Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a “precision medicine” approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p < 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2.

Disruption of the microbiota resulting in pathogenicity is known as dysbiosis and is key in the pathogenesis of inflammatory bowel disease [IBD]. The microbiome of patients with IBD is characterized by depletion of commensal bacteria, in particular Bacteroidetes and the Lachnospiraceae subgroup of Firmicutes, and by the concomitant increase in Proteobacteria and the Bacillus subgroup of Firmicutes. These changes reflect a decrease in microbial diversity with a concomitant decrease in health-promoting bacteria like Faecalibacterium and Roseburia. Treatment with biologic agents has changed the natural course of disease, improving patient outcomes. Changes in gut microbiota occur under treatment with biologic agents and act towards reversal of dysbiosis. These changes are more striking in patients achieving remission and specific gut microbiota signatures may be predictive of treatment response and a step towards precision medicine, since, despite advances in medical treatment, some patients are at risk of surgery and subsequent complications such as anastomotic leakage. This review summarizes current available evidence on the interplay of gut microbiota and biologic agents, surgery, and surgical complications in patients with IBD.

Background/Objectives: Sepsis heterogeneity limits advances in immunotherapy. Increasing use of artificial intelligence (AI) and machine learning (ML) attempts to turn multi-dimensional data into meaningful clusters, indicating biological mechanisms. We provide an overview of the existing evidence on AI-derived sepsis subtyping, exploring treatment response to available immune modulating therapies. Methods: On 1 October 2025, we conducted a structured search on all relative publications on MEDLINE and undertook a narrative review. Results: Multiple subphenotyping algorithms were identified, using clinical, biological, and omics data, across different cohorts, mainly through secondary analyses of randomized trials. The main classification was between hyper- and hypoinflammatory subphenotypes. Statins, corticosteroids, activated protein C, or thrombomodulin displayed differential effects on the outcome of these subphenotypes. Conclusions: Further research is required to prospectively validate findings and to offer pragmatic solutions to patients who need them the most. Issues of validity, equity, ethics, and feasibility are discussed.

OBJECTIVES: It is suggested that sepsis may be classified into four clinical phenotypes, using an algorithm employing 29 admission parameters. We applied a simplified phenotyping algorithm among patients with bacterial sepsis and severe COVID-19 and assessed characteristics and outcomes of the derived phenotypes. DESIGN: Retrospective analysis of data from prospective clinical studies. SETTING: Greek ICUs and Internal Medicine departments. PATIENTS AND INTERVENTIONS: We analyzed 1498 patients, 620 with bacterial sepsis and 878 with severe COVID-19. We implemented a six-parameter algorithm (creatinine, lactate, aspartate transaminase, bilirubin, C-reactive protein, and international normalized ratio) to classify patients with bacterial sepsis intro previously defined phenotypes. Patients with severe COVID-19, included in two open-label immunotherapy trials were subsequently classified. Heterogeneity of treatment effect of anakinra was assessed. The primary outcome was 28-day mortality. MEASUREMENTS AND MAIN RESULTS: The algorithm validated the presence of the four phenotypes across the cohort of bacterial sepsis and the individual studies included in this cohort. Phenotype α represented younger patients with low risk of death, β was associated with high comorbidity burden, and δ with the highest mortality. Phenotype assignment was independently associated with outcome, even after adjustment for Charlson Comorbidity Index. Phenotype distribution and outcomes in severe COVID-19 followed a similar pattern. CONCLUSIONS: A simplified algorithm successfully identified previously derived phenotypes of bacterial sepsis, which were predictive of outcome. This classification may apply to patients with severe COVID-19 with prognostic implications.

Background: Pressure ulcers (PUs) remain a prevalent complication in intensive care unit (ICU) settings, especially among immobilized patients. The impact of structured, nursing-led mobilization protocols on PU prevention and recovery remains underexplored. Objective: To evaluate the impact of nursing-led mobilization protocols on the incidence and progression of PUs in critically ill patients. Methods: In this retrospective observational cohort study, 188 ICU patients were admitted during one of two consecutive periods of care: conventional care (6-hourly repositioning) and an advanced nursing-led protocol (3-hourly repositioning with support surfaces and specialized nurse training), which replaced conventional care as standard in our institution. The primary outcome included new PU development for patients with no pre-existing ulcers or worsening/non-progression of pre-existing ulcers at discharge; ICU mortality was evaluated as a secondary outcome. Results: Among patients without pre-existing ulcers (n = 155), new PU incidence did not significantly differ between groups, even after adjusting for SOFA score (OR 0.40, 95% CI: 0.05 TO 3.17; p = 0.374). However, in patients with pre-existing ulcers (n = 33), the advanced care group showed improvement (53.3% versus 0% in the conventional group, OR 0.07, 95% CI: 0.01–0.64; p = 0.012); this effect was independent of initial SOFA score. Mortality was associated with the SOFA score, but not with the type of care. Conclusions: While advanced nursing-led mobilization did not reduce PU incidence, it significantly improved existing ulcer outcomes. Findings support the integration of structured protocols for high-risk ICU patients, especially those with existing ulcers.