Critical Care

ObjectivesThis study aims to evaluate the association between pre-Extracorporeal Membrane Oxygenation (ECMO) markers of impaired oxygen delivery, as quantified by the iDO2 predictive analytics platform, and neurologic outcomes in infants supported on ECMO. The goal is to determine whether these markers can inform decision-support systems for optimizing ECMO initiation timing. Materials & MethodsWe performed a single-center retrospective cohort study of infants <1 year supported on ECMO from 2013-2017, excluding cases with congenital diaphragmatic hernia or post-cardiac surgery ECMO. Data included demographics, clinical variables, and iDO2 estimates retroactively calculated in 120-minute intervals prior to ECMO initiation. Primary outcomes included mortality, EEG abnormalities, and head imaging findings; secondary outcomes included MRI abnormalities and Functional Status Scores (FSS). ResultsOf 219 patients, 47 met inclusion criteria. Median age and weight at ECMO initiation were 16 days [IQR 6-112] and 3.3 kg [IQR 2.8-4.8], with an overall mortality rate of 55%. Non-survivors had higher rates of congenital heart disease (77% vs. 42%, p=0.03) and pre-ECMO cardiac arrest (53% vs. 14%, p=0.006). Time spent above iDO2 thresholds of 25%, 50%, and 75% increased closer to ECMO initiation. Higher iDO2 dose correlated with adverse neurologic outcomes, including EEG abnormalities and abnormal imaging, and predicted poor composite functional outcomes (p<0.05). Discussion & ConclusionMarkers of impaired oxygen delivery, such as iDO2, may inform the development of decision-support systems to optimize ECMO timing, potentially improving neurologic outcomes. Further research is needed to validate these findings and develop decision-support systems for clinical practice.

BackgroundUntangling the heterogeneity of sepsis in children and identifying clinically relevant phenotypes could lead to the development of enrichment strategies and targeted therapies. In this study, our aim was to analyze the organ dysfunction-based trajectories of children with sepsis-associated multiple organ dysfunction syndrome (MODS) to identify and characterize reproducible and clinically relevant sepsis phenotypes using a data-driven approach. MethodsWe collected data from patients admitted with suspected infections to 13 pediatric intensive care units (PICUs) in the U.S. between 2012-2018. We used subgraph-augmented nonnegative matrix factorization to identify candidate trajectory-driven phenotypes based on the type, severity, and progression of organ dysfunction in the first 72 hours of PICU admission. We analyzed the candidate phenotypes to determine reproducibility as well as prognostic, therapeutic, and biological relevance. ResultsOverall, 38,732 children had suspected infection, of which 15,246 (39.4%) had sepsis-associated MODS. Amongst patients with sepsis-associated MODS, 1,537 (10.1%) died in the hospital. We identified an organ dysfunction trajectory-based phenotype (which we termed persistent hypoxemia and shock) that was highly reproducible, had features of systemic inflammation and coagulopathy, and was independently associated with higher mortality. In a propensity score matched analysis, patients with the persistent hypoxemia and shock phenotype appeared to have a higher likelihood to benefit from adjuvant therapy with hydrocortisone and albumin than other patients. When compared to other high-risk clinical syndromes, the persistent hypoxemia and shock phenotype only overlapped with 50 to 60% of patients with septic shock, those with moderate-to-severe pediatric acute respiratory distress syndrome, or those in the top tertile of organ dysfunction burden, suggesting that it represents a distinct clinical phenotype of sepsis-associated MODS with a disproportionately high risk of mortality. ConclusionsWe derived and validated the persistent hypoxemia and shock phenotype, a trajectory-based organ dysfunction phenotype which is highly reproducible, clinically relevant, and associated with heterogeneity of treatment effect to common adjuvant therapies. Further validation is warranted. Future studies are needed to validate this phenotype, assess whether it can be predicted earlier in the course, study possible biological mechanisms underlying it, and investigate candidate therapeutic targets.

