Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.

BACKGROUND: The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. METHODS: We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. RESULTS: We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. CONCLUSION: Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

ICU-acquired infections in immunocompromised patients.

Immunocompromised patients account for an increasing proportion of the typical intensive care unit (ICU) case-mix. Because of the increased availability of new drugs for cancer and auto-immune diseases, and improvement in the care of the most severely immunocompromised ICU patients (including those with hematologic malignancies), critically ill immunocompromised patients form a highly heterogeneous patient population. Furthermore, a large number of ICU patients with no apparent immunosuppression also harbor underlying conditions altering their immune response, or develop ICU-acquired immune deficiencies as a result of sepsis, trauma or major surgery. While infections are associated with significant morbidity and mortality in immunocompromised critically ill patients, little specific data are available on the incidence, microbiology, management and outcomes of ICU-acquired infections in this population. As a result, immunocompromised patients are usually excluded from trials and guidelines on the management of ICU-acquired infections. The most common ICU-acquired infections in immunocompromised patients are ventilator-associated lower respiratory tract infections (which include ventilator-associated pneumonia and tracheobronchitis) and bloodstream infections. Recently, several large observational studies have shed light on some of the epidemiological specificities of these infections-as well as on the dynamics of colonization and infection with multidrug-resistant bacteria-in these patients, and these will be discussed in this review. Immunocompromised patients are also at higher risk than non-immunocompromised hosts of fungal and viral infections, and the diagnostic and therapeutic management of these infections will be covered. Finally, we will suggest some important areas of future investigation.

Immunosuppression at ICU admission is not associated with a higher incidence of ICU-acquired bacterial bloodstream infections: the COCONUT study.

BACKGROUND: Immunosuppression at intensive care unit (ICU) admission has been associated with a higher incidence of ICU-acquired infections, some of them related to opportunistic pathogens. However, the association of immunosuppression with the incidence, microbiology and outcomes of ICU-acquired bacterial bloodstream infections (BSI) has not been thoroughly investigated. METHODS: Retrospective single-centered cohort study in France. All adult patients hospitalized in the ICU of Lille University-affiliated hospital for > 48 h between January 1st and December 31st, 2020, were included, regardless of their immune status. Immunosuppression was defined as active cancer or hematologic malignancy, neutropenia, hematopoietic stem cell and solid organ transplants, use of steroids or immunosuppressive drugs, human immunodeficiency virus infection and genetic immune deficiency. The primary objective was to compare the 28-day cumulative incidence of ICU-acquired bacterial BSI between immunocompromised and non-immunocompromised patients. Secondary objectives were to assess the microbiology and outcomes of ICU-acquired bacterial BSI in the two groups. RESULTS: A total of 1313 patients (66.9% males, median age 62 years) were included. Among them, 271 (20.6%) were immunocompromised at ICU admission. Severity scores at admission, the use of invasive devices and antibiotic exposure during ICU stay were comparable between groups. Both prior to and after adjustment for pre-specified baseline confounders, the 28-day cumulative incidence of ICU-acquired bacterial BSI was not statistically different between immunocompromised and non-immunocompromised patients. The distribution of bacteria was comparable between groups, with a majority of Gram-negative bacilli (~ 64.1%). The proportion of multidrug-resistant bacteria was also similar between groups. Occurrence of ICU-acquired bacterial BSI was associated with a longer ICU length-of-stay and a longer duration of invasive mechanical ventilation, with no significant association with mortality. Immune status did not modify the association between occurrence of ICU-acquired bacterial BSI and these outcomes. CONCLUSION: The 28-day cumulative incidence of ICU-acquired bacterial BSI was not statistically different between patients with and without immunosuppression at ICU admission.

A computational framework to improve cross-platform implementation of transcriptomics signatures.

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.