The Clinical Challenge of Sepsis Identification and Monitoring

IMPLICATIONS OF YWHAH GENE EXPRESSION IN THE EARLY DETECTION OF SEPSIS

ABSTRACT

Sepsis, a complex and multifaceted condition, is a common occurrence with serious implications for critically ill patients in the intensive care unit (ICU). The YWHAH gene encodes the 14-3-3n protein, a member of the 14-3-3 protein family. While existing research primarily focuses on the role of 14-3-3n in conditions such as schizophrenia and various cancers, our study revealed that the expression of the YWHAH gene remained relatively stable in both infected individuals and healthy controls. Through Venn plot analysis following weighted gene correlation network analysis, we observed a potential association between elevated YWHAH expression and the transition from infection to sepsis. In a comprehensive analysis of public single-cell transcriptome databases, the expression of YWHAH was found to be distinctive in cases of sepsis and infection. These findings were corroborated through an in vitro analysis utilizing real-time polymerase chain reaction. This study represents the initial identification of variations in YWHAH gene expression between patients with infection and sepsis, potentially offering insights for the development of early detection and treatment strategies for sepsis.

Comparison of Differences in Cohort (Forward) and Case Control (Backward) Methodologic Approaches for Validation of the Hypotension Prediction Index

BACKGROUND

The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique.

METHODS

A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance.

RESULTS

The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77.

CONCLUSIONS

Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method.

EDITOR’S PERSPECTIVE

Sepsis Alert Systems, Mortality, and Adherence in Emergency Departments: A Systematic Review and Meta-Analysis

Importance

Early detection and management of sepsis are crucial for patient survival. Emergency departments (EDs) play a key role in sepsis management but face challenges in timely response due to high patient volumes. Sepsis alert systems are proposed to expedite diagnosis and treatment initiation per the Surviving Sepsis Campaign guidelines.

Objective

To review and analyze the association of sepsis alert systems in EDs with patient outcomes.

Data Sources

A thorough search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from January 1, 2004, to November 19, 2023.

Study Selection

Studies that evaluated sepsis alert systems specifically designed for adult ED patients were evaluated. Inclusion criteria focused on peer-reviewed, full-text articles in English that reported on mortality, ICU admissions, hospital stay duration, and sepsis management adherence. Exclusion criteria included studies that lacked a control group or quantitative reports.

Data Extraction and Synthesis

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Two independent reviewers conducted the data extraction using a standardized form. Any disagreements were resolved through discussion. The data were synthesized using a random-effects model due to the expected heterogeneity among the included studies.

Main Outcomes and Measures

Key outcomes included mortality, intensive care unit admissions, hospital stay duration, and adherence to the sepsis bundle.

Results

Of 3281 initially identified studies, 22 (0.67%) met inclusion criteria, encompassing 19 580 patients. Sepsis alert systems were associated with reduced mortality risk (risk ratio [RR], 0.81; 95% CI, 0.71 to 0.91) and length of hospital stay (standardized mean difference [SMD], -0.15; 95% CI, -0.20 to -0.11). These systems were also associated with better adherence to sepsis bundle elements, notably in terms of shorter time to fluid administration (SMD, -0.42; 95% CI, -0.52 to -0.32), blood culture (SMD, -0.31; 95% CI, -0.40 to -0.21), antibiotic administration (SMD, -0.34; 95% CI, -0.39 to -0.29), and lactate measurement (SMD, -0.15; 95% CI, -0.22 to -0.08). Electronic alerts were particularly associated with reduced mortality (RR, 0.78; 95% CI, 0.67 to 0.92) and adherence with blood culture guidelines (RR, 1.14; 95% CI, 1.03 to 1.27).

Conclusions and Relevance

These findings suggest that sepsis alert systems in EDs were associated with better patient outcomes along with better adherence to sepsis management protocols. These systems hold promise for enhancing ED responses to sepsis, potentially leading to better patient outcomes.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score

OBJECTIVE

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort.

DESIGN

A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study.

SETTING

Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort).

PATIENTS

We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality.

CONCLUSIONS

An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Peripheral PD-1+NK cells could predict the 28-day mortality in sepsis patients

Background

Unbalanced inflammatory response is a critical feature of sepsis, a life-threatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model.

Methods

Patients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis.

Results

The study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38+CD8+T cells, PD-1+NK cells, HLA-DR+CD8+T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1+ NK cells, while cluster 2 featured higher proportions of naïve CD4+T cells. Furthermore, the level of PD-1+NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P<0.01). Moreover, the levels of PD1+ NK cells combined with SOFA score showed good performance in predicting the 28-day mortality in sepsis (AUC=0.91,95%CI 0.82-0.99), which is superior to PD1+ NK cells only(AUC=0.69, sensitivity 0.74, specificity 0.64, cut-off value of 11.25%). In the multivariate Cox regression, high expression of PD1+ NK cells proportion was related to 28-day mortality (aHR=1.34, 95%CI 1.19 to 1.50; P<0.001).

Conclusion

The study provides novel insights into the association between PD1+NK cell profiles and prognosis of sepsis. Peripheral immunophenotyping could potentially stratify the septic patients and identify those with a high risk of 28-day mortality.

Clinical usefulness of NT-proBNP as a prognostic factor for septic shock patients presenting to the emergency department

Plasma N-terminal prohormone of brain natriuretic peptide (NT-proBNP) level is primarily used as a biomarker for left ventricular (LV) dysfunction. It is influenced by various conditions, such as myocardial strain and situations affecting the clearance of NT-proBNP, including sepsis and shock. In this study, we investigated the appropriateness of NT-proBNP as a prognostic factor for septic shock. Patients with septic shock who visited the emergency department of the Ewha Womans' University Mokdong Hospital between January 1, 2018, and December 31, 2020, were classified into the survival group (those who survived in the hospital and were discharged) and the death group (those who died in the hospital). The effectiveness of NT-proBNP, lactate, and blood urea nitrogen as predictive factors of in-hospital mortality was evaluated using the area under the receiver operating characteristic (AUROC) curve. The AUROC curve was 0.678 and 0.648 for lactate and NT-proBNP, respectively, with lactate showing the highest value. However, there was no significant difference between lactate and NT-proBNP levels in the comparison of their AUROC curve (p = 0.6278). NT-proBNP could be a useful predictor of in-hospital mortality in patients with septic shock who present to the emergency department.

Cellular host response sepsis test for risk stratification of patients in the emergency department: A pooled analysis

OBJECTIVES

Sepsis is one of the most common, costly, and misdiagnosed conditions in U.S. emergency departments (EDs). ED providers often treat on nonspecific signs, subjective suspicion, or presumption of infection, resulting in over- and undertreatment. An increased understanding of host response has opened a new direction for sepsis diagnostics. The IntelliSep test is a U.S. Food and Drug Administration-cleared cellular host response diagnostic that could help distinguish sepsis in ED settings. Our objective was to evaluate the potential of the cellular host response test to expedite appropriate care for patients who present with signs of infection.