We have previously shown that fresh frozen plasma (FFP) and fibrinogen have protective effects in mice with hemorrhagic shock through restoration of endothelial syndecan-1 and reversal of endothelial injury. In the current study, we tested the hypothesis that a combined model of abdominal sepsis and hemorrhagic shock would induce endothelial syndecan-1 shedding and lung injury which could be attenuated by both FFP and fibrinogen. C57BL/6 mice underwent cecal ligation and puncture (CLP) followed by hemorrhagic shock (HS) and fluid resuscitation with lactated Ringers (LR), fibrinogen (5 mg/mouse), and FFP, all at 1X shed blood volume. After 24 hours, lung tissues and plasma were harvested for assays. CLP+HS induced an increase in alveolar thickness and decreases in lung syndecan-1 and lung neutrophil granule-enzymes (myeloperoxidase, neutrophil elastase, and MMP9), with reciprocal elevations in plasma syndecan-1 and plasma neutrophil granule-enzymes (myeloperoxidase, neutrophil elastase, and MMP9). All these alterations were significantly attenuated by FFP but not by fibrinogen. Additionally, CLP+HS-induced hypotension at 24 hours was partially reversed by FFP but not by fibrinogen. FFP administration inhibits CLP+HS-induced neutrophil degranulation to prevent syndecan-1 shedding and lung injury. The current study supports that FFP has therapeutic benefit in a combined septic and hemorrhage shock model.

BackgroundTherapeutic effects of steroids on acute respiratory distress syndrome (ARDS) requiring mechanical ventilation (MV) have been reported. However, predictive indicators of early weaning from MV post-treatment have not yet been defined, making treating established ARDS challenging. Interleukin (IL)-6 has been associated with the pathogenesis of ARDS. ObjectiveOur aim was to clarify clinical utility of IL-6 level in ventilated patients with established ARDS. MethodsClinical, treatment, and outcome data were evaluated in 119 invasively ventilated patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-mediated ARDS. Plasma levels of IL-6 and C-reactive protein (CRP) were measured on days 1, 4, and 7 after intubation. ResultsFifty-two patients were treated with dexamethasone (steroid group), while the remaining 67 patients were not (non-steroid group). Duration of MV use was significantly shorter in the steroid group compared to non-steroid group (11.5{+/-}0.6 vs. 16.1{+/-}1.0 days, P = 0.0005, respectively) along with significantly decreased levels of IL-6 and CRP. Even when restricted to the steroid group, among variables post-MV, IL-6 level on day 7 was most closely correlated with duration of MV use (Spearmans rank correlation coefficient [{rho}] = 0.73, P < 0.0001), followed by CRP level on day 7 and the percentage change in IL-6 or CRP levels between day 1 and day 7. Moreover, among these variables, IL-6 levels on day 7 showed the highest accuracy for withdrawal from MV within 11 days (AUC: 0.88), with optimal cutoff value of 20.6 pg/mL. Consistently, the rate of MV weaning increased significantly earlier in patients with low IL-6 ([&le;] 20.6 pg/mL) than in those with high IL-6 (> 20.6 pg/mL) (log-rank test P < 0.0001). ConclusionsIn invasively ventilated patients with established ARDS due to SARS-CoV-2, plasma IL-6 levels served as a predictor of early withdrawal from MV after dexamethasone administration.

Rationale: Fibrinolysis resistance in sepsis associates with thrombotic burden, multi-organ failure and death. The degrees and dynamics of resistance that associate with mortality in acute sepsis are unknown, and a simple tool to aid clinician interpretation of fibrinolysis measurements is lacking. Objectives: To establish a point of care grading tool of fibrinolysis resistance that aligns with scoring systems for disease acuity, is substantiated by plasma fibrinolysis markers and enables rapid investigation of the fibrinolysis state at the point of care. Methods: Prospective observational study of 116 adult sepsis/septic shock patients with sequential measurements of fibrinolysis resistance during Intensive Care Unit (ICU) admission using tissue plasminogen activator (tPA) enhanced viscoelastic testing (VET). The clot lysis time (TPA-LT) adjusted for fibrin clot amplitude (TPA-LT/FIBA10, sec/mm) underwent cluster analysis and was evaluated against disease severity scores, standard pathology, clinical outcomes and fibrinolysis markers. Measurements and Main Results: Three clusters of progressively increasing fibrinolysis resistance were identified (Grades 1-3). At admission, Grade 3 associated with the highest disease severity, organ failure, haematological and biochemical perturbations, fibrinolysis marker inhibitory profile and mortality (42% versus 24% and 15% in Grade 2 and Grade 1, respectively) with a 3.9-fold [95% CI 1.4-11] increased hazard ratio for death at 28 days compared to Grade 1. Transitions between grades were frequent over 7 days with a reduced Grade associated with decreased risk of death. Conclusions: Grading of fibrinolysis resistance in sepsis enables rapid identification of patients at greatest mortality risk with any dynamic improvement corresponding to favourable clinical outcomes.

