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Article ; Online: Incorporation of biomarkers into a prediction model for paediatric radiographic pneumonia

Sriram Ramgopal / Lilliam Ambroggio / Douglas Lorenz / Samir S. Shah / Richard M. Ruddy / Todd A. Florin

ERJ Open Research, Vol 9, Iss

2023 Volume 2

Abstract: Objective The aim of this study was to evaluate biomarkers to predict radiographic pneumonia among children with suspected lower respiratory tract infections (LRTI). Methods We performed a single-centre prospective cohort study of children 3 months to 18 ...

Abstract	Objective The aim of this study was to evaluate biomarkers to predict radiographic pneumonia among children with suspected lower respiratory tract infections (LRTI). Methods We performed a single-centre prospective cohort study of children 3 months to 18 years evaluated in the emergency department with signs and symptoms of LRTI. We evaluated the incorporation of four biomarkers (white blood cell count, absolute neutrophil count, C-reactive protein (CRP) and procalcitonin), in isolation and in combination, with a previously developed clinical model (which included focal decreased breath sounds, age and fever duration) for an outcome of radiographic pneumonia using multivariable logistic regression. We evaluated the improvement in performance of each model with the concordance (c-) index. Results Of 580 included children, 213 (36.7%) had radiographic pneumonia. In multivariable analysis, all biomarkers were statistically associated with radiographic pneumonia, with CRP having the greatest adjusted odds ratio of 1.79 (95% CI 1.47–2.18). As an isolated predictor, CRP at a cut-off of 3.72 mg·dL−1 demonstrated a sensitivity of 60% and a specificity of 75%. The model incorporating CRP demonstrated improved sensitivity (70.0% versus 57.7%) and similar specificity (85.3% versus 88.3%) compared to the clinical model when using a statistically derived cutpoint. In addition, the multivariable CRP model demonstrated the greatest improvement in concordance index (0.780 to 0.812) compared with a model including only clinical variables. Conclusion A model consisting of three clinical variables and CRP demonstrated improved performance for the identification of paediatric radiographic pneumonia compared with a model with clinical variables alone.
Keywords	Medicine ; R
Subject code	310
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	European Respiratory Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The antibiotic resistance reservoir of the lung microbiome expands with age in a population of critically ill patients

Victoria T. Chu / Alexandra Tsitsiklis / Eran Mick / Lilliam Ambroggio / Katrina L. Kalantar / Abigail Glascock / Christina M. Osborne / Brandie D. Wagner / Michael A. Matthay / Joseph L. DeRisi / Carolyn S. Calfee / Peter M. Mourani / Charles R. Langelier

Nature Communications, Vol 15, Iss 1, Pp 1-

2024 Volume 10

Abstract: Abstract Antimicrobial resistant lower respiratory tract infections are an increasing public health threat and an important cause of global mortality. The lung microbiome can influence susceptibility of respiratory tract infections and represents an ... ...

Abstract	Abstract Antimicrobial resistant lower respiratory tract infections are an increasing public health threat and an important cause of global mortality. The lung microbiome can influence susceptibility of respiratory tract infections and represents an important reservoir for exchange of antimicrobial resistance genes. Studies of the gut microbiome have found an association between age and increasing antimicrobial resistance gene burden, however, corollary studies in the lung microbiome remain absent. We performed an observational study of children and adults with acute respiratory failure admitted to the intensive care unit. From tracheal aspirate RNA sequencing data, we evaluated age-related differences in detectable antimicrobial resistance gene expression in the lung microbiome. Using a multivariable logistic regression model, we find that detection of antimicrobial resistance gene expression was significantly higher in adults compared with children after adjusting for demographic and clinical characteristics. This association remained significant after additionally adjusting for lung bacterial microbiome characteristics, and when modeling age as a continuous variable. The proportion of adults expressing beta-lactam, aminoglycoside, and tetracycline antimicrobial resistance genes was higher compared to children. Together, these findings shape our understanding of the lung resistome in critically ill patients across the lifespan, which may have implications for clinical management and global public health.
Keywords	Science ; Q
Subject code	610
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children

The Journal of Clinical Investigation, Vol 133, Iss

2023 Volume 7

Abstract: BACKGROUND Lower 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 ... ...

Abstract	BACKGROUND Lower 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.METHODS We 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.RESULTS The 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.CONCLUSION Lower 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.FUNDING Support 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, ...
Keywords	Infectious disease ; Pulmonology ; Medicine ; R
Subject code	610
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	American Society for Clinical Investigation
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Accounting for misclassification bias of binary outcomes due to underscreening

Nanhua Zhang / Si Cheng / Lilliam Ambroggio / Todd A. Florin / Maurizio Macaluso

BMC Medical Research Methodology, Vol 17, Iss 1, Pp 1-

a sensitivity analysis

2017 Volume 9

Abstract: Abstract Background Diagnostic tests are performed in a subset of the population who are at higher risk, resulting in undiagnosed cases among those who do not receive the test. This poses a challenge for estimating the prevalence of the disease in the ... ...