METHODS

We performed a pooled analysis of five adult (≥18 years) cohorts enrolled at seven geographically diverse U.S. sites in separate studies. Structured blinded adjudication was used to classify presence or absence of sepsis, and only patients with high confidence in the adjudicated label were included (n = 1002), defined as patients for whom there was consensus in the determination of sepsis per the Sepsis-3 and severe sepsis per the Sepsis-2 definitions between both the independent adjudication panel and the site-level physician.

RESULTS

Among patients with signs or suspicion of infection, the test achieved similar or better performance compared to other indicators in identifying patients at high risk for sepsis (specificity > 83%) and significantly superior performance in identifying those at low risk (sensitivity > 92%; 0% sepsis-associated mortality). The test also stratified severity of illness, as shown by 30-day in-hospital mortality (p < 0.001), hospital length of stay (p < 0.01), and use of hospital resources (p < 0.001).

CONCLUSIONS

Our data suggest that the cellular host response test provides clinically actionable results for patients at both high and low risk for sepsis and provides a rapid, objective means for risk stratification of patients with signs of infection. If integrated into standard of care, the test may help improve outcomes and reduce unnecessary antibiotic use.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis

BACKGROUND

Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions.

OBJECTIVE

The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis.

METHODS

We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords.

RESULTS

A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023).

CONCLUSIONS

This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses

Whole blood models are rapid and versatile for determining immune responses to inflammatory and infectious stimuli, but they have not been used for bacterial discrimination. Staphylococcus aureus, S. epidermidis and Escherichia coli are the most common causes of invasive disease, and rapid testing strategies utilising host responses remain elusive. Currently, immune responses can only discriminate between bacterial 'domains' (fungi, bacteria and viruses), and very few studies can use immune responses to discriminate bacteria at the species and strain level. Here, whole blood was used to investigate the relationship between host responses and bacterial strains. Results confirmed unique temporal profiles for the 10 parameters studied: IL-6, MIP-1α, MIP-3α, IL-10, resistin, phagocytosis, S100A8, S100A8/A9, C5a and TF3. Pairwise analysis confirmed that IL-6, resistin, phagocytosis, C5a and S100A8/A9 could be used in a discrimination scheme to identify to the strain level. Linear discriminant analysis (LDA) confirmed that (i) IL-6, MIP-3α and TF3 could predict genera with 95% accuracy; (ii) IL-6, phagocytosis, resistin and TF3 could predict species at 90% accuracy and (iii) phagocytosis, S100A8 and IL-10 predicted strain at 40% accuracy. These data are important because they confirm the proof of concept that host biomarker panels could be used to identify bacterial pathogens.

Association between preoperative systemic immune inflammation index and postoperative sepsis in patients with intestinal obstruction: A retrospective observational cohort study

BACKGROUND

Sepsis is a severe complication that results in increased morbidity and mortality after intestinal obstruction surgery. This study examined the role of preoperative systemic immune inflammation index (SII) for postoperative sepsis in intestinal obstruction patients.

METHODS

Data on patients who underwent intestinal obstruction surgery were collected. SII was determined and separated into two groups (≤1792.19 and >1792.19) according to the optimal cut-off value of SII for postoperative sepsis. The odds ratio (OR) is calculated for the correlation between SII and postoperative sepsis. Additional analyses were used to estimate the robustness of SII.

RESULTS

A total of 371 intestinal obstruction patients undergoing surgery were included in the final cohort, and 60 (16.17%) patients developed postoperative sepsis. Patients with an SII ＞1792.19 had a significantly higher risk for developing postoperative sepsis after multivariable adjustment [adjusted odds ratio = 2.12, 95% confidence interval: [1.02-4.40]]. The analysis of interaction showed no correlation between the preoperative SII and postoperative sepsis regarding age, hypertension, American Society of Anesthesiologists classification, blood loss, albumin, hemoglobin, creatinine, and leukocyte (all interactions p > .05). In subgroup analysis, all statistically significant subgroups showed that SII was a risk factor for postoperative sepsis (all p < .05). The analyses of subgroups and interactions revealed that the interaction effect of a preoperative SII ＞1792.19 and postoperative sepsis remained significant. A sensitivity analysis confirmed the robustness of the results.

CONCLUSIONS

A preoperative SII ＞ 1792.19 was a risk factor for postoperative sepsis in patients undergoing intestinal obstruction surgery.

Nanobiosensors for procalcitonin (PCT) analysis

BACKGROUND

Procalcitonin (PCT) is a critical biomarker that is released in response to bacterial infections and can be used to differentiate the pathogenesis of the infectious process.

OBJECTIVE

In this article, we provide an overview of recent advances in PCT biosensors, highlighting different approaches for biosensor construction, different immobilization methods, advantages and roles of different matrices used, analytical performance, and PCT biosensor construction. Also, we will explain PCT biosensors sensible limits of detection (LOD), linearity, and other analytical characteristics. Future prospects for the development of better PCT biosensor systems are also discussed.

METHODS

Traditional methods such as capillary electrophoresis, high-performance liquid chromatography, and mass spectrometry are effective in analyzing PCT in the medical field, but they are complicated, time-consuming sample preparation, and require expensive equipment and skilled personnel.

RESULTS

In the past decades, PCT biosensors have emerged as simple, fast, and sensitive tools for PCT analysis in various fields, especially medical fields.

CONCLUSION

These biosensors have the potential to accompany or replace traditional analytical methods by simplifying or reducing sample preparation and making field testing easier and faster, while significantly reducing the cost per analysis.

Variability in Provider Assessment of Sepsis and Potential of Host Response Technology to Address this Dilemma-Results of an Online Delphi Study

Potentially septic patients have a huge clinical and economic impact on hospitals and often present to the emergency department (ED) with undifferentiated symptoms. The triage of these patients is complex and has historically relied heavily upon provider judgment. This study aims to evaluate the consistency of provider judgment and the potential of a new host response sepsis test to aid in the triage process. A modified Delphi study involving 26 participants from multiple specialties was conducted to evaluate provider agreement about sepsis risk and to test proposed actions based on the results of a sepsis test. The participants considered case vignettes of potentially septic patients designed to represent diagnostic dilemmas. Provider assessment of sepsis risk in these cases ranged from 10% to 90% and agreement was poor. Agreement about clinical actions to take in response to testing improved when participants considered their own hypothetical borderline cases. New host response testing for sepsis may have the potential to improve sepsis diagnosis and care and should be applied in a protocolized fashion to ensure consistency of results.

