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BACKGROUNDLower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODSWe used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTSThe host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSIONLower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDINGSupport for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.

Background Risk factors for MIS-C, a rare but serious hyperinflammatory syndrome associated with SARS-CoV-2 infection, remain unclear. We evaluated household, clinical, and environmental risk factors potentially associated with MIS-C. Methods This investigation included MIS-C cases hospitalized in 14 US pediatric hospitals in 2021. Outpatient controls were frequency-matched to case-patients by age group and site and had a positive SARS-CoV-2 viral test within 3 months of the admission of their matched MIS-C case (Figure 1). We conducted telephone surveys with caregivers and evaluated potential risk factors using mixed effects multivariable logistic regression, including site as a random effect. We queried regarding exposures within the month before hospitalization for MIS-C cases or the month after a positive COVID-19 test for controls. Figure.Patient enrollment timeline. Enrollment scheme for MIS-C case-patients and SARS-CoV-2-positive outpatient controls. MIS-C case-patients were identified through hospital electronic medical records, while two outpatient controls per case were identified through registries of outpatient SARS-CoV-2 testing logs at facilities affiliated with that medical center. Caregivers of outpatient controls were interviewed at least four weeks after their positive test to ensure they did not develop MIS-C after their infection. Results We compared 275 MIS-C case-patients with 494 outpatient SARS-CoV-2-positive controls. Race, ethnicity and social vulnerability indices were similar. MIS-C was more likely among persons who resided in households with >1 resident per room (aOR=1.6, 95% CI: 1.1–2.2), attended a large (≥10 people) event with little to no mask-wearing (aOR=2.2, 95% CI: 1.4–3.5), used public transportation (aOR=1.6, 95% CI: 1.2–2.1), attended school >2 days per week with little to no mask wearing (aOR=2.1, 95% CI: 1.0–4.4), or had a household member test positive for COVID-19 (aOR=2.1, 95% CI: 1.3–3.3). MIS-C was less likely among children with comorbidities (aOR=0.5, 95% CI: 0.3–0.9) and in those who had >1 positive SARS-CoV-2 test at least 1 month apart (aOR=0.4, 95% CI: 0.2–0.6). MIS-C was not associated with a medical history of recurrent infections or family history of underlying rheumatologic disease. Conclusion Household crowding, limited masking at large indoor events or schools and use of public transportation were associated with increased likelihood of developing MIS-C after SARS-CoV-2 infection. In contrast, decreased likelihood of MIS-C was associated with having >1 SARS-CoV-2 positive test separated by at least a month. Our data suggest that additional studies are needed to determine if viral load, and/or recurrent infections in the month prior to MIS-C contribute to MIS-C risk. Medical and family history were not associated with MIS-C in our analysis. Disclosures Natasha B. Halasa, MD, Quidel: Grant/Research Support|Quidel: equipment donation|Sanofi: Grant/Research Support|Sanofi: HAI testing and vaccine donation Mark Hall, MD, Abbvie: Service on a Data Safety Monitoring Board|Kiadis: Licensing income unrelated to the current submission Mary A. Staat, MD, MPH, Centers for Disease Control and Prevention: Grant/Research Support|Cepheid: Grant/Research Support|National Institute of Health: Grant/Research Support|Uptodate: Royalties Pia S. Pannaraj, MD, MPH, AstraZeneca: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi-Pasteur: Advisor/Consultant|Seqirus: Advisor/Consultant Charlotte V. Hobbs, MD, Biofire (Biomerieux): Advisor/Consultant.