Sepsis Cohort Research Articles

The Relationship Between Neighbourhood Deprivation and Mortality in a Sepsis Cohort in England: A Retrospective Observational Study

The Relationship Between Neighbourhood Deprivation and Mortality in a Sepsis Cohort in England: A Retrospective Observational Study

Association between alactic base excess on mortality in sepsis patients: a retrospective observational study

BackgroundSepsis is a life-threatening condition often associated with metabolic and acid–base imbalances. Alactic base excess (ABE) has emerged as a novel biomarker to assess metabolic disturbances in critically ill sepsis patients, but its prognostic value remains underexplored. We aimed to investigate the relationship between ABE and 30-day/90-day ICU all-cause mortality in a large sepsis cohort in the intensive care unit (ICU) setting.MethodsThis study utilised data from a large US ICU sepsis cohort. ABE was calculated as the sum of lactate and base excess (BE) values from the first day of ICU admission. Patients were divided into quartiles based on ABE values. Kaplan–Meier survival analysis, Cox proportional hazards models, and restricted cubic spline analyses were used to examine the associations between ABE and mortality outcomes. The predictive performance of ABE combined with the SOFA score was assessed using the area under the curve, Net Reclassification Improvement, and Integrated Discrimination Improvement.Results17,099 patients with sepsis were included in this analysis, with median (IQR) age of 67.82 (56.80, 78.04) years and 59.7% males. Our analysis revealed a U-shaped association between ABE and 30-day and 90-day ICU all-cause mortality. Both the lowest (Q1) and highest (Q4) quartiles of ABE were linked to increased mortality risks, with 30-day mortality showing HRs of 1.27 (95% CI 1.13–1.44) for Q1 and 1.17 (95% CI 1.06–1.31) for Q4, while 90-day mortality showed HRs of 1.28 (95% CI 1.16–1.44) for Q1, 1.12 (95% CI 1.02–1.23) for Q2, and 1.22 (95% CI 1.11–1.34) for Q4. ABE demonstrated superior predictive performance for mortality compared to BE and lactate. Incorporating ABE into the SOFA score improved predictive performance, emphasizing ABE’s value in better risk stratification. The identified thresholds (2.5 mmol/L for 30-day mortality and 2.2 mmol/L for 90-day mortality) indicate optimal ABE levels that may be associated with improved survival outcomes.ConclusionsABE demonstrated a U-shaped association with 30-day and 90-day ICU all-cause mortality in critically ill sepsis patients, suggesting its superiority over BE and lactate as a predictive biomarker. Incorporating ABE with the SOFA score may further enhance prognostic predictions. Further studies are needed to determine whether ABE should serve solely as a biomarker for monitoring the clinical course or could also be considered a potential therapeutic target.

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

C-reactive protein to platelet ratio as an early biomarker in differentiating neonatal late-onset sepsis in neonates with pneumonia

Neonates with pneumonia (NWP) may experience unidentified life-threatening sepsis, yet distinguishing NWP from neonates with sepsis (NWS) based solely on clinical presentation remains challenging. This study aimed to evaluate the diagnostic utility of the C-reactive protein to platelet ratio (CPR) in distinguishing neonatal late-onset sepsis (LOS) among NWPs. From February 2016 to March 2022, a total of 1385 NWPs aged over 3 days were included. Of these, 174 neonates with confirmed positive blood cultures were categorized into the sepsis cohort, while the remainder formed the pneumonia cohort. All clinical data were retrospectively extracted from electronic medical records. CPR was calculated as the ratio of C-reactive protein levels to platelet count. Independent risk factors (IRFs) for neonatal LOS were identified through multivariate logistic regression. The diagnostic performance of CPR in identifying LOS among NWPs was analyzed using receiver operating characteristic (ROC) curve metrics. Statistical analyses were conducted using SPSS version 24.0 and MedCalc version 15.2.2. Neonates with NWS demonstrated significantly higher CPR compared to those with NWP alone. Further analysis revealed a notably increased incidence of sepsis among neonates exhibiting elevated CPR levels relative to those with lower values. Correlation analysis identified a direct association between CPR and elevated procalcitonin, creatinine, and urea nitrogen levels, as well as prolonged hospitalization. Multiple logistic regression analysis identified CPR as an IRF for late-onset NWS. ROC curve analysis demonstrated that CPR outperformed CRP and platelet count individually in diagnosing NWS, with a diagnostic sensitivity of 54% and specificity of 85%. CPR serves as an effective initial diagnostic marker with superior accuracy in distinguishing delayed NWS from NWP compared to CRP and platelet count alone.

