Background: Pneumonia remains a leading cause of antibiotic consumption globally, contributing significantly to the burden of antimicrobial resistance (AMR). The respiratory microbiome plays a crucial role in the emergence of AMR and therapeutic failure in both community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP). The PHENOMENON study aims to investigate the relationship between the respiratory microbiome composition and clinical outcomes to improve the prediction of treatment failure and AMR emergence. Methods: This multicenter prospective cohort study will include 300 adult patients across three cohorts: CAP in general wards, severe CAP in intensive care units (ICU), and ventilator-associated HAP (vHAP/VAP). Patients will undergo oropharyngeal and rectal swabbing at admission (Day 0), Day 3, Day 7-10, and Day 90, along with blood sampling and endotracheal aspirates in intubated patients. The primary objective is to assess the association between baseline respiratory microbiome composition and time to antibiotic response within 7-10 days. The composite primary endpoint includes clinical failure, microbiological failure, or AMR emergence. Secondary endpoints explore the association between microbiome composition and pneumonia recurrence, severity, hospital length of stay, and mortality at Day 28 and Day 90. Expected Outcomes: This study will provide insights into the predictive value of respiratory microbiome composition on antibiotic response and AMR emergence. Understanding these relationships may guide personalized antibiotic strategies and optimize pneumonia management, ultimately reducing treatment failure rates and improving patient outcomes.
Pneumonia can be acquired in the community such as COVID-19 or flu, or during hospitalization for a different medical condition. The significant burden of CAP is set to increase with ageing populations and growing rates of comorbidity. CAP was the leading cause of communicable diseases and the second cause of disability-adjusted life-years loss in the world in 2019, even before the COVID-19 pandemic (GBD 2019 Adolescent Mortality Collaborators 2021; GBD 2019 Diseases and Injuries Collaborators 2020). CAP is classically induced by virulent bacteria (such as Streptococcus pneumoniae) or viruses (influenza), but also new pathogenic viruses such as SARS-CoV-2 (COVID-19). Incidence of VAP ranges from 5% to 67% depending on case mix and diagnostic criteria. In the US, the incidence of VAP ranges from 2 to 16 episodes per 1,000 ventilator-day. The estimated risk of VAP is initially high and decreases to less than 0.5% per day after 14 days of mechanical ventilation. VAP increases the duration of hospitalization by 7 days and health-care costs by approximately $40,000 USD per episode (Safdar et al. 2005; Eber et al. 2010). Antimicrobial resistance is rising, leading to increased durations of hospital stay and excess deaths in septic patients worldwide. The World Health Organization considers antibiotic resistance to be one of the biggest global health threats we are currently facing. AMR, including malaria, tuberculosis and bacterial infections, may increase to 10 million fatalities worldwide by 2050. While some resistance is intrinsic in some bacterial taxa, the main issue is acquired resistance, as bacteria can exchange genetic material and thereby spread antibiotic resistance genes (ARG). Previous carriage of extended-spectrum beta-lactamases-producing Enterobacterales (ESBL-E) is found in 5 to 25% of ICU patients. Although a previous carriage is the major risk factor associated with VAP related to ESBL-E, only 5% to 20% of the ESBL-E carriers will develop a VAP related to ESBL-E. Carriage status therefore has a high negative predictive value for ESBL-E-associated VAP, whereas positive predictive value, i.e., the probability of having an ESBL-E infection in case of ESBL-E carriage, is less than 50%. When caring for patients with VAP, recent studies have shown that the adequacy of the initial antimicrobial therapy is not associated with a significant improvement of VAP prognosis, especially if a multidrug-resistant (MDR) Gram-negative bacterium is involved (Sommer et al. 2018; Titov et al. 2021). One likely explanation of the absence of benefit of adequate therapy within 24h is the rapid diffusion and expression of ARGs within the microbiome during treatment. It has been demonstrated the feasibility of characterizing lung microbiota by producing preliminary data from 174 respiratory samples collected in 65 patients included in the IBIS biobank within the IBIS cohort. These preliminary analyses have also confirmed that the respiratory microbiome of hospitalized patients shifts from a normal composition (day 1) to a specific pattern poor in Streptococcus and enriched in Haemophilus (day 7). Recently, it was observed in a randomized clinical trial that probiotics increase the ARG richness of the gut microbiome during antimicrobial treatment (Figure 2) (Montassier et al. 2021). These data demonstrated the ability to investigate the time course of ARG during treatment in human samples, and reinforce the hypothesis of rapid modifications of the ARG during treatment. In summary, treatment failures are common in patients with CAP and HAP, even in cases of adequate antimicrobial therapy. Some specific antimicrobial therapies result in better outcomes than standard of cares and classical microbiology fail to explain this outcome. Recent data demonstrated that the microbiome is a significant source of antibiotic resistance genes (ARG) which can rapidly be diffused between species during treatment. In light of these preliminary results, we intend 1. to define VAP and CAP sub-phenotypes based on the real-time course of the load of ARG in vivo 2. to demonstrate in vivo that specific microbiome editing based on these phenotypes can enhance pneumonia outcomes. The definition of these phenotypes of pneumonia is likely to impact the way patients are treated in daily practice, shifting the antimicrobial treatment from in vitro functional tests to in vivo prediction of response to treatment.
