Sepsis Biomarkers Research Articles

Decreased monocytic HLA-DR in patients with sepsis: Prediction of diagnosis, severity and prognosis.

Sepsis is characterized by high incidence and mortality rates, making early recognition and risk stratification critical for preventing delayed treatment and overtreatment. This study investigated the potential of monocytic (m) HLA-DR as a diagnostic and prognostic biomarker of sepsis. In this prospective study, we collected blood in EDTA-anticoagulated tubes within 48 h from patients diagnosed with sepsis or infection and analyzed the percentage of mHLA-DR in peripheral blood mononuclear cells, C-reactive protein, and procalcitonin within 2 h of collection. We gathered clinical and laboratory data, including sex, age, and comorbidities, calculated the number of dysfunctional organs and sequential organ failure assessment (SOFA) score, and recorded the survival status of patients with sepsis on the 30th day after admission. mHLA-DR levels were lower in patients with sepsis (median 46.60 [interquartile range 23.86-66.51]%) than infection (75.44 [52.13-91.50]%). mHLA-DR could distinguish sepsis from infection with an area under the curve (AUC) of 0.724 (95 %CI 0.624-0.824). Decreased mHLA-DR levels have been found in septic patients with shock or secondary infections. mHLA-DR expression decreased with an increasing number of dysfunctional organs and higher SOFA score. In 30-day non-survivors, mHLA-DR levels were 26.94 (12.06-44.45)%, significantly lower than in survivors (55.20 [24.83-72.37]%). mHLA-DR predicted sepsis prognosis with an AUC of 0.750 (95 %CI 0.623-0.877). When the cut-off value was <52.29 %, the sensitivity and specificity of mHLA-DR for prognosis were 100 % and 52.83 %, respectively. The 30-day survival rate of septic patients with mHLA-DR ≥ 52.29 % was 6.798 (95 %CI 2.075-22.27) times higher than that of patients with mHLA-DR < 52.29 %. mHLA-DR negatively correlates with the severity of sepsis and could be used as a diagnostic and prognostic biomarker for sepsis.

Plasma alpha-fetoprotein in neonatal foals affected by prematurity, sepsis and neonatal encephalopathy.

Alpha-fetoprotein (AFP) concentrations have been reported in healthy foals and proposed as a biomarker of sepsis in foals born from mares with experimentally induced placentitis. This study aimed to describe the diagnostic and prognostic value of plasma AFP in foals spontaneously affected by different diseases. The study included all foals less than 72h old that were diagnosed with either: (1) prematurity (PRE), when born prior to 320 days of gestation with immature physical characteristics; (2) sepsis (SEP), in the presence of both positive blood culture and SIRS or (3) neonatal encephalopathy (NE), with evidence of hypoxic-ischemic injury. Data from healthy foals (H; n=20) were obtained from a previous study. Foals received a complete physical and hematochemical evaluation and blood culture sample collection at hospital admission. Forty-six foals with an average age of 16h were enrolled and divided into: PRE group (n=7); SEP group (n=14); NE group (n=25). AFP was measured in plasma collected at admission using a commercially available immunoassay validated for horses. AFP was increased in foals in PRE, SEP and NE groups compared with healthy ones (P<0.001) but was not able to discriminate between different diseases and outcomes. Overall, AFP was negatively correlated with foal age (r=-0.6; P<0.001), foal weight (r=-0.3; P=0.048), monocytes count (r=-0.4; P=0.011) and SAA concentration (r=-0.4; P=0.011). AFP appears to be a useful but non-specific indicator of neonatal health, since it upregulates not only in the presence of SIRS and bacteremia, but also during prematurity and hypoxic-ischemic injury.

Selenoprotein P as a prognostic biomarker of burn sepsis: A prospective cohort study

IntroductionSeverely burned patients exhibit increased nutritional requirements and are at high risk of developing sepsis. Selenium is an essential trace element supporting antioxidant and anti-inflammatory pathways, mediated by incorporation into selenoproteins. The selenium status may affect sepsis risk in burn injury. MethodsThis prospective cohort study included 90 adult patients admitted to Zurich Burn Center, Switzerland. All patients received a continuous intravenous infusion of 1000μg sodium selenite per day during the first week as part of local standard of care. Three complementary biomarkers of serum selenium status were determined at nine time-points up to six months postburn, namely total selenium, selenoprotein P, and glutathione peroxidase 3. The resulting data were correlated to clinical parameters and outcomes, with sepsis as primary end point. ResultsA high fraction of the patients displayed selenium deficiency already at admission, and developed sepsis during hospitalization (n = 55; 61%). Selenium status at admission was inversely related to burn severity. Low baseline selenoprotein P was associated with sepsis incidence, irrespective of trauma severity (adjusted HR, 1.94; 95% CI, 1.05–3.63; p = 0.035). Burn severity and baseline concentrations of selenoprotein P and white blood cells corporately predicted sepsis with an area under the curve of 0.84 (95% CI, 0.75–0.93; p < 0.0001). Supplemental selenium was associated with a transient normalization of selenium status. ConclusionConsidering its rapid decline following severe burn injury, the assessment of serum selenoprotein P upon admission may contribute to an early prediction of sepsis risk.

Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study.