Sepsis in silico: definition, development and application of an electronic phenotype for sepsis.

Repurposing electronic health record (EHR) or electronic medical record (EMR) data holds significant promise for evidence-based epidemic intelligence and research. Key challenges include sepsis recognition by physicians and issues with EHR and EMR data. Recent advances in data-driven techniques, alongside initiatives like the Surviving Sepsis Campaign and the Severe Sepsis and Septic Shock Management Bundle (SEP-1), have improved sepsis definition, early detection, subtype characterization, prognostication and personalized treatment. This includes identifying potential biomarkers or digital signatures to enhance diagnosis, guide therapy and optimize clinical management. Machine learning applications play a crucial role in identifying biomarkers and digital signatures associated with sepsis and its sub-phenotypes. Additionally, electronic phenotyping, leveraging EHR and EMR data, has emerged as a valuable tool for evidence-based sepsis identification and management. This review examines methods for identifying sepsis cohorts, focusing on two main approaches: utilizing health administrative data with standardized diagnostic coding via the International Classification of Diseases and integrating clinical data. This overview provides a comprehensive analysis of current cohort identification and electronic phenotyping strategies for sepsis, highlighting their potential applications and challenges. The accuracy of an electronic phenotype or signature is pivotal for precision medicine, enabling a shift from subjective clinical descriptions to data-driven insights.

A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data. The TECO model was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality and was validated externally in an acute respiratory distress syndrome cohort (n = 2799) and a sepsis cohort (n = 6622) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Model performance was evaluated based on the area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost). In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the 2 MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.77) than RF (0.59-0.75) and XGBoost (0.59-0.74). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome. The TECO model outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among patients with COVID-19, widespread inflammation, respiratory illness, and other organ failures. The TECO model demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.

Risk of new-onset dementia following COVID-19 infection: a systematic review and meta-analysis.

Emerging evidence suggests coronavirus disease 2019 (COVID-19) infection may increase the risk of developing dementia, although studies have reported conflicting findings. This meta-analysis aimed to synthesise the literature on the association between COVID-19 and the risk of new-onset dementia. PubMed, Embase and Web of Science were searched for cohort studies or case-control studies that investigated new-onset dementia development among adult COVID-19 survivors compared to individuals without COVID-19 infection from inception to 9 November 2023. Studies that exclusively involved populations younger than 18years, with known dementia or lacked adequate data about the risk of dementia were excluded. Two authors independently conducted the screening of eligible studies, data extraction and risk of bias assessment. The primary outcome was new-onset dementia following COVID-19 infection. Data were pooled using random-effects models, with hazard ratios (HRs) and 95% confidence intervals (CIs) calculated. A total of 15 retrospective cohort studies encompassing 26408378 participants were included. Pooled analysis indicated COVID-19 was associated with an increased risk of new-onset dementia (HR = 1.49, 95% CI: 1.33-1.68). This risk remained elevated when compared with non-COVID cohorts (HR = 1.65, 95% CI: 1.39-1.95), and respiratory tract infection cohorts (HR = 1.29, 95% CI: 1.12-1.49), but not influenza or sepsis cohorts. Increased dementia risk was observed in both males and females, as well as in individuals older than 65years (HR = 1.68, 95% CI: 1.48-1.90), with the risk remaining elevated for up to 24months. This meta-analysis demonstrates a significant association between COVID-19 infection and increased risk of developing new-onset dementia, which underscores the need for cognitive monitoring and early intervention for COVID-19 survivors to address potential long-term neurological impacts.