BackgroundSevere respiratory failure is a major complication of SARS-CoV-2 infection, and the need for mechanical ventilation (MV) is associated with a worse outcome. Whether some soluble biomarkers of lung injury can help predict MV requirement remains unclear. MethodsThis prospective, observational, monocentric cohort study consecutively enrolled patients with laboratory-confirmed COVID-19 pneumonia within 48 h of hospital admission. The serum concentrations of five key bronchoalveolar epithelial and endothelial biomarkers were determined at Day 0, 7 and 14: Krebs von den Lungen-6 (KL-6); soluble receptor for advanced glycation end-products (sRAGE); club cell protein 16 (CC16); angiopoietin-2 (Ang-2); and soluble CD146 (sCD146). The respiratory severity of COVID-19 pneumonia was defined by the maximal level of respiratory support received during hospitalization: oxygen (by mask or nasal prong); high flow oxygen therapy (HFOT); and MV. End-points were the need for MV during hospitalization and the time to liberation from oxygen. ResultsFifty-four COVID-19 patients were enrolled; 23 (43%) required MV, 13 (24%) HFOT, and 18 (33%) oxygen. At inclusion, levels of KL-6, sRAGE, and CC16 were significantly higher in MV compared with non-MV patients (p < 0.05), with sRAGE showing the greatest difference (2.4-fold increase). In multivariate logistic regression, sRAGE (OR per 100 pg/mL increase, 1.028 [95% CI, 1.004-1.054]; p = 0.022) and SpO2/FIO2 (OR, 0.984 [95% CI, 0.970-0.998]; p = 0.008) were identified as independent risk factors for MV. Furthermore, patients with an sRAGE [&ge;] 5449 pg/mL at inclusion had a lower probability of weaning from oxygen at Day 60 (HR, 0.36 [95% CI, 0.19-0.67]; p = 0.001). From Day 7 to Day 14, CC16 levels increased while sCD146 levels decreased in MV patients. ConclusionAmong five circulating biomarkers of bronchoalveolar injury, sRAGE showed the most favorable kinetic profile, rapidly increasing in MV patients. The early measurement of sRAGE and SpO2/FIO2 upon hospital admission may effectively identify COVID-19 patients at high risk of requiring MV and prolonged oxygen support.

BackgroundSepsis therapy is still limited to treatment of the underlying infection and supportive measures. To date, various sepsis subtypes were proposed, but therapeutic options addressing the molecular changes of sepsis were not identified. With the aim of a future individualized therapy, we used machine learning (ML) to identify clinical phenotypes and their temporal development in a prospective, multicenter sepsis cohort and characterized them using plasma proteomics. MethodsRoutine clinical data and blood samples were collected from 384 patients. Sepsis phenotypes were identified based on clinical measurements and plasma samples from 301 patients were analyzed using mass spectrometry. The obtained data were evaluated in relation to the phenotypes, and supervised ML models were developed enabling prospective phenotype classification and determination of key features distinguishing the phenotypes. ResultsThree phenotypes and their progression across four time points in sepsis were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure. Cluster B showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. Time course analysis showed a strong association of cluster C with mortality and dynamic properties of cluster B. The plasma proteome reflected the clinical features of the phenotypes and revealed excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML models allow the assignment of patients based on only seven widely available features. ConclusionsThe identified clinical phenotypes reflected varying degrees of sepsis severity and were mirrored in the plasma proteome. Proteomic profiling offered novel insights into the molecular mechanisms underlying sepsis and enabled a deeper characterization of the identified phenotypes. This integrative approach may serve as a blueprint for uncovering molecular signatures of sepsis subgroups and holds promise for the development of future targeted therapies.