The Role and Initiatives Led by the Sepsis Coordinator to Improve Sepsis Bundle Compliance and Care Across the Continuum

A dedicated sepsis coordinator role at Penn Medicine Lancaster General Hospital led initiatives to improve sepsis core measure compliance by 40% during the course of 4 years with submission of all sepsis cases. Chart abstraction and analysis of noncompliant cases identified areas for improvement: early recognition education, order set revisions, documentation support, and the implementation of a nurse-driven 24/7 sepsis monitoring process. The cooperative work with Penn Medicine affiliates, sharing best practices, improves overall sepsis bundle compliance and transitions of care. Ongoing achievements acknowledge the value of building relationships and leading improvements through the collaborative efforts of interprofessional teams.

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data

BACKGROUND

Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning.

METHODS

Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment.

RESULTS

DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346).

CONCLUSIONS

The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.

Healthcare resilience: a meta-narrative systematic review and synthesis of reviews

OBJECTIVES

The COVID-19 pandemic has tested global healthcare resilience. Many countries previously considered 'resilient' have performed poorly. Available organisational and system frameworks tend to be context-dependent and focus heavily on physical capacities. This study aims to explore and synthesise evidence about healthcare resilience and present a unified framework for future resilience-building.

DESIGN

Systematic review and synthesis of reviews using a meta-narrative approach.

SETTING

Healthcare organisations and systems.

PRIMARY AND SECONDARY OUTCOME MEASURES

Definitions, concepts and measures of healthcare resilience. We used thematic analysis across included reviews to summarise evidence on healthcare resilience.

RESULTS

The main paradigms within healthcare resilience include global health, disaster risk reduction, emergency management, patient safety and public health. Definitions of healthcare resilience recognise various hierarchical levels: individual (micro), facility or organisation (meso), health system (macro) and planetary or international (meta). There has been a shift from a focus on mainly disasters and crises, to an 'all-hazards' approach to resilience. Attempts to measure resilience have met with limited success. We analysed key concepts to build a framework for healthcare resilience containing pre-event, intra-event, post-event and trans-event domains. Alongside, we synthesise a definition which dovetails with our framework.

CONCLUSION

Resilience increasingly takes an all-hazards approach and a process-oriented perspective. There is increasing recognition of the relational aspects of resilience. Few frameworks incorporate these, and they are difficult to capture within measurement systems. We need to understand how resilience works across hierarchical levels, and how competing priorities may affect overall resilience. Understanding these will underpin interdisciplinary, cross-sectoral and multi-level approaches to healthcare resilience for the future.

PROSPERO REGISTRATION NUMBER

CRD42022314729.

Identification and validation of key biomarkers based on RNA methylation genes in sepsis

Background

RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis.

Methods

Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis.

Results

Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively.

Conclusions

Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.

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.

Allicin protects against LPS-induced cardiomyocyte injury by activating Nrf2-HO-1 and inhibiting NLRP3 pathways

BACKGROUND

Allicin is a bioactive compound with potent antioxidative activity and plays a protective effect in myocardial damage and fibrosis. The role and mechanism of Allicin in septic cardiomyopathy are unclear. In this study, we investigated the effects and underlying mechanisms of Allicin on lipopolysaccharide (LPS) induced injury in H9c2 cardiomyocytes.

METHODS

H9c2 cardiomyocyte cells were pretreated with Allicin (0, 25, 50, and 100 µM) for 2 h, followed by incubation with LPS (10 µg/mL) for 24 h at 37 °C. Cell viability (cell counting kit-8 [CCK-8]), apoptosis (TUNEL staining), oxidative stress (malondialdehyde [MDA] and superoxide dismutase [SOD]), and cytokines release (Interleukin beta [IL-β], Interleukin 6 [IL-6], and tumor necrosis factor-alpha [TNF-α]) were determined. The mRNA and protein expression of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), and NLR family pyrin domain containing 3 (NLRP3) signaling pathway molecules were quantified by real-time quantitative PCR (RT-qPCR) and western blot, respectively.

RESULTS

Allicin had no effect on H9c2 cell viability but attenuated LPS-induced injury, with increased cell viability, reduction in inflammatory cytokines release, apoptosis, reduced MDA, and increased SOD (P < 0.05). Additionally, Allicin increased Nrf2 and cellular HO-1 expressions in LPS-treated H9c2 cells. Moreover, Allicin modulated the NLRP3 inflammasome, increased the cleaved caspase-1 (p10) protein, and attenuated the LPS-induced increase in NLRP3, pro-IL-1β, and IL-1β proteins. Silencing of Nrf2 by siRNA (siNrf2) significantly attenuated Allicin-induced increase in cell viability and HO-1 and decrease in NLRP3 protein in LPS-stimulated H9c2 cells.

CONCLUSIONS

Allicin protects cardiomyocytes against LPS‑induced injury through activation of Nrf2/HO-1 and inhibition of NLRP3 signaling pathways.

Severe Acute Bronchial Asthma with Sepsis: Determining the Status of Biomarkers in the Diagnosis of the Disease

Bronchial asthma is a widely prevalent illness that substantially impacts an individual's health standard worldwide and has a significant financial impact on society. Global guidelines for managing asthma do not recommend the routine use of antimicrobial agents because most episodes of the condition are linked to viral respiratory tract infections (RTI), and bacterial infection appears to have an insignificant impact. However, antibiotics are recommended when there is a high-grade fever, a consolidation on the chest radiograph, and purulent sputum that contains polymorphs rather than eosinophils. Managing acute bronchial asthma with sepsis, specifically the choice of whether or not to initiate antimicrobial treatment, remains difficult since there are currently no practical clinical or radiological markers that allow for a simple distinction between viral and bacterial infections. Researchers found that serum procalcitonin (PCT) values can efficiently and safely minimize antibiotic usage in individuals with severe acute asthma. Again, the clinical manifestations of acute asthma and bacterial RTI are similar, as are frequently used test values, like C-reactive protein (CRP) and white blood cell (WBC) count, making it harder for doctors to differentiate between viral and bacterial infections in asthma patients. The role and scope of each biomarker have not been precisely defined yet, although they have all been established to aid healthcare professionals in their diagnostics and treatment strategies.

Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study

BACKGROUND

Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals.

METHODS

We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX^®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding.

RESULTS

Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively).

CONCLUSION

Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Chemo Markers as Biomarkers in Septic Shock: A Comprehensive Review of Their Utility and Clinical Applications

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, often leading to septic shock. Early diagnosis and prompt intervention are crucial for improving patient outcomes. Chemo markers, which are measurable biological substances associated with the pathophysiology of septic shock, have emerged as potential biomarkers for the identification, risk stratification, and management of this condition. This comprehensive review aims to thoroughly evaluate the utility and clinical applications of chemo markers in septic shock. The review begins by discussing the criteria for ideal chemo markers, including specificity, sensitivity, dynamic range, stability, non-invasiveness, and prognostic value. These characteristics ensure accurate diagnosis, early detection, effective monitoring, and prediction of clinical outcomes. Furthermore, the review explores the role of chemo markers in monitoring treatment response and disease progression, highlighting their ability to serve as objective indicators for assessing the effectiveness of interventions and making timely adjustments in management strategies. Moreover, the prognostic value of chemo markers in predicting outcomes is discussed, emphasizing their association with mortality, hospital stays, and the development of complications. Integration of chemo markers into prognostic models or scoring systems enhances risk stratification and informs therapeutic decisions. The review also delves into recent advances in chemo marker research and technology, emphasizing the potential for discovering novel chemo markers with enhanced diagnostic and prognostic capabilities. It highlights the use of high-throughput proteomics, genomics, and transcriptomics in identifying specific molecular signatures associated with septic shock. This contributes to a deeper understanding of the complex immune and inflammatory responses involved. In conclusion, chemo markers have emerged as valuable biomarkers in septic shock, offering potential utility in diagnosis, risk stratification, treatment monitoring, and prediction of outcomes. Continued research, validation, and integration into clinical practice are necessary to fully realize their potential in improving patient care and outcomes in septic shock.

Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study

Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ^® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Point-of-care diagnostics for sepsis using clinical biomarkers and microfluidic technology

Sepsis is a life-threatening immune response which is caused by a wide variety of sources and is a leading cause of mortality globally. Rapid diagnosis and appropriate antibiotic treatment are critical for successful patient outcomes; however, current molecular diagnostic techniques are time-consuming, costly and require trained personnel. Additionally, there is a lack of rapid point-of-care (POC) devices available for sepsis detection despite the urgent requirements in emergency departments and low-resource areas. Recent advances have been made toward developing a POC test for early sepsis detection that will be more rapid and accurate compared to conventional techniques. Within this context, this review discusses the use of current and novel biomarkers for early sepsis diagnosis using microfluidics devices for POC testing.

Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review

PURPOSE OF REVIEW

Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking.

RECENT FINDINGS

Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome.

SUMMARY

There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.

Combining C-reactive protein and quick sequential organ failure assessment (qSOFA) to improve prognostic accuracy for sepsis and mortality in adult inpatients: A systematic review

Background and Aims

Infections are common in hospitals, and if mismanaged can develop into sepsis, a leading cause of death and disability worldwide. This study aimed to examine whether combining C-reactive protein (CRP) with the quick sequential organ failure assessment (qSOFA) improves its accuracy for predicting mortality and sepsis in adult inpatients.

Methods

PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Science Direct, CINAHL, Open Grey, Grey Literature Report, and the Clinical Trials registry were searched using CRP and qSOFA search terms. Title, abstract, and full-text screening were performed by two independent reviewers using pre-determined eligibility criteria, followed by data extraction and a risk of bias assessment using the Quality Assessment tool for Diagnostic Accuracy Studies 2 (QUADAS-2). Disagreements were settled through discussion and consultation with a third reviewer.

Results

Four retrospective studies with a total of 2070 patients were included in this review. Adding CRP to qSOFA improved the Area Under the Receiver Operating Characteristic Curve up to 9.7% for predicting mortality and by 14.9% for identifying sepsis. The sensitivity and specificity of the combined score for mortality prediction were available in two studies. CRP improved the sensitivity of qSOFA by 43% and 71% while only decreasing the specificity by 12% and 7%, respectively. A meta-analysis was not performed due to study heterogeneity.

Conclusion

This comprehensive review provided initial evidence that combining CRP with qSOFA may improve the accuracy of qSOFA alone in identifying sepsis or patients at risk of dying in hospital. The combined tool demonstrated the potential to improve patient outcomes, with implications for low-resource settings given its simplicity and low-cost.

Role of Neutrophil Gelatinase-associated Lipocalin (NGAL) and Other Clinical Parameters as Predictors of Bacterial Sepsis in Patients Presenting to the Emergency Department with Fever

Background

Bacterial sepsis is associated with significant morbidity and mortality. However, to date, there is no single test that predicts sepsis with reproducible results. We proposed that using a combination of clinical and laboratory parameters and a novel biomarker, plasma neutrophil gelatinase-associated lipocalin (NGAL) may aid in early diagnosis.

Method

A prospective cohort study was conducted at a tertiary care center in South India (June 2017 to April 2018) on patients with acute febrile episodes fulfilling the Systemic Inflammatory Response Syndrome (SIRS) criteria. Plasma NGAL and standard clinical and laboratory parameters were collected at the admission. Bacterial sepsis was diagnosed based on blood culture positivity or clinical diagnosis. Clinically relevant plasma NGAL cut-off values were identified using the receive operating characteristic (ROC) curve. Clinically relevant clinical parameters along with plasma NGAL's risk ratios estimated from the multivariable Poisson regression model were rounded and used as weights to create a new scoring tool.

Results

Of 100 patients enrolled, 37 had bacterial sepsis. The optimal plasma NGAL cut-off value to predict sepsis was 570 ng/mL [area under the curve (AUC): 0.69]. The NGAL sepsis screening tool consists of the following clinical parameter: diabetes mellitus, the presence of rigors, quick sequential organ failure assessment (qSOFA) >2, a clear focus of infection, and the plasma NGAL >570 ng/mL. A score of <3 ruled out bacterial sepsis and a score >7 were highly suggestive of bacterial sepsis with an interval likelihood ratio (LR) of 7.77.

Conclusion

The NGAL sepsis screening tool with a score >7 can be used in the emergency department (ED) to identify bacterial sepsis.

How to cite this article

Paul A, Newbigging NS, Lenin A, Gowri M, Varghese JS, Nell AJ, et al. Role of Neutrophil Gelatinase-associated Lipocalin (NGAL) and Other Clinical Parameters as Predictors of Bacterial Sepsis in Patients Presenting to the Emergency Department with Fever. Indian J Crit Care Med 2023;27(3):176-182.

Nurses' knowledge and confidence in recognizing and managing patients with sepsis: A multi-site cross-sectional study

AIMS

(1) To examine registered nurses' knowledge and confidence in recognizing and managing to patients with sepsis and (2) identify nurse and workplace factors that influence their knowledge on sepsis.

DESIGN

A multi-site, cross-sectional survey.

METHODS

An online survey was developed and content validated. Data was collected from registered nurses working in the inpatient wards and emergency departments of three hospitals of a single healthcare cluster in Singapore during August 2021. Statistical analyses of closed-ended responses and content analysis of open-ended responses were undertaken.

RESULTS

A total of 709 nurses completed the survey. Nurses possessed moderate levels of knowledge about sepsis (mean score = 10.56/15; SD = 2.01) and confidence in recognizing and responding to patients with sepsis (mean score = 18.46/25; SD = 2.79). However, only 369 (52.0%) could correctly define sepsis. Nurses' job grade, nursing education level and clinical work area were significant predictors of nurses' sepsis knowledge. Specifically, nurses with higher job grade, higher nursing education level or those working in acute care areas (i.e. emergency department, high dependency units or intensive care units) were more likely to obtain higher total sepsis knowledge scores. A weak positive correlation was observed between sepsis knowledge test scores and self-confidence (r = .184). Open comments revealed that participants desired for more sepsis education and training opportunities and the implementation of sepsis screening tool and sepsis care protocol.