Epidemiology and prognosis of sepsis in cancer patients: A multicenter prospective observational study.

Epidemiology and prognosis of sepsis in cancer patients: A multicenter prospective observational study.

P-879. Predicting Antimicrobial Resistance and Empiric Treatment Failure in Hospital-Onset Sepsis Using Machine Learning on Electronic Health Records

Abstract Background Antimicrobial resistance (AMR) is a major cause of treatment failure in hospital-onset sepsis. Approaches to guide empiric antimicrobial therapy are urgently needed. We utilized machine learning to predict empiric treatment failure and AMR patterns within a high-risk hospital-onset sepsis cohort using electronic health records (EHRs). Methods We examined hospitalizations with documented bacterial infection from 2010 to 2023 at a tertiary-care academic hospital system. Hospital-onset sepsis was defined using CDC Adult Sepsis Event criteria. Empiric therapy was considered adequate if all pathogens isolated were susceptible to antimicrobials administered. AMR patterns included methicillin (MRSA), extended-spectrum beta-lactamase (ESBL), vancomycin (VRE), ceftriaxone, and carbapenem (CRE). Features for prediction were patient characteristics, hospitalization details, and clinical severity metrics measured within 24 hours prior to sepsis onset. Linear and non-linear machine learning models were evaluated using bootstrap validation and out-of-bag area under the receiver operating characteristic curve (AUC). Results Among 49,581 hospitalizations, 2019 (4%) hospital-onset sepsis encounters were identified. Mean (SD) age was 65 (16.4) years and 900 (45%) were female. At 24 hours after sepsis onset, 377 (19%) received inadequate empiric treatment and 911 (45%) infections expressed AMR. Those who received inadequate empiric treatment were 8.2 (95%CI: 6.2-10.9) times more likely to have an AMR infection. Random forest model was best-performing, with AUC (95%CI) for any AMR pattern, VRE, and CRE at 0.64 (0.63-0.64), 0.71 (0.69-0.72), and 0.70 (0.67-0.73), respectively. The AUC (95%CI) for predicting inadequate empiric antimicrobial treatment was 0.65 (0.64-0.66) with a sensitivity of 64% (58-69%) and specificity of 52% (46-68%) at optimal probability thresholds. Conclusion In a hospital-onset sepsis cohort with prevalent AMR, machine learning approaches utilizing limited EHR data accessible at initial sepsis recognition had moderate, but clinically insufficient, discriminatory ability in predicting AMR and empiric antimicrobial treatment failure. Future research will evaluate the utilization of large language models using unstructured EHRs. Disclosures All Authors: No reported disclosures

A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data. TECO was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality, and was validated externally in an ARDS cohort (n=2799) and a sepsis cohort (n=6622) from the Medical Information Mart for Intensive Care (MIMIC)-IV. Model performance was evaluated based on area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost). In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the two MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.76) than RF (0.57-0.73) and XGBoost (0.57-0.73). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome. TECO outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among COVID-19 and non-COVID-19 patients. TECO demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.

Novel Identification of CD74 as a Biomarker for Diagnosing and Prognosing Sepsis Patients.