BackgroundDelirium is a serious complication in patients with COVID-19-related acute respiratory distress syndrome (ARDS) admitted to the intensive care unit (ICU). Although numerous clinical risk factors have been identified, the immunologic pathways underlying delirium remain unclear. In this retrospective cohort study, we investigated high-dimensional immune signatures in ICU patients to delineate peripheral immune markers associated with delirium. We also explored machine learning (ML) approaches to enhance biomarker discovery and strengthen predictive modelling through synthetic data generation. MethodsSixty-two COVID-19 ARDS patients admitted to the ICU at Lausanne University Hospital, Switzerland, were studied; 39 (63%) developed delirium. Control cohorts consisted of 55 non-ICU COVID-19 patients and 450 healthy individuals. We performed high-dimensional immunophenotyping of cytokines, chemokines, and growth factors using multiplex beads assay, along with immune cell profiling via mass cytometry (CyTOF). Ridge regression has been employed to build classification models. To address the limited sample size and improve model stability, we generated a synthetic dataset using beta-variational autoencoders. ResultsDelirious patients exhibited a distinctive immune signature, including elevated CXCL1, CCL11, CXCL13, HGF, and VEGF-A, coupled with reduced IL-1, IL-21, and IL-22. Alterations in immune cell populations featured increased exhausted B cells and decreases in CXCR3+ CD4+ T cells, IgM+ unswitched memory B cells, and HLA-DR+ activated T cells. Leveraging these high-dimensional data, we trained ridge regression models to predict delirium. Incorporating synthetic data helped stabilize the models with a best-performing model achieving an area under the curve (AUC) of 0.95, with high sensitivity (93%) and specificity (86%), based on 12 identified markers. ConclusionsOur findings demonstrate a distinct immune profile linked to ICU delirium and illustrate how ML can enhance biomarker discovery. Further prospective validation may refine these markers and guide precision-targeted interventions for mitigating delirium in critically ill populations. Graphical Abstract

BackgroundSepsis-induced immunosuppression, characterized by lymphopenia, is associated with adverse outcomes. We aimed to identify distinct lymphocyte recovery patterns in patients with sepsis, evaluate their association with mortality, and develop a machine learning model to enhance prediction MethodsThis retrospective cohort study included adult patients with sepsis and initial lymphopenia (Absolute Lymphocyte Count [ALC] < 1.0 x 10L/L) from the Medical Information Mart for Intensive Care (MIMIC)-IV database. We defined three lymphocyte recovery patterns: "Persistent Suppression," "Partial Recovery," and "Complete Recovery." The primary outcome was 90-day all-cause mortality. Multivariable Cox models were used to assess the association between recovery patterns and mortality. An Extreme Gradient Boosting (XGBoost) model was developed to predict 90-day mortality. Results90-day mortality was highest in the Persistent Suppression group (49.1%) versus Partial (41.3%) and Complete Recovery (35.7%) groups (p<0.001). Persistent Suppression remained an independent predictor of mortality (adjusted Hazard Ratio 1.31, 95% CI 1.10-1.55). The XGBoost model achieved superior discrimination (Area Under the Receiver Operating Characteristic Curve [AUROC]=0.767) over traditional scores. SHAP analysis confirmed that dynamic features, including lymphocyte recovery metrics, were key model drivers. The model also demonstrated robust performance across various clinical subgroups (e.g., age, disease severity). ConclusionsThe trajectory of lymphocyte recovery following sepsis onset is an independent prognostic marker. Failure to restore lymphocyte counts is strongly associated with increased long-term mortality. Integrating this dynamic immunological feature into machine learning algorithms significantly enhances predictive accuracy, offering a promising tool for real-time risk stratification.

ImportanceEarly identification of fever or hypothermia is crucial for diagnosing sepsis. Despite their increased use across healthcare systems, concerns have been raised about the accuracy of temporal thermometers among Black patients. ObjectiveTo study the performance of temporal thermometry across race and ethnicity, and its impact on the initiation of the sepsis management bundle (SEP-1). DesignIn this retrospective cohort study, records from 2008-2024 in 123 U.S. hospitals were analyzed, including electronic health records from Duke, MIMIC-IV, eICU-CRD-1, and eICU-CRD-2. Patients were included if they had a temporal measurement within one hour of an oral/core measurement (esophageal, bladder, rectal). Main Outcomes and MeasuresHidden Fever was defined when the temporal thermometer read [&le;] 38{degrees}C but oral/core measurement > 38{degrees}C and Hidden Hypothermia as temporal temperature [&ge;] 36{degrees}C but oral/core < 36{degrees}C. The primary outcome was Hidden Hyper/Hypothermia (HHH) when either Hidden Fever or Hidden Hypothermia happened. Observed Hyper/Hypothermia (OHH) corresponded to an agreement between both measurements, used as a control. To study the impact of temporal thermometry underperformance on SEP-1 bundle initiation, we assessed time to culture ordering, antibiotics administration, and lactate measurement. A composite of these three, whichever happened first, was defined as our secondary outcome. Results6,921 paired temperature measurements were studied for the primary outcome analysis, corresponding to 4,248 hospitalizations (2.6% Asian; 12.4% Black; 2.6% Hispanic/Latino; 83.4% White; 44.9% female; 7.7% with HHH) from 115 hospitals. After adjusting for confounders, Black [OR (95% CI): 1.760 (1.219, 2.541), p=0.003] and Hispanic/Latino [OR (95% CI): 2.183 (1.226, 3.888), p=0.008] patients were significantly more likely to present with HHH than White patients. For the study of the secondary outcome, 434 patients had either OHH or HHH. Compared to patients with OHH, patients with HHH had significantly delayed secondary outcomes, with log-rank p-value of 0.002. Conclusions and RelevanceSolely relying on temporal thermometry can lead to missed hyper/hypothermia events, especially among Asian, Black, and Hispanic patients. This has been associated with delays in the initiation of the SEP-1 bundle, which may hamper health systems compliance with reimbursement programs. Key PointsO_ST_ABSQuestionC_ST_ABSAre there racial differences in temporal thermometry that result in missed hyper/hypothermia, and are these associated with delays in sepsis bundle care (SEP-1) initiation? FindingsIn this multicenter retrospective cohort study of 4,248 hospitalizations, Asian, Black, and Hispanic patients were more likely to have hidden hyper/hypothermia when using temporal thermometers, as compared to oral/core measurements. These missed events were associated with delayed SEP-1 interventions (three out of four were studied: culture orders, antibiotic administration, and lactate measurements). MeaningReliance on temporal thermometry could exacerbate health disparities and hamper hospital systems compliance with reimbursement programs. Temporal thermometers should therefore be routinely validated with contact thermometers.

