Personalized medicine, endotypes, and intensive care medicine

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.

Pediatric Critical Care in the Twenty-first Century and Beyond

Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.

A path to precision in the ICU

Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a “one size fits all” approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges.

First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the “omics” technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.

Distinct Myocardial Mechanisms Underlie Cardiac Dysfunction in Endotoxemic Male and Female Mice

In male mice, sepsis-induced cardiomyopathy develops as a result of dysregulation of myocardial calcium (Ca) handling, leading to depressed cellular Ca transients (ΔCai). ΔCai depression is partially due to inhibition of sarcoplasmic reticulum Ca ATP-ase (SERCA) via oxidative modifications, which are partially opposed by cGMP generated by the enzyme soluble guanylyl cyclase (sGC). Whether similar mechanisms underlie sepsis-induced cardiomyopathy in female mice is unknown.Male and female C57Bl/6J mice (WT), and mice deficient in the sGC α1 subunit activity (sGCα1), were challenged with lipopolysaccharide (LPS, ip). LPS induced mouse death and cardiomyopathy (manifested as the depression of left ventricular ejection fraction by echocardiography) to a similar degree in WT male, WT female, and sGCα1 male mice, but significantly less in sGCα1 female mice. We measured sarcomere shortening and ΔCai in isolated, externally paced cardiomyocytes, at 37°C. LPS depressed sarcomere shortening in both WT male and female mice. Consistent with previous findings, in male mice, LPS induced a decrease in ΔCai (to 30 ± 2% of baseline) and SERCA inhibition (manifested as the prolongation of the time constant of Ca decay, τCa, to 150 ± 5% of baseline). In contrast, in female mice, the depression of sarcomere shortening induced by LPS occurred in the absence of any change in ΔCai, or SERCA activity. This suggested that, in female mice, the causative mechanism lies downstream of the Ca transients, such as a decrease in myofilament sensitivity for Ca. The depression of sarcomere shortening shortening after LPS was less severe in female sGCα1 mice than in WT female mice, indicating that cGMP partially mediates cardiomyocyte dysfunction.These results suggest, therefore, that LPS-induced cardiomyopathy develops through distinct sex-specific myocardial mechanisms. While in males LPS induces sGC-independent decrease in ΔCai, in female mice LPS acts downstream of ΔCai, possibly via sGC-dependent myofilament dysfunction.

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients.