Daily Sepsis Research Analysis

BACKGROUND: Sepsis is a major cause of morbidity and mortality worldwide, with its heterogeneous and dynamically evolving clinical presentation complicating diagnosis, treatment, and prognosis. The identification of clinically meaningful sub-phenotypes within the sepsis population could help tailor interventions and improve outcomes. However, existing phenotyping studies have yielded inconsistent results with limited clinical utility. In this study, we propose a novel, guided machine-learning approach to identify clinically relevant sub-phenotypes within the sepsis condition by integrating deep representation learning with prediction-guided clustering to capture temporal disease trajectories. METHODS: We trained a recurrent neural network-based encoder to generate compact, predictive representations of sepsis patients over time. During training, the encoder is guided by four auxiliary prediction objectives (i.e., 90-day mortality, remaining length of stay, need for mechanical ventilation, and need for renal replacement therapy), which encourage the model to create representations that are relevant with respect to patient-centred outcomes. After training, patient representations were clustered using the K-means algorithm. The identified sub-phenotypes were compared across two large ICU data sets (AmsterdamUMCdb and MIMIC-IV) and interpreted using Integrated Gradients-based attribution maps. Practical and clinical utility of the phenotypes was evaluated using a reinforcement learning framework to evaluate optimal treatment strategies within each sepsis sub-phenotype. RESULTS: Through our approach, we identified six clinically distinct sub-phenotypes with varying risk profiles and presentations. The learned representations demonstrated robust generalisability across the different data sets, and the reinforcement learning results indicated that the different sub-phenotypes were associated with different optimal treatment strategies, highlighting the potential for phenotype-informed decision-making. CONCLUSIONS: This study introduces a flexible and effective framework for the identification of robust and clinically meaningful sub-phenotypes within the population of sepsis patients. Moreover, the identified sub-phenotypes are clinically interpretable, and the proposed trajectory-aware phenotyping approach may support the future development of personalised and precision medicine strategies.

PURPOSE: Dexmedetomidine (DEX) is a central sympatholytic with sedative properties widely used in critically ill patients. However, its effects in patients with sepsis and septic shock remain controversial. This meta-analysis evaluated the efficacy and safety of DEX compared to other sedatives in mechanically ventilated patients with sepsis and septic shock. METHODS: A systematic search was conducted across PubMed, Embase, Scopus, and Cochrane Library from inception through May 1, 2025 for randomized controlled trials comparing DEX with other sedatives or placebo in mechanically ventilated patients with sepsis and septic shock. Primary outcomes included overall mortality and Sequential Organ Failure Assessment (SOFA) scores. Secondary outcomes encompassed duration of mechanical ventilation (MV), length of stay in Intensive Care Unit (ICU), incidence of hypotension and bradycardia. RESULTS: Fifteen studies involving 3,882 patients (1,945 in the DEX group, 1,937 in the control group) were included. DEX was demonstrated no significant differences compared to other sedatives or placebo in overall mortality (Risk Ratio [RR] 0.98, 95% Confidence Interval [CI] 0.90 to 1.07, CONCLUSIONS: In mechanically ventilated patients with sepsis and septic shock, DEX shortened duration of MV but was associated increased bradycardia risk. No mortality or organ dysfunction benefits were observed. These findings suggest DEX is a reasonable therapeutic option to facilitate earlier ventilator weaning in selected patients (particularly those without shock), but careful monitoring for cardiovascular adverse effects is warranted.

BACKGROUND: Acute respiratory failure is common in sepsis. When severe conditions like refractory hypoxemia or persistent CO2 retention occur, invasive mechanical ventilation (IMV) is required. The PaO2/FiO2 ratio is a key indicator for assessing acute respiratory failure severity and sepsis prognosis. This study investigates the early ICU trajectory of PaO2/FiO2 in septic patients requiring IMV for acute respiratory failure and its association with 28-day mortality. METHODS: This retrospective cohort study utilized the MIMIC-IV database to examine septic patients with acute respiratory failure receiving IMV. Group-based trajectory modeling (GBTM) classified PaO2/FiO2 trends over the first 96 h. Cox and logistic regression assessed associations between trajectory patterns and 28-day or ICU mortality. RESULTS: Among 2270 patients, ICU mortality was 27.1% and 28-day mortality 31.2%. Five distinct PaO2/FiO2 trajectories were identified. Compared to the persistently low trajectory group, patients in trajectory 5 (initially low PaO2/FiO2 that improved and stabilized at a mildly reduced level) had significantly lower risks of 28-day mortality (HR 0.73, 95% CI 0.61-0.87) and ICU mortality (OR 0.64, 95% CI 0.50-0.81). CONCLUSION: In patients with sepsis-related acute respiratory failure receiving invasive mechanical ventilation, early dynamic patterns of PaO2/FiO2 in the ICU are closely associated with short-term outcomes. Compared to those with persistently low oxygenation levels, patients whose PaO2/FiO2 was initially low but improved and stabilized at a mildly reduced level within 96 h exhibited significantly lower risks of ICU and 28-day all-cause mortality. PaO2/FiO2 trajectories may serve as a valuable tool for early prognostic assessment in patients with sepsis.