IntroductionSepsis diagnosis remains clinical and heterogeneous. We hypothesized that a proteomics-informed machine-learning approach could identify a small, easy-to-use, and optimized set of clinical variables to complement or potentially outperform SOFA. MethodsWe conducted a prospective, single-center, observational study in an academic intensive care unit. Plasma from critically ill patients with and without sepsis was analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS). Data were acquired with data-independent acquisition parallel accumulation- serial fragmentation (diaPASEF) and processed using DIA-NN software. Differentially expressed proteins informed model development. Random Forest models were trained in a Discovery cohort (n=55) to select clinical variables linked to the proteome, then tested in an independent Validation cohort (n=59). Recursive feature elimination (RFE) identified a minimal feature set that was predictive of sepsis. The performance was assessed using repeated cross-validation and external validation. ResultsTwelve plasma proteins differed between sepsis and non-sepsis patients at FDR < 0.1, corresponding to 26 proteome-enriched clinical variables. The classifier achieved mean AUCs of 0.73 and 0.76 in Discovery and Validation cohorts, respectively. RFE performance plateaued with [&ge;]9 variables, peaked at an accuracy of 0.78, and deteriorated below seven; the final three features before collapse were plasma BUN, chemokine ligand 3 (CCL3), and creatinine. Proteome-to-clinical regression highlighted creatinine as having the strongest correlation (R{superscript 2} = 0.558). Discussion: A concise set of routinely obtainable variables anchored by renal markers and CCL3 captured proteomic signals and discriminated sepsis across cohorts, supporting a "proteomics-informed, clinic-first" strategy for pragmatic EHR deployment. While larger multicenter studies are warranted, these findings suggest that renal dysfunction exerts a disproportionate influence on sepsis and that increased emphasis on kidney-related markers may improve both recognition and risk assessment.

BackgroundRespiratory distress requiring intubation is the most serious complication associated with coronavirus disease 2019 (COVID-19). MethodsIn this retrospective study, we used survival analysis to determine whether or not mortality following intubation was associated with hormone exposure in patients treated at New York Presbyterian/ Columbia University Irving Medical Center. Here, we report the overall hazards ratio for each hormone for exposure before and after intubation for intubated and mechanically ventilated patients. ResultsAmong the 189,987 patients, we identified 948 intubation periods across 791 patients who were diagnosed with COVID-19 or infected with SARS-CoV2 and 3,497 intubation periods across 2,981 patients who were not. Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02 - 0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142 - 0.542, p-value = 1.72E-04) intubated patients. Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02 - 0.269, p-value = 7.15E-08). ConclusionsMelatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients. Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation. While our models account for many covariates, including clinical history and demographics, it is impossible to rule out confounding or collider biases within our population. Further study into the possible mechanism of this observation is warranted.

