Antimicrobial Stewardship Opportunities in Critically Ill Patients with Gram-Negative Lower Respiratory Tract Infections: A Multicenter Cross-Sectional Analysis

ABSTRACTLower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 – 0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI = 0.75 – 1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.SIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, airway microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.

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Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

10.1101/341149 ◽

2018 ◽

Cited By ~ 4

Author(s):

Charles Langelier ◽

Katrina L Kalantar ◽

Farzad Moazed ◽

Michael R. Wilson ◽

Emily Crawford ◽

...

Keyword(s):

Respiratory Tract ◽

Critically Ill ◽

Critically Ill Patients ◽

Lower Respiratory Tract ◽

Respiratory Tract Infections ◽

Validation Cohort ◽

Lower Respiratory Tract Infections ◽

Respiratory Illnesses ◽

Lung Microbiome ◽

Tract Infections

ABSTRACTLower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the lung microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 – 0.98), and the host transcriptional classifier with an AUC of 0.91 (95% CI = 0.80 – 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 – 1.00) and negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.SIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, lung microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.FundingNHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).

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Reappraisal of respiratory syncytial virus as an aetiology of severe acute lower respiratory tract infections in children younger than 5 years in Nigeria

Transactions of the Royal Society of Tropical Medicine and Hygiene ◽

10.1093/trstmh/trz026 ◽

2019 ◽

Vol 113 (8) ◽

pp. 446-452

Author(s):

Damilola M Oladele ◽

Dimeji P Oladele ◽

Rasheedat M Ibraheem ◽

Mohammed B Abdulkadir ◽

Rasaki Adewole Raheem ◽

...

Keyword(s):

Respiratory Syncytial Virus ◽

Respiratory Tract ◽

Lower Respiratory Tract ◽

Respiratory Tract Infections ◽

Preventive Strategy ◽

Lower Respiratory Tract Infections ◽

Cross Sectional ◽

Rsv Infection ◽

Syncytial Virus ◽

Tract Infections

Abstract Background Acute lower respiratory tract infections (ALRIs) especially severe ALRIs, constitute a global high burden of morbidity and mortality in children <5 y of age and respiratory syncytial virus (RSV) has been documented to a play a major aetiological role. However, Nigerian reports on severe childhood RSV ALRIs are rare and most reports are old. With recent advances in RSV preventive strategy, arises the need for a recent appraisal of RSV infection in children with severe ALRI. The current study thus set out to determine the prevalence of RSV infection among hospitalized children <5 y of age and describe the related social determinants. Methods We performed a descriptive cross-sectional study conducted over 1 y of 120 children, ages 2–59 months, diagnosed with ALRI. Relevant data were obtained and an antigen detection assay was used for viral studies. Results The prevalence of RSV infection was 34.2% and its peak was in the rainy months. The proportion of infants in the RSV-positive group was significantly higher than that in the RSV-negative group (82.9% vs 54.4%; p=0.002). These findings were largely consistent with those of earlier reports. Conclusions RSV has remained a common cause of severe ALRI in infants, especially during the rainy months in Nigeria. It is thus suggested that more effort be focused towards implementing the current global recommendations for the prevention of RSV-associated LRI, particularly in infants.

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Ritkábban előforduló, alsó légúti fertőzést okozó Gram-negatív nem fermentáló pálcák

Orvosi Hetilap ◽

10.1556/650.2018.30926 ◽

2018 ◽

Vol 159 (1) ◽

pp. 23-30

Author(s):

Emese Juhász ◽

Miklós Iván ◽

Júlia Pongrácz ◽

Katalin Kristóf

Keyword(s):

Respiratory Tract ◽

Lower Respiratory Tract ◽

Respiratory Tract Infections ◽

Diffusion Method ◽

Lower Respiratory Tract Infections ◽

Gram Negative ◽

Maldi Tof Ms ◽

Maldi Tof ◽

Tract Infections ◽

Tof Ms

Abstract: Introduction: Glucose non-fermenting Gram-negative bacteria are ubiquitous environmental organisms. Most of them are identified as opportunistic, nosocomial pathogens in patients. Uncommon species are identified accurately, mainly due to the introduction of matrix-assisted laser desorption-ionization time of flight mass spectrometry (MALDI-TOF MS) in clinical microbiology practice. Most of these uncommon non-fermenting rods are isolated from lower respiratory tract samples. Their significance in lower respiratory tract infections, such as rules of their testing are not clarified yet. Aim: The aim of this study was to review the clinical microbiological features of these bacteria, especially their roles in lower respiratory tract infections and antibiotic treatment options. Method: Lower respiratory tract samples of 3589 patients collected in a four-year period (2013–2016) were analyzed retrospectively at Semmelweis University (Budapest, Hungary). Identification of bacteria was performed by MALDI-TOF MS, the antibiotic susceptibility was tested by disk diffusion method. Results: Stenotrophomonas maltophilia was revealed to be the second, whereas Acinetobacter baumannii the third most common non-fermenting rod in lower respiratory tract samples, behind the most common Pseudomonas aeruginosa. The total number of uncommon non-fermenting Gram-negative isolates was 742. Twenty-three percent of isolates were Achromobacter xylosoxidans. Beside Chryseobacterium, Rhizobium, Delftia, Elizabethkingia, Ralstonia and Ochrobactrum species, and few other uncommon species were identified among our isolates. The accurate identification of this species is obligatory, while most of them show intrinsic resistance to aminoglycosides. Resistance to ceftazidime, cefepime, piperacillin-tazobactam and carbapenems was frequently observed also. Conclusions: Ciprofloxacin, levofloxacin and trimethoprim-sulfamethoxazole were found to be the most effective antibiotic agents. Orv Hetil. 2018; 159(1): 23–30.

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