Sepsis, a life-threatening inflammatory condition due to an imbalanced response to infections, has been a major concern. Necroptosis, a newly discovered programmed cell death form, plays a crucial role in various inflammatory diseases. Our study aims to identify necroptosis - related genes (NRGs) and explore their potential for sepsis diagnosis. We used weighted gene co-expression network analysis to identify gene modules associated with sepsis. Cox regression and Kaplan-Meier methods were employed to assess the diagnostic and prognostic value of these genes. Single-cell and immune infiltration analyses were carried out to explore the immune environment in sepsis. Plasma CD74 protein levels were quantified in our samples, and relevant clinical data from electronic patient records were analyzed for correlation. CD74 was identified through the intersection of the hub genes of sepsis and NRGs related modules. Septic patients had lower CD74 expression compared to healthy controls. The CD74-based diagnostic model showed better performance in the training dataset (AUC, 0.79 [95% CI, 0.75-0.84]), was cross-validated in external datasets, and demonstrated better performances than other published diagnostic models. Pathway analysis and single-cell profiling supported further exploration of CD74-related inflammation and immune response in sepsis. This study presents the first quantitative assessment of human plasma CD74 in sepsis patients. CD74 levels were significantly lower in the sepsis cohort. CD74 warrants further exploration as a potential prognostic and therapeutic target for sepsis.

Mortality-associated plasma proteome dynamics in a prospective multicentre sepsis cohort.

Mortality-associated plasma proteome dynamics in a prospective multicentre sepsis cohort.

Role of serum procalcitonin in differentiating disease flare and systemic bacterial infection among febrile children with known chronic rheumatic diseases: a cross-sectional study.

To evaluate the role of serum procalcitonin (PCT) as a diagnostic tool to differentiate bacterial sepsis from flare-ups during febrile episodes in children with known rheumatic disorders compared to other inflammatory markers like C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Previously diagnosed patients with known rheumatic disorders presenting in emergency or outpatient departments with febrile episodes were included in the study. Blood samples were collected upon admission to test for signs of infection, including serum PCT levels with routine laboratory and radiological tests. Patients with juvenile idiopathic arthritis (JIA) and systemic lupus erythematosus (SLE) were stratified using the Juvenile Arthritis Disease Activity Score (JADAS-27) and SLE Disease Activity Index (SLEDAI) respectively. Patients without bacterial focus with high disease activity were included in the flare-up group and the rest in the sepsis cohort. The diagnostic value of PCT was calculated using receiver operating characteristic (ROC) curve analysis. In the study (N=73), 41 (56.2%) patients were previously diagnosed with JIA and 28 (38.3%) had SLE. 38 patients had definite evidence of sepsis and 35 had disease flare-ups as per respective disease activity scores. There was a significant difference in PCT and CRP among the flare-up and sepsis groups. For detecting sepsis, the area under curve (0.959), sensitivity (94.7%), and specificity (74.3%) of PCT at a cut-off of 0.275 ng/mL were significantly better than those of CRP. PCT is a better diagnostic test than CRP or ESR during febrile episodes in differentiating flare-ups from infection and PCT >0.275 ng/mL indicates bacterial infection with good specificity and sensitivity in children with low disease activity.

Periprosthetic Joint Infection and Concomitant Sepsis: Unveiling Clinical Manifestations, Risk Factors, and Patient Outcomes.

This study investigated the epidemiology, risk factors, and outcomes of sepsis, a life-threatening complication, in the context of periprosthetic joint infections (PJIs) of the hip and knee. Sepsis was determined using the sepsis-1 criteria. The cohort with PJI and sepsis was compared to patients who had PJI without sepsis. Analyzed risk factors were patient characteristics, microbiological findings, and comorbidities. Outcome parameters were mortality, length of hospital stay, and intensive care unit stay. Among 108 PJIs (48 hips and 60 knees), 40.6% met the sepsis criteria. In hip PJI, the sepsis group had a higher Charlson Comorbidity Index (4.0 versus 1.0; P ≤ 0.001) with Staphylococcus aureus infections more common in septic cases (9 of 17 versus 6 of 31; P= 0.04). Renal (odds ratio (OR) 16.9; P ≤ 0.001) and cardiac (OR 12.5; P= 0.02) disease increased sepsis risk. Sepsis correlated with prolonged hospital stays (54 versus 24 days; P= 0.002) and increased mortality (23.5 versus 3.2%; P= 0.047). In knee PJI cases, septic patients had more Staphylococcus aureus PJI (14 of 28 versus 8 of 32; P= 0.04). Atrial fibrillation (OR 3.3; P= 0.04) and renal disease (OR 4.0; P= 0.02) were associated with sepsis. Sepsis cases had longer hospital stays (48 versus 29.5 days; P= 0.01) and higher intensive care unit admissions (67.9 versus 34.4%; P= 0.02). In-hospital mortality was 10-fold higher in the sepsis cohort (25.0 versus 3.3%; OR 10.3, P= 0.02). In a considerable number of patients, PJI can lead to a septic course associated with increased mortality. This underscores the need for close monitoring to prevent overlooking these patients' deteriorating clinical conditions. Timely interventions, akin to the "every hour counts" approach in sepsis management, might help reduce morbidity and mortality in these patients.