Background: Many patients in the ICU receive oxygen to secure blood and tissue oxygenation. Increasing evidence shows exposure to high fractions of inhaled oxygen (FiO2) being associated with adverse effects. In patients with severe ARDS, veno-venous Extracorporeal Membrane Oxygenation (VV-ECMO) can be implemented as a rescue therapy and PaO2 levels can be controlled by the blood flow of the VV-ECMO. Yet, optimal oxygenation targets in ARDS patients treated with VV-ECMO are unknown. Methods: Retrospective analysis of 443 patients with severe ARDS treated with VV-ECMO. Regression analyses were performed for mortality and time-weighted averages of PaO2 and FiO2. Furthermore, considering a possible non-linear relationship, a restricted cubic spline (RCS) model was performed for PaO2. Results: A simple logistic regression for mean PaO2 and ICU mortality showed a significant positive association (per mmHg OR 0.99 [95%CI 0.98-1.00], p=0.002). RCS analysis showed a U-shaped association of mortality and mean paO2 (paO2 69.70-90.24mmHg: OR 0.92 [95%CI 0.89-0.94], p<0.001; paO2 90.24-123.40mmHg: OR 1.09 [95%CI 1.06-1.13], p<0.001). A model including PaO2 as RCS variable and FiO2 showed significant associations of mortality with both variables (PaO2 69.70-90.24mmHg: OR 0.94 [95%CI 0.91-0.97], p<0.001; paO2 90.24-123.40 mmHg: OR 1.07 [95%CI 1.04-1.11], p<0.001; FiO2: OR 35.98 [95%CI 8.67-158.60], p<0.001, VIF<1.11). Conclusions: PaO2-levels in patients with ARDS and VV-ECMO have a U-shaped association with mortality. Optimal outcomes are observed in the 90-123 mmHg range, which is higher compared to non-ECMO settings. Whether this is explainable by increased tissue oxygenation with concurrent avoidance of pulmonary hypoxia should be subject of future research.

Overview: SeptiCyte RAPID is an FDA-cleared gene expression test that quantifies host immune response to aid in the diagnosis of sepsis. The test yields a score (the SeptiScore) ranging from 0-15, distributed across four bands (1-4) based on increased likelihood of sepsis. Each band can be characterized by average positive and negative likelihood ratios (LR+, LR- respectively) for the discrimination of sepsis versus the non-infectious systemic inflammatory response syndrome (SIRS). Methods: A retrospective analysis of prospectively collected data from a combined cohort of critically ill patients suspected of sepsis (N=889), recruited across 19 hospitals in the USA and Europe. The analysis quantified the LR+ and LR- parameters as a function of SeptiScore, for discrimination of sepsis vs. SIRS in patients admitted to ICU. Hypotheses: (1) The likelihood ratio (LR) framework provides a clinically useful interpretive approach that complements the previously used SeptiScore banding scheme; (2) Low Band 1 SeptiScores are associated with sufficiently small LR- to support the use of SeptiCyte RAPID as a rule-out test for sepsis; (3) High Band 4 SeptiScores are associated with sufficiently large LR+ to support the use of SeptiCyte RAPID as a rule-in test for sepsis; and (4) SeptiScore-derived LR+ and LR- values can be combined with estimates of pre-test probability (derived from patient characteristics and/or other diagnostic tests) to generate individualized, patient-specific post-test probabilities of sepsis. Results: The SeptiCyte RAPID test demonstrates strong diagnostic performance in distinguishing sepsis from SIRS. The likelihood ratios across different score bands provide clear clinical utility: the median LR+ was 3.26 (range 2.57-4.24) for Band 3, and 6.97 (range 4.35-15.57) for Band 4 providing evidence toward ruling in sepsis at high SeptiScores. Conversely, the median LR- was 0.16 (range 0.14-0.20) for Band 2 and 0.085 (range 0.014-0.16) for Band 1, providing evidence toward ruling out sepsis at low SeptiScores. A higher-resolution analysis of SeptiCyte RAPID performance confirmed these trends by evaluating LR+ and LR- at specific values within each band. The sepsis group was further stratified according to whether patients were classified as blood-culture positive (BC+) or blood culture negative (BC-), and the detailed LR+ and LR- analyses were repeated. A monotonic increase in likelihood ratio with increasing SeptiScore was consistently observed, independent of whether sepsis patients were culture-positive, culture-negative, or unstratified with respect to blood culture status. Conclusion: High SeptiScores have correspondingly high LR+ values, and low SeptiScores have correspondingly low LR- values, both of which may have clinical utility. High likelihood ratios for band 4 SeptiScores, which precede traditional microbiology results, may provide clinicians with early confidence of a sepsis diagnosis and microbiology diagnostic stewardship. Low likelihood ratios for band 1 SeptiScores may prompt clinicians to consider an alternate diagnosis to sepsis. Such results, obtained early in the diagnostic workup process, may lead to fewer missed diagnoses and more efficient use of hospital resources.