Abstract	Abstract Background Diagnostic tests are performed in a subset of the population who are at higher risk, resulting in undiagnosed cases among those who do not receive the test. This poses a challenge for estimating the prevalence of the disease in the study population, and also for studying the risk factors for the disease. Methods We formulate this problem as a missing data problem because the disease status is unknown for those who do not receive the test. We propose a Bayesian selection model which models the joint distribution of the disease outcome and whether testing was received. The sensitivity analysis allows us to assess how the association of the risk factors with the disease outcome as well as the disease prevalence change with the sensitivity parameter. Results We illustrated our model using a retrospective cohort study of children with asthma exacerbation that were evaluated for pneumonia in the emergency department. Our model found that female gender, having fever during ED or at triage, and having severe hypoxia are significantly associated with having radiographic pneumonia. In addition, simulation studies demonstrate that the Bayesian selection model works well even under circumstances when both the disease prevalence and the screening proportion is low. Conclusion The Bayesian selection model is a viable tool to consider for estimating the disease prevalence and in studying risk factors of the disease, when only a subset of the target population receive the test.
Keywords	Misclassification ; Selection model ; Underscreening ; Radiographic pneumonia ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2017-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prospective cohort study of children with suspected SARS-CoV-2 infection presenting to paediatric emergency departments

Nathan Kuppermann / Daniel Joseph Tancredi / Lilliam Ambroggio / Anna L. Funk / Todd A. Florin / Stuart R. Dalziel / Marina I. Salvadori / Mark I. Neuman / Daniel C. Payne / Amy C. Plint / Terry P. Klassen / Richard Malley / Kelly Kim / Stephen B. Freedman

BMJ Open, Vol 11, Iss

a Paediatric Emergency Research Networks (PERN) Study Protocol

2021 Volume 1

Abstract: Introduction Relatively limited data are available regarding paediatric COVID-19. Although most children appear to have mild or asymptomatic infections, infants and those with comorbidities are at increased risk of experiencing more severe illness and ... ...

Abstract	Introduction Relatively limited data are available regarding paediatric COVID-19. Although most children appear to have mild or asymptomatic infections, infants and those with comorbidities are at increased risk of experiencing more severe illness and requiring hospitalisation due to COVID-19. The recent but uncommon association of SARS-CoV-2 infection with development of a multisystem inflammatory syndrome has heightened the importance of understanding paediatric SARS-CoV-2 infection.Methods and analysis The Paediatric Emergency Research Network-COVID-19 cohort study is a rapid, global, prospective cohort study enrolling 12 500 children who are tested for acute SARS-CoV-2 infection. 47 emergency departments across 12 countries on four continents will participate. At enrolment, regardless of SARS-CoV-2 test results, all children will have the same information collected, including clinical, epidemiological, laboratory, imaging and outcome data. Interventions and outcome data will be collected for hospitalised children. For all children, follow-up at 14 and 90 days will collect information on further medical care received, and long-term sequelae, respectively. Statistical models will be designed to identify risk factors for infection and severe outcomes.Ethics and dissemination Sites will seek ethical approval locally, and informed consent will be obtained. There is no direct risk or benefit of study participation. Weekly interim analysis will allow for real-time data sharing with regional, national, and international policy makers. Harmonisation and sharing of investigation materials with WHO, will contribute to synergising global efforts for the clinical characterisation of paediatric COVID-19. Our findings will enable the implementation of countermeasures to reduce viral transmission and severe COVID-19 outcomes in children.Trial registration number NCT04330261
Keywords	Medicine ; R
Subject code	170
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Lower respiratory tract infections in children requiring mechanical ventilation

Alexandra Tsitsiklis, PhD / Christina M Osborne, MD / Jack Kamm, PhD / Kayla Williamson, MS / Katrina Kalantar, PhD / Gytis Dudas, PhD / Saharai Caldera, BA / Amy Lyden, BA / Michelle Tan, BS / Norma Neff, PhD / Victoria Soesanto, BS / J Kirk Harris, PhD / Lilliam Ambroggio, PhD / Aline B Maddux, MD / Todd C Carpenter, MD / Ron W Reeder, PhD / Chris Locandro, MS / Eric A F Simões, ProfMD / Matthew K Leroue, MD /

Mark W Hall, ProfMD / Athena F Zuppa, ProfMD / Joseph Carcillo, ProfMD / Kathleen L Meert, ProfMD / Anil Sapru, ProfMD / Murray M Pollack, ProfMD / Patrick S McQuillen, ProfMD / Daniel A Notterman, ProfMD / J Michael Dean, ProfMD / Matt S Zinter, MD / Brandie D Wagner, PhD / Joseph L DeRisi, ProfPhD / Peter M Mourani, ProfMD / Charles R Langelier, MDPhD

The Lancet Microbe, Vol 3, Iss 4, Pp e284-e

a multicentre prospective surveillance study incorporating airway metagenomics

2022 Volume 293

Abstract: Summary: Background: Lower respiratory tract infections (LRTI) are a leading cause of critical illness and mortality in mechanically ventilated children; however, the pathogenic microbes frequently remain unknown. We combined traditional diagnostics with ...