CONCLUSION

A stronger foundation in sepsis education and training programs and the implementation of sepsis screening tools and care bundles are needed to enhance nurses' knowledge and confidence in recognizing and managing patients with sepsis.

IMPACT

The findings of this study are beneficial to administrators, educators and researchers in designing interventions to support nurses in their role in recognizing and responding to sepsis.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Assessment of Opioid-Induced Immunomodulation in Experimental and Clinical Sepsis

Opioids remain a standard supportive therapy in patients admitted to the ICU with sepsis. However, as preclinical models indicate an association between opioid exposure and immunosuppression, the use of this class of drugs warrants investigation. The objective of this study was to investigate whether opioid exposure causes immunosuppression in patients with sepsis, and to use a murine sepsis model to determine the effects of opioid exposure on secondary infection.

HYPOTHESIS

We hypothesized opioid exposure would be associated with immunosuppression in patients with sepsis and secondary infection in a murine sepsis model.

METHODS AND MODELS

This was a two-phase preclinical and clinical study. The clinical phase included a subgroup of patients with sepsis from an existing randomized controlled trial while the preclinical phase used a murine model of sepsis with C57BL/6 mice. In the clinical phase, a post hoc analysis was performed in subjects receiving fentanyl versus no opioid receipt. In the preclinical phase, a murine cecal slurry-induced sepsis model followed by secondary infection was used. Mice were randomized to fentanyl versus no fentanyl concomitantly.

RESULTS

In clinical sepsis, a significant decrease in interleukin-23 (IL-23) level in patients with fentanyl exposure was observed and lower IL-23 was associated with mortality (p < 0.001). Other measured cytokines showed no significant differences. Concomitant fentanyl exposure during murine sepsis was associated with a significantly higher bacterial burden (p < 0.001) after secondary infection; however, immune cell counts and plasma cytokine levels were largely unaffected by fentanyl.

INTERPRETATION AND CONCLUSIONS

Minimal alterations in cytokines were seen with opioid exposure during clinical sepsis. In a preclinical model, opioid exposure during sepsis was associated with ineffective bacterial clearance upon secondary infection. Further studies are warranted to evaluate the immunomodulatory role of opioids and their implications, especially in the post-sepsis period.

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these "silver" labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Accuracy of International Classification of Diseases, 10th Revision Codes for Identifying Sepsis: A Systematic Review and Meta-Analysis

Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of International Classification of Diseases, 10th revision (ICD-10), codes for identifying sepsis in adult and pediatric patients.

DATA SOURCES

We searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos, and McMaster Superfilters from inception to September 7, 2021.

STUDY SELECTION

We included studies that validated the accuracy of sepsis ICD-10 codes against any reference standard.

DATA EXTRACTION

Three authors, working in duplicate, independently extracted data. We conducted meta-analysis using a random effects model to pool sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated individual study risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool and assessed certainty in pooled diagnostic effect measures using the Grading of Recommendations Assessment, Development, and Evaluation framework.

DATA SYNTHESIS

Thirteen eligible studies were included in the qualitative synthesis and the meta-analysis. Eleven studies used manual chart review as the reference standard, and four studies used registry databases. Only one study evaluated pediatric patients exclusively. Compared with the reference standard of detailed chart review and/or registry databases, the pooled sensitivity for sepsis ICD-10 codes was 35% (95% CI, 22-48, low certainty), whereas the pooled specificity was 98% (95% CI: 98-99, low certainty). The PPV for ICD-10 codes ranged from 9.8% to 100% (median, 72.0%; interquartile range [IQR], 50.0-84.7%). NPV ranged from 54.7% to 99.1% (median, 95.9%; interquartile range, 85.5-98.3%).

CONCLUSIONS

Sepsis is undercoded in administrative databases. Future research is needed to explore if greater consistency in ICD-10 code definitions and enhanced quality measures for ICD-10 coders can improve the coding accuracy of sepsis in large databases.

To Determine the Genotyping of Fc-gamma Receptor FCGR2A Polymorphism as Genetic Susceptibility to Neonatal Sepsis: A Study from a Tertiary Center of North India

Background

Neonatal sepsis term is an infection of newborns <28 days of age. It is a common cause of death in developing countries. The receptor-gamma receptor FCGR2A has been shown to be associated with neonatal sepsis. It is an activating receptor found in many cell types such as monocytes, neutrophils, macrophages, platelets, and others. The receptor has a polymorphism (single-nucleotide polymorphism rs1801274) in its gene (FCGR2A) that encodes either a histidine (H) or arginine (R) at amino acid position 131. There are many studies showing the impact of these FCGR2A polymorphisms on sepsis. Our study aims to determine the prevalence of Fc-gamma receptor FCGR2A (rs1801274) polymorphism in neonatal sepsis and control in Eastern UP populations.

Patients and Methods

We conducted a cross-sectional descriptive study of 590 patients (310 healthy individuals and 280 sepsis patients) to determine polymorphisms in the CD32A coding region in neonates. All individuals were genotyped for a variant at position 131 of the FcγRIIA gene.

Discussion

In our study, the prevalence of FcγRIIa polymorphism is more in neonates with sepsis than in noninfected neonates. It was observed that the heterozygous allele (AG) were significantly increased in septic neonates when compared to the normal.

Conclusion

Our data indicate that FcγRIIA genotyping can be used as a marker of genetic susceptibility to sepsis.

Does lactate enhance the prognostic accuracy of the quick Sequential Organ Failure Assessment for adult patients with sepsis? A systematic review

OBJECTIVES

To investigate whether adding lactate to the quick Sequential (sepsis-related) Organ Failure Assessment (qSOFA) improves the prediction of mortality in adult hospital patients, compared with qSOFA alone.

DESIGN

Systematic review in accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines.

DATA SOURCES

Embase, Medline, PubMed, SCOPUS, Web of Science, CINAHL and Open Grey databases were searched in November 2020.

ELIGIBILITY CRITERIA

Original research studies published after 2016 comparing qSOFA in combination with lactate (LqSOFA) with qSOFA alone in adult patients with sepsis in hospital. The language was restricted to English.

DATA EXTRACTION AND SYNTHESIS

Title and abstract screening, full-text screening, data extraction and quality assessment (using Quality Assessment of Diagnostic Accuracy Studies-2) were conducted independently by two reviewers. Extracted data were collected into tables and diagnostic test accuracy was compared between the two tests.

RESULTS

We identified 1621 studies, of which 11 met our inclusion criteria. Overall, there was a low risk of bias across all studies. The area under the receiver operating characteristic (AUROC) curve for qSOFA was improved by the addition of lactate in 9 of the 10 studies reporting it. Sensitivity was increased in three of seven studies that reported it. Specificity was increased in four of seven studies that reported it. Of the six studies set exclusively within the emergency department, five published AUROCs, all of which reported an increase following the addition of lactate. Sensitivity and specificity results varied throughout the included studies. Due to insufficient data and heterogeneity of studies, a meta-analysis was not performed.