A multicentre prospective registry of one thousand sepsis patients admitted in Indian ICUs: (SEPSIS INDIA) study

BackgroundSepsis is a global health problem with high morbidity and mortality. Low- and middle-income countries have a higher incidence and poorer outcome with sepsis. Large epidemiological studies in sepsis using Sepsis-3 criteria, addressing the process of care and deriving predictors of mortality are scarce in India.MethodA multicentre, prospective sepsis registry was conducted using Sepsis 3 criteria of suspected or confirmed infection and SOFA score of 2 or more in 19 ICUs in India over a period of one year (August 2022–July 2023). All adult patients admitted to the Intensive Care Unit who fulfilled the Sepsis 3 criteria for sepsis and septic shock were included. Patient infected with Covid 19 were excluded. Patients demographics, severity, admission details, initial resuscitation, laboratory and microbiological data and clinical outcome were recorded. Performance improvement programs as recommended by the Surviving Sepsis guideline were noted from the participating centers. Patients were followed till discharge or death while in hospital.ResultsRegistry Data of 1172 patients with sepsis (including 500 patients with septic shock) were analysed. The average age of the study cohort was 65 years, and 61% were male. The average APACHE II and SOFA score was 21 and 6.7 respectively. The majority of patients had community-acquired infections, and lung infections were the most common source. Of all culture positive results, 65% were gram negative organism. Carbapenem-resistance was identified in 50% of the gram negative blood culture isolates. The predominant gram negative organisms were Klebsiella spp (25%), Escherechia coli (24%) and Acinetobacter Spp (11%). Tropical infections (Dengue, Malaria, Typhus) constituted minority (n = 32, 2.2%) of sepsis patients. The observed hospital mortality for the entire cohort (n = 1172) was 36.3%, for those without shock (n = 672) it was 25.6% and for those with shock (n = 500) it was 50.8%. The average length of ICU and hospital stay for the study cohort was 8.64 and 11.9 respectively. In multivariate analysis adequate source control, correct choice of empiric antibiotic and the use of intravenous thiamine were protective.ConclusionThe general demographics of the sepsis population in the Indian Sepsis Registry is comparable to Western population. The mortality of sepsis cohort was higher (36.3%) but septic shock mortality (50.8%) was comparable to Western reports. Gram negative infection was the predominant cause of sepsis with a high incidence of carbapenem resistance. Eschericia coli, Klebsiella Spp and Acinetobacter Spp were the predominant causative organism. Tropical infection constituted a minority of sepsis population with low hospital mortality. The SOFA score on admission was a comparatively better predictor of poor outcome. Sepsis secondary to nosocomial infections had the worst outcomes, while source control, correct empirical antibiotic selection, and intravenous thiamine were protective.CTRI Registration CTRI:2022/07/044516.

Three-year mortality of ICU survivors with sepsis, an infection or an inflammatory illness: an individually matched cohort study of ICU patients in the Netherlands from 2007 to 2019.