Abstract	Summary: Background: Lower respiratory tract infections (LRTI) are a leading cause of critical illness and mortality in mechanically ventilated children; however, the pathogenic microbes frequently remain unknown. We combined traditional diagnostics with metagenomic next generation sequencing (mNGS) to evaluate the cause of LRTI in critically ill children. Methods: We conducted a prospective, multicentre cohort study of critically ill children aged 31 days to 17 years with respiratory failure requiring mechanical ventilation (>72 h) in the USA. By combining bacterial culture and upper respiratory viral PCR testing with mNGS of tracheal aspirate collected from all patients within 24 h of intubation, we determined the prevalence, age distribution, and seasonal variation of viral and bacterial respiratory pathogens detected by either method in children with or without LRTI. Findings: Between Feb 26, 2015, and Dec 31, 2017, of the 514 enrolled patients, 397 were eligible and included in the study (276 children with LRTI and 121 with no evidence of LRTI). A presumptive microbiological cause was identified in 255 (92%) children with LRTI, with respiratory syncytial virus (127 [46%]), Haemophilus influenzae (70 [25%]), and Moraxella catarrhalis (65 [24%]) being most prevalent. mNGS identified uncommon pathogens including Ureaplasma parvum and Bocavirus. Co-detection of viral and bacterial pathogens occurred in 144 (52%) patients. Incidental carriage of potentially pathogenic microbes occurred in 82 (68%) children without LRTI, with rhinovirus (30 [25%]) being most prevalent. Respiratory syncytial virus (p<0·0001), H influenzae (p=0·0006), and M catarrhalis (p=0·0002) were most common in children younger than 5 years. Viral and bacterial LRTI occurred predominantly during winter months. Interpretation: These findings demonstrate that respiratory syncytial virus, H influenzae, and M catarrhalis contribute disproportionately to severe paediatric LRTI, co-infections are common, and incidental carriage of potentially ...
Keywords	Medicine (General) ; R5-920 ; Microbiology ; QR1-502
Subject code	610
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting severe pneumonia in the emergency department

Nicholas Watkins / Mark I Neuman / Todd Adam Florin / Daniel Joseph Tancredi / Lilliam Ambroggio / Fahd A Ahmad / Andrea Álvarez-Álvarez / Alberto Arrighini / Usha Avva / Elena Aquino Olivia / Uchechi Azubuine / Luisa Baron Gonzalez de Suso / Kelly R Bergmann / Stuart A Bradin / Kristen Breslin / Rosa María Calderón Checa / Maria Natali Campo Fernández / Carmen Campos-Calleja / Kerry Caperell /

Pradip P Chaudhari / Jonathan Cherry / Wee-Jhong Chua / Ida Concha Murray / Thosar Deepali / Pinky-Rose Espina / Susan Fairbrother / Alexandria Farish / Daniel M Fein / Ramón Fernández Álvarez / Todd A Florin / Karen Forward / Jara Gaitero Tristán / Iker Gangoiti / Michael A Gardiner / Virginia Gómez-Barrena / Tamara Hirsch Birn / Adam Isacoff / April J Kam / Nirupama Kannikeswaran / Maria Y Kwok / Maren M Lunoe / Ryan McKee / Son H McLaren / Lianne McLean / Garth D Meckler / Erin Mills / Diana Aniela Moldovan / Andrea Mora-Capín / Viera Morales / Claudia R Morris

BMJ Open, Vol 10, Iss

a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

2020 Volume 12

Abstract: Introduction Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting ... ...

Abstract	Introduction Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysis This study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and dissemination This study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.
Keywords	Medicine ; R
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Incorporation of biomarkers into a prediction model for paediatric radiographic pneumonia

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Article ; Online: The antibiotic resistance reservoir of the lung microbiome expands with age in a population of critically ill patients

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Article ; Online: Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children

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Article ; Online: Accounting for misclassification bias of binary outcomes due to underscreening

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Article ; Online: Prospective cohort study of children with suspected SARS-CoV-2 infection presenting to paediatric emergency departments

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Article ; Online: Lower respiratory tract infections in children requiring mechanical ventilation

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Article ; Online: Predicting severe pneumonia in the emergency department

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