Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Accurate and reliable guidelines for referral of children from resource-limited primary care settings are lacking. We identified three practicable paediatric severity scores (the Liverpool quick Sequential Organ Failure Assessment (LqSOFA), the quick Pediatric Logistic Organ Dysfunction-2, and the modified Systemic Inflammatory Response Syndrome) and externally validated their performance in young children presenting with acute respiratory infections (ARIs) to a primary care clinic located within a refugee camp on the Thailand-Myanmar border. This secondary analysis of data from a longitudinal birth cohort study consisted of 3010 ARI presentations in children aged ≤ 24 months. The primary outcome was receipt of supplemental oxygen. We externally validated the discrimination, calibration, and net-benefit of the scores, and quantified gains in performance that might be expected if they were deployed as simple clinical prediction models, and updated to include nutritional status and respiratory distress. 104/3,010 (3.5%) presentations met the primary outcome. The LqSOFA score demonstrated the best discrimination (AUC 0.84; 95% CI 0.79-0.89) and achieved a sensitivity and specificity > 0.80. Converting the scores into clinical prediction models improved performance, resulting in ~ 20% fewer unnecessary referrals and ~ 30-50% fewer children incorrectly managed in the community. The LqSOFA score is a promising triage tool for young children presenting with ARIs in resource-limited primary care settings. Where feasible, deploying the score as a simple clinical prediction model might enable more accurate and nuanced risk stratification, increasing applicability across a wider range of contexts.

Original publication

DOI

10.1038/s41598-023-45746-4

Type

Journal article

Journal

Sci Rep

Publication Date

03/11/2023

Volume

13

Keywords

Humans, Child, Child, Preschool, Cohort Studies, Models, Statistical, Prognosis, Respiratory Tract Infections, Referral and Consultation, Primary Health Care