Keiko M Tarquinio1, Todd Karsies2, Steven L Shein3, Andrew Beardsley4, Robinder Khemani5, Adam Schwarz6, Lincoln Smith7, Heidi Flori8, Oliver Karam9, Quy Cao10, Zainab Haider11, Ekaterina Smirnova12, Myrna G Serrano13,14, Gregory A Buck13,14,15, Douglas F Willson9. 1. Division of Pediatric Critical Care, Children's Healthcare of Atlanta, Emory University, Atlanta, Georgia, USA. 2. Division of Pediatric Critical Care, Nationwide Children's Hospital, Columbus, Ohio, USA. 3. Division of Pediatric Critical Care, Rainbow Babies and Children's Hospital, Cleveland, Ohio, USA. 4. Division of Pediatric Critical Care, Riley Hospital for Children, Indianapolis, Indiana, USA. 5. Division of Pediatric Critical Care, Children's Hospital of Los Angeles, Los Angeles, California, USA. 6. Division of Pediatric Critical Care, Children's Hospital of Orange Country, Mission Viejo, California, USA. 7. Division of Pediatric Critical Care, Seattle Children's Hospital, Seattle, Washington, USA. 8. Division of Pediatric Critical Care, CS Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, USA. 9. Division of Pediatric Critical Care, Children's Hospital of Richmond at VCU, Richmond, Virginia, USA. 10. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Hershey, Pennsylvania, USA. 11. Department of Bioinformatics, Virginia Commonwealth University, Richmond, Virginia, USA. 12. Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA. 13. Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, Virginia, USA. 14. Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, Virginia, USA. 15. Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA.
Abstract
BACKGROUND: Little is known about the airway microbiome in intubated mechanically ventilated children. We sought to characterize the airway microbiome longitudinally and in association with clinical variables and possible ventilator-associated infection (VAI). METHODS: Serial tracheal aspirate samples were prospectively obtained from mechanically ventilated subjects under 3 years old from eight pediatric intensive care units in the United States from June 2017 to July 2018. Changes in the tracheal microbiome were analyzed by sequencing bacterial 16S ribosomal RNA gene relative to subject demographics, diagnoses, clinical parameters, outcomes, antibiotic treatment, and the Ventilator-Associated InfectioN (VAIN) score. RESULTS: A total of 221 samples from 58 patients were processed and 197 samples met the >1000 reads criteria (89%), with an average of 43,000 reads per sample. The median number of samples per subject was 3 (interquartile range [IQR]: 2-5), with a median VAIN score of 2 (IQR: 1-3). Proteobacteria was the highest observed phyla throughout the intubation period, followed by Firmicutes and Actinobacteria. Alpha diversity was negatively associated with days of intubation (p = .032) and VAIN score (p = .016). High VAIN scores were associated with a decrease of Mycobacterium obuense, and an increase of Streptococcus peroris, Porphyromonadaceae family (unclassified species), Veillonella atypica, and several other taxa. No specific pattern of microbiome composition related to clinically diagnosed VAIs was observed. CONCLUSIONS: Our data demonstrate decreasing alpha diversity with increasing VAIN score and days of intubation. No specific microbiome pattern was associated with clinically diagnosed VAI.
BACKGROUND: Little is known about the airway microbiome in intubated mechanically ventilated children. We sought to characterize the airway microbiome longitudinally and in association with clinical variables and possible ventilator-associated infection (VAI). METHODS: Serial tracheal aspirate samples were prospectively obtained from mechanically ventilated subjects under 3 years old from eight pediatric intensive care units in the United States from June 2017 to July 2018. Changes in the tracheal microbiome were analyzed by sequencing bacterial 16S ribosomal RNA gene relative to subject demographics, diagnoses, clinical parameters, outcomes, antibiotic treatment, and the Ventilator-Associated InfectioN (VAIN) score. RESULTS: A total of 221 samples from 58 patients were processed and 197 samples met the >1000 reads criteria (89%), with an average of 43,000 reads per sample. The median number of samples per subject was 3 (interquartile range [IQR]: 2-5), with a median VAIN score of 2 (IQR: 1-3). Proteobacteria was the highest observed phyla throughout the intubation period, followed by Firmicutes and Actinobacteria. Alpha diversity was negatively associated with days of intubation (p = .032) and VAIN score (p = .016). High VAIN scores were associated with a decrease of Mycobacterium obuense, and an increase of Streptococcus peroris, Porphyromonadaceae family (unclassified species), Veillonella atypica, and several other taxa. No specific pattern of microbiome composition related to clinically diagnosed VAIs was observed. CONCLUSIONS: Our data demonstrate decreasing alpha diversity with increasing VAIN score and days of intubation. No specific microbiome pattern was associated with clinically diagnosed VAI.
Authors: Tetyana Zakharkina; Ignacio Martin-Loeches; Sébastien Matamoros; Pedro Povoa; Antoni Torres; Janine B Kastelijn; Jorrit J Hofstra; B de Wever; Menno de Jong; Marcus J Schultz; Peter J Sterk; Antonio Artigas; Lieuwe D J Bos Journal: Thorax Date: 2017-01-18 Impact factor: 9.139
Authors: Douglas F Willson; Michelle Hoot; Robinder Khemani; Christopher Carrol; Aileen Kirby; Adam Schwarz; Rainer Gedeit; Sholeen T Nett; Simon Erickson; Heidi Flori; Spencer Hays; Mark Hall Journal: Pediatr Crit Care Med Date: 2017-01 Impact factor: 3.624
Authors: Jennifer M Fettweis; Myrna G Serrano; Nihar U Sheth; Carly M Mayer; Abigail L Glascock; J Paul Brooks; Kimberly K Jefferson; Gregory A Buck Journal: BMC Genomics Date: 2012-12-17 Impact factor: 3.969