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Mick E, Tsitsiklis A, Kamm J, Kalantar KL, Caldera S, Lyden A, Tan M, Detweiler AM, Neff N, Osborne CM, Williamson KM, Soesanto V, Leroue M, Maddux AB, Simões EA, Carpenter TC, Wagner BD, DeRisi JL, Ambroggio L, Mourani PM, Langelier CR. Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children. J Clin Invest 2023; 133:e165904. [PMID: 37009900 PMCID: PMC10065066 DOI: 10.1172/jci165904] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/02/2023] [Indexed: 04/04/2023] Open
Abstract
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.
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Affiliation(s)
- Eran Mick
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, and
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Alexandra Tsitsiklis
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Jack Kamm
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | | | - Saharai Caldera
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Amy Lyden
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Michelle Tan
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | | | - Norma Neff
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Christina M. Osborne
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Kayla M. Williamson
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Victoria Soesanto
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Matthew Leroue
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Aline B. Maddux
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Eric A.F. Simões
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Todd C. Carpenter
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Brandie D. Wagner
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Joseph L. DeRisi
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA
| | - Lilliam Ambroggio
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Peter M. Mourani
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children’s Research Institute, Little Rock, Arkansas, USA
| | - Charles R. Langelier
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
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Zambrano LD, Wu MJ, Martin LM, Malloch L, Newhams MM, Son MB, Sanders C, Patterson K, Halasa NB, Fitzgerald JC, Leroue M, Hall M, Irby K, Rowan CM, Wellnitz K, Loftis LL, Bradford TT, Staat MA, Babbit C, Carroll CL, Pannaraj PS, Kong M, Chou J, Patel MM, Randolph AG, Campbell AP, Hobbs CV. 237. A Case-Control Study Investigating Household, Community, and Clinical Risk Factors Associated with Multisystem Inflammatory Syndrome in Children (MIS-C) after SARS-CoV-2 Infection. Open Forum Infect Dis 2022. [DOI: 10.1093/ofid/ofac492.315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Abstract
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.
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Affiliation(s)
| | - Michael J Wu
- Centers for Disease Control and Prevention , Atlanta , Georgia
| | - Lora M Martin
- University of Mississippi Medical Center , Jackson, Mississippi
| | - Lacy Malloch
- University of Mississippi Medical Center , Jackson, Mississippi
| | | | | | - Cameron Sanders
- University of Mississippi Medical Center , Jackson, Mississippi
| | - Kayla Patterson
- University of Mississippi Medical Center , Jackson, Mississippi
| | | | | | - Matthew Leroue
- University of Colorado School of Medicine and Children's Hospital Colorado , Aurora, Colorado
| | - Mark Hall
- Nationwide Children’s Hospital , Columbus, Ohio
| | | | - Courtney M Rowan
- Indiana University School of Medicine, Riley Hospital for Children , Indianapolis, Indiana
| | - Kari Wellnitz
- University of Iowa Hospitals & Clinics , Iowa City, Iowa
| | - Laura L Loftis
- Texas Children's Hospital and Baylor College of Medicine , Houston, Texas
| | - Tamara T Bradford
- Louisiana State University Health Sciences Center and Children’s Hospital of New Orleans , New Orleans, Louisiana
| | | | - Christopher Babbit
- 16. Miller Children’s and Women’s Hospital of Long Beach , Long Beach, California
| | | | - Pia S Pannaraj
- Children’s Hospital Los Angeles and University of Southern California , Los Angeles, California
| | - Michele Kong
- University of Alabama at Birmingham , Birmingham, Alabama
| | - Janet Chou
- Boston Children’s Hospital and Harvard Medical School , Boston, Massachusetts
| | - Manish M Patel
- U.S. Centers for Disease Control and Prevention , Atlanta , Georgia
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