Adrenal crises occur due to either a complete lack or an insufficient amount of cortisol and are usually triggered by stressors like infections, trauma, surgical interventions, and dental procedures. The physiological changes associated with major surgery are well-studied stressors linked to adrenal crisis. Extracorporeal membrane oxygenation (ECMO), a strategy employed to assist with circulatory support and gas exchange, is also a significant physiologically demanding stressor to the human body. In this study, we retrospectively assess the incidence and outcomes of patients with adrenal insufficiency who were cannulated for extracorporeal membrane oxygenation (ECMO) using the National Inpatient Sample (NIS) database. Patients who were cannulated for ECMO from 2016 to 2020 were identified and divided into two cohorts depending on the presence or absence of adrenal insufficiency. The two groups were compared for the primary outcome of inpatient mortality and secondary outcomes including rates of LVAD, heart transplant, lung transplantation, stroke, GI bleed, and renal failure. In our study, we found that mortality rates between the two groups were similar. All-cause inpatient mortality was 45% in both groups (p>0.9). We found no differences in the rates of heart transplantation, lung transplantation, or LVAD implantation between patients with and without adrenal insufficiency. We additionally find that mortality rates in our study mirror those from dedicated ECMO registries, further lending credence to our findings. Our study discusses important findings from a relatively understudied disease process, based on a large and representative population from multiple centers across the country.

BACKGROUNDTrauma remains a leading cause of morbidity and mortality in part due to secondary organ injury and infection. Yet, our ability to predict the downstream pathophysiologic responses leading to organ injury and adverse outcomes is limited. Extracellular microRNAs (ex-miRNAs) as a DAMP can drive innate immune response and organ injury. Here, we tested plasma miRNAs as predictive biomarkers for organ injury in trauma. METHODSTwelve miRNAs were selected based on RNAseq and pro-inflammatory nucleotide motifs identified by machine learning. Digital PCR was employed to quantify plasma miRNAs and Luminex to measure trauma injury markers. Multivariate Random Forest models were built to assess the predictive performance of the miRNA biomarkers. RESULTSWe identified a set of five nucleotide motifs that can predict the pro-inflammatory property of plasma miRNAs with a sensitivity of 84% and specificity of 69%. There was a marked and severity-dependent increase in the plasma miRNA biomarkers and numerous trauma injury markers at time of admission. The plasma concentrations of these miRNA biomarkers were highly correlated with the injury markers linked to various trauma endotypes. AUROC analyses indicated that the miRNA biomarkers possess strong diagnostic abilities and prediction in overall severity, organ injury, metabolic acidosis, coagulopathy, and innate inflammation in the trauma (n=48) but not sepsis (n=47) cohort. In a combined cohort, miR-224-5p and miR-145-5p exhibited a superior performance in differential diagnosis of trauma and sepsis with AUROC of 0.90 and 0.91, respectively. CONCLUSIONThe panel of plasma miRNAs are specific biomarkers with strong diagnostic and prognostic performance in trauma-induced organ injury.

BackgroundEarly lactate is widely used to risk-stratify septic shock, yet clinically actionable cut-offs for 28-day mortality remain uncertain. MethodsIn a single-centre study conducted across two intensive care units, we analysed 84 adults with septic shock identified within 24 hours of intensive care unit admission. The primary endpoint was 28-day mortality. Four lactate metrics obtained during the first 24 hours were evaluated: first (admission) lactate, last lactate, peak lactate, and lactate clearance from first to last. Associations were tested using logistic regression with and without adjustment for the Simplified Acute Physiology Score 3; discrimination was assessed by area under the receiver-operating characteristic curve (AUROC), and optimal cut-offs were defined by the Youden index. ResultsThirty-nine of 84 patients (46.4%) died by day 28. Higher absolute lactate values were independently associated with death (adjusted odds ratio (OR) per 1 mmol/L increase: First 1.47, p<0.001; Last 1.41, p=0.002; Peak 1.39, p<0.001), whereas Lactate clearance was not (OR 0.65, p=0.202). Discrimination was moderate to good for peak (AUROC 0.817), first (0.791), and last (0.757) lactate, and poor for clearance (0.577). Youden-derived thresholds provided pragmatic trade-offs: First 3.55 mmol/L (sensitivity 0.821, specificity 0.689), Last 3.15 mmol/L (0.567, 0.864), and Peak 3.55 mmol/L (0.973, 0.556). Kaplan-Meier curves using these cut-offs showed early and sustained separation. ConclusionsIn adults with septic shock, simple early lactate thresholds around 3.3- 3.6 mmol/L (first/peak) and approximately 3.15 mmol/L (last) identify 28-day mortality risk and outperform lactate clearance.