Sepsis is a frequent reason for ICU admission and a leading cause of death. Its incidence has been increasing over the past decades. While hospital mortality is decreasing, it is recognized that the sequelae of sepsis extend well beyond hospitalization and are associated with a high mortality rate that persists years after hospitalization. The aim of this study was to disentangle the relative contribution of sepsis (infection with multi-organ failure), of infection and of inflammation, as reasons for ICU admission to long-term survival. This was done as infection and inflammation are both cardinal features of sepsis. We assessed the 3-year mortality of ICU patients admitted with sepsis, with individually matched ICU patients with an infection but not sepsis, and with an inflammatory illness not caused by infection, discharged alive from hospital. A multicenter cohort study of adult ICU survivors admitted between January 1st 2007 and January 1st 2019, with sepsis, an infection or an inflammatory illness. Patients were classified within the first 24 h of ICU admission according to APACHE IV admission diagnoses. Dutch ICUs (n = 78) prospectively recorded demographic and clinical data of all admissions in the NICE registry. These data were linked to ahealth care insurance claims database to obtain 3-year mortality data. To better understand and distinct the sepsis cohort from the non-sepsis infection and inflammatory condition cohorts, we performed several sensitivity analyses with varying definitions of the infection and inflammatory illness cohort. Three-year mortality after discharge was 32.7% in the sepsis (N = 10,000), 33.6% in the infectious (N = 10,000), and 23.8% in the inflammatory illness cohort (N = 9997). Compared with sepsis patients, the adjusted HR for death within 3 years after hospital discharge was 1.00 (95% CI 0.95-1.05) for patients with an infection and 0.88 (95% CI 0.83-0.94) for patients with an inflammatory illness. Both sepsis and non-sepsis infection patients had a significantly increased hazard rate of death in the 3 years after hospital discharge compared with patients with an inflammatory illness. Among sepsis and infection patients, one third died in the next 3 years, approximately 10% more than patients with an inflammatory illness. The fact that we did not find a difference between patients with sepsis or an infection suggests that the necessity for an ICU admission with an infection increases the risk of long-term mortality. This result emphasizes the need for greater attention to the post-ICU management of sepsis, infection, and severe inflammatory illness survivors.

Clinical Subtype Trajectories in Sepsis Patients Admitted to the ICU: A Secondary Analysis of an Observational Study.

Sepsis is an evolving process and proposed subtypes may change over time. We hypothesized that previously established sepsis subtypes are dynamic, prognostic of outcome, and trajectories are associated with host response alterations. A secondary analysis of two observational critically ill sepsis cohorts: the Molecular diAgnosis and Risk stratification of Sepsis (MARS) and the Medical Information Mart for Intensive Care-IV (MIMIC-IV). ICUs in the Netherlands and United States between 2011-2014 and 2008-2019, respectively. Patient admission fulfilling the Sepsis-3 criteria upon ICU admission adjudicated to one of four previously identified subtypes, comprising 2,416 admissions in MARS and 10,745 in MIMIC-IV. Subtype stability and the changes per subtype on days 2, 4 and 7 of ICU admission were assessed. Next, the associated between change in clinical subtype and outcome and host response alterations. In MARS, upon ICU admission, 6% (n = 150) of the patient admissions were α-type, 3% (n = 70) β-type, 55% (n = 1317) γ-type, and 36% (n = 879) δ-type; in MIMIC-IV, this was α = 22% (n = 2398), β = 22% (n = 2365), γ = 31% (n = 3296), and δ = 25% (2686). Overall, prevalence of subtypes was stable over days 2, 4, and 7. However, 28-56% (MARS/MIMIC-IV) changed from α on ICU admission to any of the other subtypes on day 2, 33-71% from β, 57-32% from γ, and 50-48% from δ. On day 4, overall subtype persistence was 33-36%. γ or δ admissions remaining in, or transitioning to, subtype γ on days 2, 4, and 7 exhibited lower mortality rates compared with those remaining in, or transitioning to, subtype δ. Longitudinal host response biomarkers reflecting inflammation, coagulation, and endothelial dysfunction were most altered in the δ-δ group, followed by the γ-δ group, independent of the day or biomarker domain. In two large cohorts, subtype change to δ was associated with worse clinical outcome and more aberrant biomarkers reflecting inflammation, coagulation, and endothelial dysfunction. These findings underscore the importance of monitoring sepsis subtypes and their linked host responses for improved prognostication and personalized treatment strategies.

A positive correlation between serum lactate dehydrogenase level and in-hospital mortality in ICU sepsis patients: evidence from two large databases

BackgroundSepsis presents a significant healthcare challenge, characterized by high morbidity and mortality rates. There is a scarcity of relevant studies investigating the association between serum lactate dehydrogenase level and the prognosis of sepsis in patients from intensive care unit, with smaller sample sizes compared to other studies.MethodsA retrospective analysis was conducted utilizing data from the Second Affiliated Hospital of Guangzhou Medical University and the Medical Information Mart for Intensive Care IV (MIMIC-IV). The primary outcome was the in-hospital mortality. Multivariate Cox proportional hazards regression was adopted to assess the independent association. Receiver operator characteristic (ROC) curve was used to evaluate the predictive value.ResultsWe included a total of 2148 patients in the Guangzhou Sepsis Cohort (GZSC) database and 5830 patients in the MIMIC-IV database. In multivariate Cox proportional hazards regression, high levels of LDH are significantly associated with higher mortality (HR = 1.21, 95% CI 1.13–1.30, p < 0.001 in the GZSC database and HR = 1.19, 95% CI 1.13–1.25, p < 0.001 in the MIMIC-IV database). The ROC curves showed that the AUC of LDH was 0.663 in the GZSC database and 0.660 in the MIMIC-IV database.ConclusionsA lower lactate dehydrogenase level is associated with reduced in-hospital mortality among patients with sepsis, suggesting its potential as a valuable marker for predicting prognosis in this patient population.

NINJ1: A NOVEL SEPSIS SEVERITY AND MORTALITY BIOMARKER.

Background : Multiple cell death modalities are implicated in sepsis pathobiology. However, the clinical relevance of NINJ1, a key mediator of plasma membrane rupture during lytic cell death, in sepsis progression and outcomes has remained poorly explored. Methods: Circulating NINJ1 levels were measured in 116 septic intensive care unit (ICU) patients, 16 nonseptic ICU controls, and 16 healthy controls. Comparative analysis of serum NINJ1 across these groups was performed. Correlations between NINJ1 and clinical disease severity scores (Sequential Organ Failure Assessment [SOFA], Acute Physiology and Chronic Health Evaluation [APACHE II]) as well as laboratory parameters were examined in the sepsis cohort. Furthermore, we assessed the prognostic performance of NINJ1 for predicting 28-day mortality in septic patients using receiver operating characteristic (ROC) analyses. Results: Circulating NINJ1 levels were elevated in septic patients and positively correlated with sepsis severity scores. NINJ1 also showed positive correlations with liver injury markers (aspartate transaminase/alanine aminotransferase) and coagulation parameters (D-dimer, activated partial thromboplastin time, prothrombin time, thrombin time) in sepsis. Further analysis using the International Society on Thrombosis and Hemostasis overt disseminated intravascular coagulation scoring system revealed an association between NINJ1 and sepsis-induced coagulopathy. ROC analysis demonstrated that NINJ1 outperformed traditional inflammatory biomarkers procalcitonin and C-reactive protein in predicting 28-day sepsis mortality, although its prognostic accuracy was lower than SOFA and APACHE II scores. Combining NINJ1 with SOFA improved mortality prediction from an area under the curve of 0.6843 to 0.773. Conclusions: Circulating NINJ1 serves as a novel sepsis biomarker indicative of disease severity, coagulopathy and mortality risk, and its integration with SOFA and APACHE II scores substantially enhances prognostic risk stratification. These findings highlight the prospective clinical utility of NINJ1 for sepsis prognostication and monitoring, warranting further validation studies to facilitate implementation.

Association between albumin-bilirubin score and in-hospital mortality in patients with sepsis: Evidence from two large databases

Association between albumin-bilirubin score and in-hospital mortality in patients with sepsis: Evidence from two large databases