RESUMO

BACKGROUND: Penicillin (PCN) allergy label, reported in approximately 5% of children, influences antibiotic choice and prolongs hospital stay. To our knowledge, the impact of PCN allergy label on clinical outcomes of pneumonia in children is not well characterized. OBJECTIVES: To investigate the impact of PCN allergy label on clinical outcomes of pneumonia in children. METHODS: In this propensity score-matched cohort study, we used the TriNetX research network, a population-based database, to compare the 30-day risk of hospitalization, need for intensive level of care, and acute respiratory failure from pneumonia between pediatric patients (aged 1-17 years) with and without a PCN allergy label after matching the 2 cohorts for demographic and medical comorbidities. Antibiotic prescription patterns were also contrasted. RESULTS: When comparing 3793 pediatric patients with pneumonia labeled with a PCN allergy with matched children without a PCN allergy label, PCN allergy label was associated with a higher risk of hospitalization (relative risk [RR], 1.15; 95% confidence interval [CI], 1.07-1.23), acute respiratory failure (RR, 1.27; 95% CI, 1.17-1.39), and need for intensive level of care (RR, 1.46; 95% CI, 1.15-1.84). PCN allergy label resulted in overutilization of broader-spectrum antibiotics and increased complications including cutaneous drug reactions (RR, 2.43; 95% CI, 1.31-4.52) and Clostridioides difficile infection (RR, 2.25; 95% CI, 1.14-4.44). CONCLUSION: Children with a PCN allergy label are more likely to be hospitalized, receive broader-spectrum antibiotics, and develop acute respiratory failure from pneumonia. Delabeling may offer a way to lessen morbidity from pneumonia in children.

Assuntos

Hipersensibilidade a Drogas , Hipersensibilidade , Pneumonia , Insuficiência Respiratória , Humanos , Criança , Estudos de Coortes , Penicilinas/efeitos adversos , Antibacterianos/efeitos adversos , Hipersensibilidade a Drogas/etiologia , Hipersensibilidade/tratamento farmacológico , Pneumonia/tratamento farmacológico , Pneumonia/epidemiologia , Insuficiência Respiratória/complicações

RESUMO

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.