A Tool to Distinguish Viral From Bacterial Pneumonia. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Tagarro, Alfredo; Moraleda, Cinta; Domínguez-Rodríguez, Sara; Rodríguez, Mario; Martín, María Dolores; Herreros, María Luisa; Jensen, Julia; López, Agustín; Galán, Juan Carlos; Otheo, Enrique
• Is Part Of: Pediatric infectious disease journal. Volume 41:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: community-acquired pneumonia -- typical bacteria -- atypical bacteria -- viral pneumonia -- antibiotic stewardship; Communicable diseases in children -- Periodicals; Infection in children -- Periodicals; 618.929
• DOI: 10.1097/INF.0000000000003340
• Languages: English
• ISSNs: 0891-3668

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text.