CONCLUSIONS

LqSOFA is an effective tool for identifying mortality risk both in adult inpatients with sepsis and those in the emergency department. LqSOFA increases AUROC over qSOFA alone, particularly within the emergency department. However, further original research is required to provide a stronger base of evidence in lactate measurement timing, as well as prospective trials to strengthen evidence and reduce bias.

PROSPERO REGISTRATION NUMBER

CRD42020207648.

Antithrombin as Therapeutic Intervention against Sepsis-Induced Coagulopathy and Disseminated Intravascular Coagulation: Lessons Learned from COVID-19-Associated Coagulopathy

Recent research has contributed significantly to our understanding of the pathogenesis of acute disseminated intravascular coagulation. COVID-19 can be considered as a new underlying condition of disseminated intravascular coagulation. In this narrative review, current evidence is presented regarding biomarker differences between sepsis-induced and COVID-19-associated coagulopathies, supporting the importance of acquired antithrombin deficiency in the early differential diagnosis of septic coagulopathy and its potential impact on treatment with endogenous anticoagulants. Establishing new scoring systems for septic coagulopathy in combination with endogenous anticoagulant biomarker activities may allow for the identification of those in the heterogeneous population of sepsis patients who are more likely to benefit from targeted specific treatment interventions.

Outer Membrane Vesicles as Molecular Biomarkers for Gram-negative Sepsis: Taking Advantage of Nature's Perfect Packages

Sepsis is an often life-threatening response to infection, occurring when host pro-inflammatory immune responses become abnormally elevated and dysregulated. To diagnose sepsis, the patient must have a confirmed or predicted infection, as well as other symptoms associated with the pathophysiology of sepsis. However, a recent study found that a specific causal organism could not be determined in the majority (70.1%) of sepsis cases, likely due to aggressive antibiotics or localized infections. The timing of a patient's sepsis diagnosis is often predictive of their clinical outcome, underlining the need for a more definitive molecular diagnostic test. Here, we outline the advantages and challenges to using bacterial outer membrane vesicles (OMVs), nanoscale spherical buds derived from the outer membrane of Gram-negative bacteria, as a diagnostic biomarker for Gram-negative sepsis. Advantages include OMV abundance, their robustness in the presence of antibiotics, and their unique features derived from their parent cell that could allow for differentiation between bacterial species. Challenges include the rigorous purification methods required to isolate OMVs from complex biofluids and the additional need to separate OMVs from similarly-sized extracellular vesicles, which can share physical properties with OMVs.

Pancreatic stone protein point-of-care testing can reduce healthcare expenditure in sepsis

BACKGROUND

Sepsis is a life-threatening organ dysfunction in response to infection. Early recognition and rapid treatment are critical to patient outcomes and cost savings, but sepsis is difficult to diagnose because of its non-specific symptoms. Biomarkers such as pancreatic stone protein (PSP) offer rapid results with greater sensitivity and specificity than standard laboratory tests.

METHODS

This study developed a decision tree model to compare a rapid PSP test to standard of care in the emergency department (ED) and intensive care unit (ICU) to diagnose patients with suspected sepsis. Key model parameters included length of hospital and ICU stay, readmission due to infection, cost of sepsis testing, length of antibiotic treatment, antibiotic resistance, and clostridium difficile infections. Model inputs were determined by review of sepsis literature.

RESULTS

The rapid PSP test was found to reduce costs by $1688 per patient in the ED and $3315 per patient in the ICU compared to standard of care. Cost reductions were primarily driven by the specificity of PSP in the ED and the sensitivity of PSP in the ICU.

CONCLUSIONS

The results of the model indicate that PSP testing is cost saving compared to standard of care in diagnosis of sepsis. The abundance of sepsis cases in the ED and ICU make these findings important in the clinical field and further support the potential of sensitive and specific markers of sepsis to not only improve patient outcomes but also reduce healthcare expenditures.

Axial light loss of monocytes as a readily available prognostic biomarker in patients with suspected infection at the emergency department

Objectives

To evaluate the prognostic value of the coefficient of variance of axial light loss of monocytes (cv-ALL of monocytes) for adverse clinical outcomes in patients suspected of infection in the emergency department (ED).

Methods

We performed an observational, retrospective monocenter study including all medical patients ≥18 years admitted to the ED between September 2016 and June 2019 with suspected infection. Adverse clinical outcomes included 30-day mortality and ICU/MCU admission <3 days after presentation. We determined the additional value of monocyte cv-ALL and compared to frequently used clinical prediction scores (SIRS, qSOFA, MEWS). Next, we developed a clinical model with routinely available parameters at the ED, including cv-ALL of monocytes.

Results

A total of 3526 of patients were included. The OR for cv-ALL of monocytes alone was 2.21 (1.98–2.47) for 30-day mortality and 2.07 (1.86–2.29) for ICU/MCU admission <3 days after ED presentation. When cv-ALL of monocytes was combined with a clinical score, the prognostic accuracy increased significantly for all tested scores (SIRS, qSOFA, MEWS). The maximum AUC for a model with routinely available parameters at the ED was 0.81 to predict 30-day mortality and 0.81 for ICU/MCU admission.

Conclusions

Cv-ALL of monocytes is a readily available biomarker that is useful as prognostic marker to predict 30-day mortality. Furthermore, it can be used to improve routine prediction of adverse clinical outcomes at the ED.

Clinical trial registration

Registered in the Dutch Trial Register (NTR) und number 6916.

Changes in Biomarkers and Hemodynamics According to Antibiotic Susceptibility in a Model of Bacteremia

Proper selection of susceptible antibiotics in drug-resistant bacteria is critical to treat bloodstream infection. Although biomarkers that guide antibiotic therapy have been extensively evaluated, little is known about host biomarkers targeting in vivo antibiotic susceptibility. Therefore, we aimed to evaluate the trends of hemodynamics and biomarkers in a porcine bacteremia model treated with insusceptible antibiotics compared to those in susceptible models. Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli, 5.0 * 10^9 CFU) was intravenously administered to 11 male pigs. One hour after bacterial infusion, pigs were assigned to two groups of antibiotics, ceftriaxone (n = 6) or ertapenem (n = 5). Pigs were monitored up to 7 h after bacterial injection with fluid and vasopressor support to maintain the mean arterial blood pressure over 65 mmHg. Blood sampling for blood culture and plasma acquisition was performed before and every predefined hour after E. coli injection. Cytokine (tumor necrosis factor-α, interleukin [IL]-1β, IL-6, IL-8, IL-10, C-reactive protein, procalcitonin, presepsin, heparan sulfate, syndecan, and soluble triggering receptor expressed on myeloid cells-1 [sTREM-1]) levels in plasma were analyzed using enzyme-linked immunosorbent assays. Bacteremia developed after intravenous injection of E. coli, and negative conversion was confirmed only in the ertapenem group. While trends of other biomarkers failed to show differences, the trend of sTREM-1 was significantly different between the two groups (P = 0.0001, two-way repeated measures analysis of variance). Among hemodynamics and biomarkers, the sTREM-1 level at post 2 h after antibiotics administration represented a significant difference depending on susceptibility, which can be suggested as a biomarker candidate of in vivo antibiotics susceptibility. Further clinical studies are warranted for validation.

