) had good discrimination (bootstrapped AUROC 0.83, 95% CI = 0.74 to 0.92) and calibration. A three-item model (respiratory rate; oxygen saturation; and sputum or a rattly chest) also performed well (AUROC 0.81, 95% CI = 0.70 to 0.91), as did a score (ranging from 19 to 102) derived from coefficients of the model (AUROC 0.78, 95% CI = 0.67 to 0.88): a score of <70 classified 89% (n= 600/674) of children having a low risk (<5%) of progression of illness. Conclusion A simple three-item prognostic score could be useful as a tool to identify children with LRTI who are at low risk of an adverse outcome and to guide clinical management.

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Identifiers

ORCID for Paul Little: orcid.org/0000-0003-3664-1873

ORCID for Taeko Becque: orcid.org/0000-0002-0362-3794

ORCID for Nick A. Francis: orcid.org/0000-0001-8939-7312

ORCID for Beth Stuart: orcid.org/0000-0001-5432-7437

ORCID for Michael Moore: orcid.org/0000-0002-5127-4509

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