BackgroundHyperglycemia and hypoglycemia are key risk factors for morbidity and mortality in critically ill septic patients. Despite ongoing research, glucose management guidelines for critically ill patients remain inconsistent, especially for diabetic patients. This study identifies optimal glucose targets to reduce mortality in critically ill patients with sepsis. Research QuestionWhat is the optimal blood glucose range associated with reduced in-hospital mortality for critically ill patients with sepsis, and how does this vary by diabetic status? Study Design and MethodsThis cohort study analyzed 22,374 adult intensive care unit (ICU) patients with sepsis from the MIMIC-IV database. Non-linear logistic regression models assessed the relationship between 72-hour median blood glucose levels and in-hospital mortality, adjusting for age, gender, and Sequential Organ Failure Assessment (SOFA) score. Subgroup analyses explored variations based on diabetic status and other clinical factors. ResultsThe study found a U-shaped relationship between blood glucose levels and mortality, with the lowest risk at 6.3 mmol/L overall and 6.8 mmol/L for diabetic patients. A 5-8 mmol/L glucose range during the first 72 hours was associated with a mortality risk below 10%, representing up to a 5% reduction in mortality compared to the guideline targets of 7.8-10.0 mmol/L. No significant differences were found between patients with or without skin and soft tissue infection. InterpretationThe findings suggest that a tighter glycemic control range of 5-8 mmol/L could improve survival in ICU patients with sepsis, challenging current guidelines. Further randomized controlled trials are necessary to validate and optimize glycemic control strategies for critically ill septic patients. Clinical Trial RegistrationNot applicable. Take-Home PointsO_ST_ABSStudy QuestionC_ST_ABSWhat is the optimal blood glucose level for reducing mortality in critically ill patients with sepsis? ResultsA U-shaped relationship was found, with the lowest mortality risk at 6.3 mmol/L overall and 6.8 mmol/L for people with diabetes. Patients with a 5-8 mmol/L glycemic range had an approximate 50% reduced mortality risk compared to those who maintained glycemic ranges within existing guideline recommendations. InterpretationUnlike the guideline-recommended glucose targets of 7.8 - 10mmol/L, a tighter glycemic target (5-8 mmol/L) may improve mortality for critically ill patients with sepsis.

Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: "Rapidly Improving" (RI) and "Rapidly Worsening," (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.

Objective We aimed to evaluate the performance of the recently updated Sequential Organ Failure Assessment Score-2 (SOFA-2) on organ dysfunction classification and prognostication compared to SOFA-1 in critically injured trauma patients. Methods Adult trauma patients admitted to critical care at four urban Major Trauma Centres between 2011 and 2024 were included. Daily organ dysfunction scoring was performed using SOFA-1 and SOFA-2 until death or discharge. The primary outcome was MODS, defined as SOFA score [&ge;]6. Results In 2162 severely injured patients (median Injury Severity Score 25 [IQR, 17-34]), SOFA-2 reduced the proportion of patients classified as having MODS compared with SOFA-1 (61.6% vs 68.5%, p<0.001). SOFA-2 scores on the first day after admission were lower than SOFA-1 (median 6 [IQR, 3-8] vs 7 [IQR, 4-10], p<0.001), driven predominantly by lower respiratory and cardiovascular scoring. Critical care mortality in trauma patients was increased in respiratory, cardiovascular and renal components of SOFA-2 at the higher ends of the scores, consistent with the aims of the SOFA-2 reclassification. A group of 159 severely injured patients (7.3%) classified as MODS by SOFA-1 were reclassified to no-MODS by SOFA-2. Despite this reclassification, these patients had substantially higher ICU mortality (7.5% vs 0.7%, p<0.01), greater ventilator and vasopressor requirements, and longer hospital stays than patients classified as no-MODS by both systems. Conclusions SOFA-2 reduces MODS prevalence in severely injured patients and changes organ dysfunction classification, with lower rates of severe respiratory and cardiovascular dysfunction. This represents an important update in trauma MODS measurement and has implications for future trauma trial design. However SOFA-2 reclassification generates a small cohort a small but clinically significant group with occult MODS that warrants further evaluation in severely injured trauma patients.