Abstract
BACKGROUND: Pulse oximeters are not routinely available in outpatient clinics in low- and middle-income countries. We derived clinical scores to identify hypoxemic child pneumonia. METHODS: This was a retrospective pooled analysis of two outpatient datasets of 3-35 month olds with World Health Organization (WHO)-defined pneumonia in Bangladesh and Malawi. We constructed, internally validated, and compared fit & discrimination of four models predicting SpO(2 )< 93% and <90%: (1) Integrated Management of Childhood Illness guidelines, (2) WHO-composite guidelines, (3) Independent variable least absolute shrinkage and selection operator (LASSO); (4) Composite variable LASSO. RESULTS: 12,712 observations were included. The independent and composite LASSO models discriminated moderately (both C-statistic 0.77) between children with a SpO(2 )< 93% and ≥94%; model predictive capacities remained moderate after adjusting for potential overfitting (C-statistic 0.74 and 0.75). The IMCI and WHO-composite models had poorer discrimination (C-statistic 0.56 and 0.68) and identified 20.6% and 56.8% of SpO(2 )< 93% cases. The highest score stratum of the independent and composite LASSO models identified 46.7% and 49.0% of SpO(2 )< 93% cases. Both LASSO models had similar performance for a SpO(2 )< 90%. CONCLUSIONS: In the absence of pulse oximeters, both LASSO models better identified outpatient hypoxemic pneumonia cases than the WHO guidelines. Score external validation and implementation are needed.