Building a Prediction Model for Radiographically Confirmed Pneumonia in Peruvian Children: From Symptoms to Imaging

构建秘鲁儿童放射学确诊肺炎的预测模型:从症状到影像学

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Abstract

BACKGROUND: Community-acquired pneumonia remains the leading cause of death in children worldwide, and current diagnostic guidelines in resource-poor settings are neither sensitive nor specific. We sought to determine the ability to correctly diagnose radiographically confirmed clinical pneumonia when diagnostics tools were added to clinical signs and symptoms in a cohort of children with acute respiratory illnesses in Peru. METHODS: Children < 5 years of age with an acute respiratory illness presenting to a tertiary hospital in Lima, Peru, were enrolled. The ability to predict radiographically confirmed clinical pneumonia was assessed using logistic regression under four additive scenarios: clinical signs and symptoms only, addition of lung auscultation, addition of oxyhemoglobin saturation (Spo(2)), and addition of lung ultrasound. RESULTS: Of 832 children (mean age, 21.3 months; 59% boys), 453 (54.6%) had clinical pneumonia and 221 (26.6%) were radiographically confirmed. Children with radiographically confirmed clinical pneumonia had lower average Spo(2) than those without (95.9% vs 96.6%, respectively; P < .01). The ability to correctly identify radiographically confirmed clinical pneumonia using clinical signs and symptoms was limited (area under the curve [AUC] = 0.62; 95% CI, 0.58-0.67) with a sensitivity of 66% (95% CI, 59%-73%) and specificity of 53% (95% CI, 49%-57%). The addition of lung auscultation improved classification (AUC = 0.73; 95% CI, 0.69-0.77) with a sensitivity of 75% (95% CI, 69%-81%) and specificity of 53% (95% CI, 49%-57%) for the presence of crackles. In contrast, the addition of Spo(2) did not improve classification (AUC = 0.73; 95% CI, 0.69-0.77) with a sensitivity of 40% (95% CI, 33%-47%) and specificity of 72% (95% CI, 68%-75%) for an Spo(2) ≤ 92%. Adding consolidation on lung ultrasound was associated with the largest improvement in classification (AUC = 0.85; 95% CI, 0.82-0.89) with a sensitivity of 55% (95% CI, 48%-63%) and specificity of 95% (95% CI, 93%-97%). CONCLUSIONS: The addition of lung ultrasound and auscultation to clinical signs and symptoms improved the ability to correctly classify radiographically confirmed clinical pneumonia. Implementation of auscultation- and ultrasound-based diagnostic tools can be considered to improve diagnostic yield of pneumonia in resource-poor settings.

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