Precision Medicine in Sepsis and Septic Shock

Phenotypic Characterization of Intensive Care Patients With Infections: A Pilot Study of Host and Pathogen-Based Cluster Analysis

INTRODUCTION

Sepsis is a prevalent, albeit complex, disorder among critically ill patients and a "one-size-fits-all" approach does not seem applicable. Host intrinsic characteristics and microorganisms' particularities may influence response to therapy and outcomes. Attempting to group patients and microorganism characteristics may be an important step in developing and facilitating personalized infection treatment plans. This work intends to identify infected patients' clusters using clinical data that includes infection determinants: the isolated pathogen and the site of infection.

METHODS

In this retrospective analysis, we included patients with a microbiologically documented infection and non-infected controls. Patients admitted between January 2015 and December 2019 in the intensive care unit (ICU) were included (aged 17-95 years). Those with isolated microorganisms during their ICU stay were further analyzed using cluster analysis (hierarchical clustering and K-means; SPSS version 25.0). Four primary outcomes were addressed: ICU and hospital mortality rate and ICU and hospital length of stay (LOS).

RESULTS

This study included 1,923 patients, of whom 721 (37.5%) had at least one microbiological isolate during their ICU stay. Patients with at least one isolate identification were older (mean age 67.7 years vs. 65 years; p < 0.001) and had a higher ICU and hospital mortality (20.3% vs. 24.3%, p = 0.041; 26.9% vs. 38.4%, p < 0.001), as well as a longer LOS (median hospital LOS 8 vs. 18 days; p < 0.001) than patients without microorganisms identified. Patients with at least one isolated microorganism were divided into five different clusters. Notable differences were found in their ICU and hospital trajectories between clusters.

CONCLUSION

The cluster analysis approach provided valuable insights into the complex interplay between bacterial virulence, infection site, and patient outcomes in critical care medicine. Patients infected with bacteraemia by Gram-positive bacteria (cluster 2) or Enterobacteriaceae (Cluster 5) and fungal isolation in respiratory samples (Cluster 3) should prompt more aggressive clinical interventions, as these patients are more prone to die in the hospital.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.