IMPORTANCE Early and appropriate antibiotic treatment is a keystone in treating patients with sepsis. Despite its importance, blood culture which requires a few days remains as a pillar of diagnostic method for microorganisms and their antibiotic susceptibility. Whether changes in biomarkers and hemodynamics indicate treatment response of susceptible antibiotic compared to resistant one is not well understood to date. In this study using extended-spectrum β-lactamase -producing E. coli bacteremia porcine model, we have demonstrated the comprehensive cardiovascular hemodynamics and trends of plasma biomarkers in sepsis and compared them between two groups with susceptible and resistant antibiotics. While other hemodynamics and biomarkers have failed to differ, we have identified that levels of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) significantly differed between the two groups over time. Based on the data in this study, trends of sTREM-1 obtained before the antibiotics and 2~4 h after the antibiotics could be a novel host biomarker that triggers the step-up choice of antibiotics.

Effects of Early Initiation of High-Dose Dexamethasone Therapy on Pro-Inflammatory Cytokines and Mortality in LPS-Challenged Mice

This study aims to explore the effects of early dexamethasone therapy at low to high doses on the survival and inflammatory responses in lipopolysaccharide (LPS)-challenged mice. We performed two-series experiments to explore the impact of early dexamethasone therapy at different doses (0.5 mg/kg, 1.5 mg/kg, and 5 mg/kg; PO) on pro-inflammatory cytokine levels, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), as well as survival in LPS-treated mice (10 mg/kg, IP). Dexamethasone was administered daily from 24 h before and 5 days after LPS challenge. Dose-dependent improved survival was demonstrated with dexamethasone (p < 0.05). Body weight was significantly decreased within 24 h of LPS injection, with significantly greater weight loss in the dexamethasone groups (p < 0.05). Weight changes were significantly associated with the days after LPS administration (p < 0.01), but not with the dexamethasone dose (p > 0.05). Mice treated with high-dose dexamethasone (5 mg/kg) had a significantly lowered serum TNF-α (134.41 ± 15.83 vs. 408.83 ± 18.32) and IL-6 (22.08 ± 4.34 vs. 91.27 ± 8.56) compared with those without dexamethasone. This study provides essential insights that the suppression of early-phase hyperactivation of pro-inflammatory activities through the early initiation of high-dose dexamethasone therapy increases sepsis-related prognosis.

Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients

Importance

Rapid and accurate discrimination of sepsis and its potential severity currently require multiple assays with slow processing times that are often inconclusive in discerning sepsis from sterile inflammation.

Objective

To analyze a whole-blood, multivalent, host-messenger RNA expression metric for estimating the likelihood of bacterial infection and 30-day mortality and compare performance of the metric with that of other diagnostic and prognostic biomarkers and clinical parameters.

Design, Setting, and Participants

This prospective diagnostic and prognostic study was performed in the surgical intensive care unit (ICU) of a single, academic health science center. The analysis included 200 critically ill adult patients admitted with suspected sepsis (cohort A) or those at high risk for developing sepsis (cohort B) between July 1, 2020, and July 30, 2021.

Exposures

Whole-blood sample measurements of a custom 29-messenger RNA transcriptomic metric classifier for likelihood of bacterial infection (IMX-BVN-3) or 30-day mortality (severity) (IMX-SEV-3) in a clinical-diagnostic laboratory setting using an analysis platform (510[k]-cleared nCounter FLEX; NanoString, Inc), compared with measurement of procalcitonin and interleukin 6 (IL-6) plasma levels, and maximum 24-hour sequential organ failure assessment (SOFA) scores.

Main Outcomes and Measures

Estimated sepsis and 30-day mortality performance.

Results

Among the 200 patients included (124 men [62.0%] and 76 women [38.0%]; median age, 62.5 [IQR, 47.0-72.0] years), the IMX-BVN-3 bacterial infection classifier had an area under the receiver operating characteristics curve (AUROC) of 0.84 (95% CI, 0.77-0.90) for discriminating bacterial infection at ICU admission, similar to procalcitonin (0.85 [95% CI, 0.79-0.90]; P = .79) and significantly better than IL-6 (0.67 [95% CI, 0.58-0.75]; P < .001). For estimating 30-day mortality, the IMX-SEV-3 metric had an AUROC of 0.81 (95% CI, 0.66-0.95), which was significantly better than IL-6 levels (0.57 [95% CI, 0.37-0.77]; P = .006), marginally better than procalcitonin levels (0.65 [95% CI, 0.50-0.79]; P = .06), and similar to the SOFA score (0.76 [95% CI, 0.62-0.91]; P = .48). Combining IMX-BVN-3 and IMX-SEV-3 with procalcitonin or IL-6 levels or SOFA scores did not significantly improve performance. Among patients with sepsis, IMX-BVN-3 scores decreased over time, reflecting the resolution of sepsis. In 11 individuals at high risk (cohort B) who subsequently developed sepsis during their hospital course, IMX-BVN-3 bacterial infection scores did not decline over time and peaked on the day of documented infection.

Conclusions and Relevance

In this diagnostic and prognostic study, a novel, multivalent, transcriptomic metric accurately estimated the presence of bacterial infection and risk for 30-day mortality in patients admitted to a surgical ICU. The performance of this single transcriptomic metric was equivalent to or better than multiple alternative diagnostic and prognostic metrics when measured at admission and provided additional information when measured over time.

EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION

ABSTRACT

Objective: The aim of this study was to characterize early urinary gene expression differences between patients with sepsis and patients with sterile inflammation and summarize in terms of a reproducible sepsis probability score. Design: This was a prospective observational cohort study. Setting: The study was conducted in a quaternary care academic hospital. Patients: One hundred eighty-six sepsis patients and 78 systemic inflammatory response syndrome (SIRS) patients enrolled between January 2015 and February 2018. Interventions: Whole-genome transcriptomic analysis of RNA was extracted from urine obtained from sepsis patients within 12 hours of sepsis onset and from patients with surgery-acquired SIRS within 4 hours after major inpatient surgery. Measurements and Main Results: We identified 422 of 23,956 genes (1.7%) that were differentially expressed between sepsis and SIRS patients. Differentially expressed probes were provided to a collection of machine learning feature selection models to identify focused probe sets that differentiate between sepsis and SIRS. These probe sets were combined to find an optimal probe set (UrSepsisModel) and calculate a urinary sepsis score (UrSepsisScore), which is the geometric mean of downregulated genes subtracted from the geometric mean of upregulated genes. This approach summarizes the expression values of all decisive genes as a single sepsis score. The UrSepsisModel and UrSepsisScore achieved area under the receiver operating characteristic curves 0.91 (95% confidence interval, 0.86-0.96) and 0.80 (95% confidence interval, 0.70-0.88) on the validation cohort, respectively. Functional analyses of probes associated with sepsis demonstrated metabolic dysregulation manifest as reduced oxidative phosphorylation, decreased amino acid metabolism, and decreased oxidation of lipids and fatty acids. Conclusions: Whole-genome transcriptomic profiling of urinary cells revealed focused probe panels that can function as an early diagnostic tool for differentiating sepsis from sterile SIRS. Functional analysis of differentially expressed genes demonstrated a distinct metabolic dysregulation signature in sepsis.

