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External validation and updating of clinical severity scores to guide referral of young children with acute respiratory infections in resource-limited primary care settings

Chandna, A.; Mwandigha, L.; Koshiaris, C.; Limmathurotsakul, D.; Nosten, F.; Lubell, Y.; Perera-Salazar, R.; Turner, C.; Turner, P.

2022-12-07 pediatrics
10.1101/2022.12.06.22283016 medRxiv
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BackgroundAccurate and reliable guidelines for referral of children from resource-limited primary care settings are lacking. We identified three practicable paediatric severity scores (Liverpool quick Sequential Organ Failure Assessment [LqSOFA], quick Pediatric Logistic Organ Dysfunction-2 [qPELOD-2], and the modified Systemic Inflammatory Response Syndrome [mSIRS]) and externally validated their performance in young children presenting with acute respiratory infections to a primary care clinic located within a refugee camp on the Thailand-Myanmar border. MethodsThis secondary analysis of data from a longitudinal birth cohort study consisted of 3,010 acute respiratory infections 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. Results104/3,010 (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 [~]30-60% fewer children incorrectly managed in the community. ConclusionsThe LqSOFA score is a promising triage tool for young children presenting with acute respiratory infections 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.

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