Background: Establishing the etiology of community-acquired pneumonia (CAP) in children at admission is challenging. Most of the admitted children with CAP receive antibiotics. We aimed to build and validate a diagnostic tool combining clinical, analytical and radiographic features to differentiate viral from bacterial CAP, and among bacterial CAP, typical from atypical bacteria.
Methods: Design-observational, multi-center, prospective cohort study was conducted in 2 phases. Settings: 24 secondary and tertiary hospitals in Spain. Patients-A total of 495 consecutive hospitalized children between 1 month and 16 years of age with CAP were enrolled. Interventions-A score with 2 sequential steps was built (training set, 70% patients, and validation set 30%). Step 1 differentiates between viral and bacterial CAP and step 2 between typical and atypical bacterial CAP. Optimal cutoff points were selected to maximize specificity setting a high sensitivity (80%). Weights of each variable were calculated with a multivariable logistic regression. Main outcome measures-Viral or bacterial etiology.
Results: In total, 262 (53%) children (median age: 2 years, 52.3% male) had an etiologic diagnosis. In step 1, bacterial CAPs were classified with a sensitivity = 97%, a specificity = 48%, and a ROC's area under the curve = 0.81. If a patient with CAP was classified as bacterial, he/she was assessed with step 2. Typical bacteria were classified with a sensitivity = 100%, a specificity = 64% and area under the curve = 0.90. We implemented the score into a mobile app named Pneumonia Etiology Predictor, freely available at usual app stores, that provides the probability of each etiology.
Conclusions: This 2-steps tool can facilitate the physician's decision to prescribe antibiotics without compromising patient safety.
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