Recognising Sepsis as a Health Priority in Sub-Saharan African Country: Learning Lessons from Engagement with Gabon’s Health Policy Stakeholders

Sepsis has been recognised as a global health priority by the United Nations World Health Assembly, which adopted a resolution in 2017 to improve sepsis prevention, diagnosis, and management globally. This study investigated how sepsis is prioritised in Gabon. From May to November 2021, we conducted a qualitative study in healthcare stakeholders at the local, regional, and national levels. Stakeholders included the Ministry of Health (MOH), ethics/regulatory bodies, research institutions, academic institutions, referral hospitals, international funders, and the media. Twenty-three multisectoral stakeholders were interviewed. Respondents indicated that sepsis is not yet prioritised in Gabon due to the lack of evidence of its burden. They also suggest that the researchers should focus on linkages between sepsis and the countries’ existing health sector priorities to accelerate sepsis prioritisation in health policy. Stakeholder awareness and engagement might be accelerated by involving the media in the generation of communication strategies around sepsis awareness and prioritisation. There is a need for local, regional and national evidence to be generated by researchers and taken up by policymakers, focusing on linkages between sepsis and a country’s existing health sector priorities. The MOH should set sepsis reporting structures and develop appropriate sepsis guidelines for identification, management, and prevention.

On prioritising global health's triple crisis of sepsis, COVID-19 and antimicrobial resistance: a mixed-methods study from Malawi

Sepsis causes 20% of global deaths, particularly among children and vulnerable populations living in developing countries. This study investigated how sepsis is prioritised in Malawi’s health system to inform health policy. In this mixed-methods study, twenty multisectoral stakeholders were qualitatively interviewed and asked to quantitatively rate the likelihood of sepsis-related medium-term policy outcomes being realised. Respondents indicated that sepsis is not prioritised in Malawi due to a lack of local sepsis-related evidence and policies. However, they highlighted strong linkages between sepsis and maternal health, antimicrobial resistance and COVID-19, which are already existing national priorities, and offers opportunities for sepsis researchers as policy entrepreneurs. To address the burden of sepsis, we recommend that funding should be channelled to the generation of local evidence, evidence uptake, procurement of resources and treatment of sepsis cases, development of appropriate indicators for sepsis, adherence to infection prevention and control measures, and antimicrobial stewardship.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

Spectroscopy detects skeletal muscle microvascular dysfunction during onset of sepsis in a rat fecal peritonitis model

Sepsis is a dysregulated host inflammatory response to infection potentially leading to life-threatening organ dysfunction. The objectives of this study were to determine whether early microvascular dysfunction (MVD) in skeletal muscle can be detected as dynamic changes in microvascular hemoglobin (MVHb) levels using spectroscopy and whether MVD precedes organ histopathology in septic peritonitis. Skeletal muscle of male Sprague-Dawley rats was prepared for intravital microscopy. After intraperitoneal injection of fecal slurry or saline, microscopy and spectroscopy recordings were taken for 6 h. Capillary red blood cell (RBC) dynamics and SO₂ were quantified from digitized microscopy frames and MVHb levels were derived from spectroscopy data. Capillary RBC dynamics were significantly decreased by 4 h after peritoneal infection and preceded macrohemodynamic changes. At the same time, low-frequency oscillations in MVHb levels exhibited a significant increase in Power in parts of the muscle and resembled oscillations in RBC dynamics and SO₂. After completion of microscopy, tissues were collected. Histopathological alterations were not observed in livers, kidneys, brains, or muscles 6 h after induction of peritonitis. The findings of this study show that, in our rat model of sepsis, MVD occurs before detectable organ histopathology and includes ~ 30-s oscillations in MVHb. Our work highlights MVHb oscillations as one of the indicators of MVD onset and provides a foundation for the use of non-invasive spectroscopy to continuously monitor MVD in septic patients.

High Fecal Carriage of Multidrug Resistant Bacteria in the Community among Children in Northwestern Tanzania

Colonization of multidrug resistant (MDR) bacteria is associated with subsequent invasive infections in children with comorbidities. This study aimed to determine the resistance profile and factors associated with MDR pathogen colonization among HIV−and HIV+ children below five years of age in Mwanza, Tanzania. A total of 399 (HIV− 255 and HIV+ 144) children were enrolled and investigated for the presence of MDR bacteria. The median [IQR] age of children was 19 (10–36) months. Out of 27 Staphylococcus aureus colonizing the nasal cavity, 16 (59.5%) were methicillin resistant while 132/278 (47.2%) of Enterobacteriaceae from rectal swabs were resistant to third generation cephalosporins, with 69.7% (92/132) exhibiting extended spectrum beta lactamase (ESBL) phenotypes. The proportion of resistance to gentamicin, amoxicillin/clavulanic acid and meropenem were significantly higher among HIV+ than HIV− children. A history of antibiotic use in the last month OR 2.62 [1.1, 6.9] (p = 0.04) and history of a relative admitted from the same household in the past three months OR 3.73 [1.1, 13.2] (p = 0.03) independently predicted ESBL rectal colonization. HIV+ children had significantly more fecal carriage of isolates resistant to uncommonly used antibiotics. There is a need to strengthen antimicrobial stewardship and Infection Prevention and Control (IPC) programs to prevent the emergence and spread of MDR pathogens in children.

Improving sepsis recognition through use of the Sepsis Trust's community screening tool

Sepsis is associated with high levels of morbidity and mortality. All healthcare professionals have a responsibility to ensure they have sufficient knowledge to effectively screen patients for signs and symptoms of sepsis. In the community setting, screening for sepsis can be challenging, due to the complexity within the patient population and difficulties associated with observation for changes in the patient's condition. The Sepsis Trust community nursing sepsis screening tool provides decision-making support to community healthcare professionals, enabling them to make a rapid assessment for risk factors for sepsis, ensuring a proportionate, consistent and appropriate response. Through implementation of a decision-support tool within the clinical setting, it is likely that patients at risk of sepsis will be identified earlier, and patients will be escalated in a more consistent manner. This process of improving consistency in practice can improve patient outcomes, including mortality, morbidity and overall patient experience.