Study Type
OBSERVATIONAL
Enrollment
300
Hospital Beaujon
Clichy, France
ACTIVE_NOT_RECRUITINGCHU Nantes - Saint Herblain
Nantes, France
NOT_YET_RECRUITINGCHU Nantes - Saint Herblain
Nantes, France
NOT_YET_RECRUITINGCHU Nantes
Nantes, France
ACTIVE_NOT_RECRUITINGHospital Bichat
Paris, France
RECRUITINGHospital Bichat
Paris, France
ACTIVE_NOT_RECRUITINGTo study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) Clinical success is defined as : \- vHAP-VAP: resolution of the clinical symptoms that lead to the diagnosis on vHAP/VAP
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) Clinical success is defined as : \- CAP: resolution of fever and dyspnea, or oxygen therapy requirement clinical symptoms
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) without subsequent microbiological failure Microbiological failure is defined as: \- Persistence in standard culture of respiratory samples, of the microorganisms causing the pneumonia, independently from antibiotic susceptibility
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) without subsequent microbiological failure OR Microbiological failure is defined as: Appearance, on respiratory sample, of a new pathogen considered as requiring a new antimicrobial therapy (superinfection) by the attending physician, before the test-of-cure visit (day 7-10).
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) without subsequent microbiological failure or antibiotic resistance within the primary endpoint observation period, which will last 90 days after inclusion. Antibiotic resistance is defined as one of the 3 following definitions: \- Detection of the same pneumonia pathogen but with resistance to at least one of the given antibiotics, on any respiratory sample collected after initial diagnosis;
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) without subsequent microbiological failure or antibiotic resistance within the primary endpoint observation period, which will last 90 days after inclusion. Antibiotic resistance is defined as one of the 3 following definitions: \- Occurrence of a new pathogen in respiratory specimens resistant to at least one of the given antibiotics;
Time frame: 3 months
To study the relationship between the composition of the respiratory microbiome at the start of treatment and time to response to antibiotic treatment.
The measurement of interest for the primary endpoint will be the time to clinical success (resolution of symptoms) without subsequent microbiological failure or antibiotic resistance within the primary endpoint observation period, which will last 90 days after inclusion. Antibiotic resistance is defined as one of the 3 following definitions: \- Occurrence of a new pathogen in another site (infection, colonization or carriage) resistant the given antibiotic(s).
Time frame: 3 months
Pneumonia relapse or recurrence
Pneumonia relapse or recurrence
Time frame: 3 months
Time to clinical recovery (regardless of microbiological recovery or resistance failure)
To investigate the link between the composition of the respiratory microbiome at the start of treatment and pneumonia recurrence (second episode of pneumonia with at least one common pathogen)
Time frame: 3 months
Time to clinical recovery (regardless of microbiological recovery or resistance failure)
To investigate the link between the composition of the respiratory microbiome at the start of treatment and pneumonia relapse (second episode of pneumonia with new pathogens)
Time frame: 3 months
Presence or absence of antimicrobial resistance at any point during follow-up
To study the link between the composition of the respiratory microbiome at the start of treatment and the emergence of antibiotic resistance
Time frame: 3 months
Association between specific antibiotics and respiratory microbiome evolution
To study the link between the antibiotic regimen (spectrum and dose) and time course of the respiratory microbiome composition and resistome
Time frame: 3 months
Association between specific antibiotics and gut microbiome evolution
To study the link between the antibiotic regimen (spectrum and dose) and time course of the gut microbiome composition and resistome
Time frame: 3 months
Duration of antimicrobial treatment
Duration of antimicrobial treatment
Time frame: 3 months
Association between baseline microbiome and Acute Respiratory Distress Syndrome during pneumonia
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To evaluate the link between the respiratory microbiome on baseline and Pneumonia severity (ARDS yes or no)
Time frame: 3 months
Association between baseline microbiome and Length of initial hospital stay/ ICU stay
To evaluate the link between the respiratory microbiome on baseline and the length of hospital stay/ ICU stay after the start of antimicrobial treatment
Time frame: 3 months
Association between baseline microbiome and All-cause mortality on day 28 and day 90
To evaluate the link between the respiratory microbiome on baseline and all-cause mortality on day 28 and day 90 after the start of antimicrobial treatment
Time frame: 3 months
Quality of life questionnaires (EQ-5D-5L) on day 0, day 28 and day 90
To assess the quality of life on day 28 and day 90 after the start of antimicrobial therapy
Time frame: 3 months
Correlation between oropharyngeal, endotracheal and gut microbiota profiles in intubated patients
To validate in intubated patients the relationship between oropharyngeal, endotracheal and gut microbiota profiles.
Time frame: 3 months