Sepsis is a serious condition that occurs when the body's response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored. This research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, were utilized in identifying crucial hub genes. Receiver Operating Characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of these genetic markers. Additionally, single-gene enrichment and immune infiltration analyses were conducted to investigate the underlying mechanisms involving these hub genes in sepsis. Three hub genes, DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8), were identified and validated for their diagnostic efficacy, showing area under the curve (AUC) values above 0.7 in both test and validation sets. Enrichment analyses revealed that these genes are involved in key pathways such as p53 signaling and Toll-like receptor signaling. Immune infiltration analysis indicated significant correlations between hub genes and various immune cell types, suggesting their roles in modulating immune responses during sepsis. The study highlights the diagnostic potential of m5C-related genes in sepsis and their involvement in immune regulation. These findings offer new insights into sepsis pathogenesis and suggest that DNMT1, TP53, and TLR8 could serve as valuable biomarkers for early diagnosis. Further studies should prioritize validating these biomarkers in clinical settings and investigating their potential for therapy.

Cloperastine Reduces IL-6 Expression via Akt/GSK3/Nrf2 Signaling in Monocytes/Macrophages and Ameliorates Symptoms in a Mouse Sepsis Model Induced by Lipopolysaccharide.

Cloperastine (CLP) is a drug with a central antitussive effect that is used to treat bronchitis. Therefore, we have attempted to examine the anti-inflammatory effects of CLP. CLP reduced the secretion of interleukin (IL)-6, a pro-inflammatory cytokine, from RAW264.7 monocyte/macrophage-linage cells treated with lipopolysaccharide (LPS). IL-6 is a biomarker of sepsis and has been suggested to exacerbate its symptoms. We found that the intraperitoneal administration of CLP reduced IL-6 levels in the lungs and also improved hypothermia in mice with LPS-induced sepsis. CLP ameliorated kidney pathologies such as congestion and increased the survival rate of mice administered with a lethal dose of LPS. To reveal the mechanisms underlying the anti-inflammatory function of CLP, we analysed the intracellular signaling in LPS-treated RAW264.7 cells. CLP induced the phosphorylation of protein kinase B (Akt) and glycogen synthase kinase 3 (GSK3) and also increased the amount of nuclear factor erythroid-2-related factor 2 (Nrf2) in RAW264.7 cells with/without LPS. Wortmannin, an inhibitor of phosphoinositide 3-kinase (PI3K), reduced the upregulated phosphorylation levels of Akt and GSK3 and the increased amount of Nrf2. It also halted the reduction of IL-6 secretion caused by CLP. These results suggest that CLP has an anti-inflammatory function via Akt/GSK3/Nrf2 signaling and could be a candidate drug for the treatment of inflammatory diseases, including sepsis.

Transcriptional pathways of terminal differentiation in high- and low-density blood granulocytes in sepsis

BackgroundTrauma and infection induce emergency granulopoiesis. Counts of immature granulocytes and transcriptional pathways of terminal granulocytic differentiation in blood are elevated in sepsis but correlate with disease severity. This limits their performance as sepsis biomarkers in critically ill patients. We hypothesized that activation of these pathways in sepsis is attributable to immature low-density (LD) rather than mature high-density (HD) granulocytes.MethodsWe included patients with sepsis and systemic inflammatory response syndrome (SIRS) of comparable disease severity, and additionally septic shock, on intensive or intermediate care unit admission. Blood granulocyte isolation by CD15 MicroBeads was followed by density-gradient centrifugation. Flow cytometry was used to determine counts of developmental stages (precursors) and their relative abundancies in total, HD, and LD granulocytes. Five degranulation markers were quantified in plasma by multiplex immunoassays. A set of 135 genes mapping granulocyte differentiation was assayed by QuantiGene™ Plex. CEACAM4, PLAC8, and CD63 were analyzed by qRT-PCR. Nonparametric statistical tests were applied.ResultsPrecursor counts appeared higher in sepsis than SIRS but did not correlate with disease severity for early immature and mature granulocytes. Precursor subpopulations were enriched at least ten-fold in LD over HD granulocytes without sepsis-SIRS differences. Degranulation markers in blood were comparable in sepsis and SIRS. Higher expression of early developmental genes in sepsis than SIRS was more pronounced in LD and less in HD than total granulocytes. Only the cell membrane protein encoding genes CXCR2 and CEACAM4 were more highly expressed in SIRS than sepsis. By qRT-PCR, the azurophilic granule genes CD63 and PLAC8 showed higher sepsis than SIRS levels in LD granulocytes and PLAC8 also in total granulocytes where its discriminatory performance resembled C-reactive protein (CRP).ConclusionsTranscriptional programs of early terminal granulocytic differentiation distinguish sepsis from SIRS due to both higher counts of immature granulocytes and elevated expression of early developmental genes in sepsis. The sustained expression of PLAC8 in mature granulocytes likely accounts for its selection in the whole blood SeptiCyte™ LAB test. Total granulocyte PLAC8 rivals CRP as sepsis biomarker. However, infection-specific transcriptional pathways, that differentiate sepsis from sterile stress-induced granulocytosis more reliably than CRP, remain to be identified.

Metabolic biomarkers of neonatal sepsis: identification using metabolomics combined with machine learning.

Sepsis is a common disease associated with neonatal and infant mortality, and for diagnosis, blood culture is currently the gold standard method, but it has a low positivity rate and requires more than 2days to develop. Meanwhile, unfortunately, the specific biomarkers for the early and timely diagnosis of sepsis in infants and for the determination of the severity of this disease are lacking in clinical practice. Samples from 18 sepsis infants with comorbidities, 25 sepsis infants without comorbidities, and 25 infants with noninfectious diseases were evaluated using a serum metabolomics approach based on liquid chromatography‒mass spectrometry (LC‒MS) technology. Differentially abundant metabolites were screened via multivariate statistical analysis. In addition, least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses were conducted to identify the key metabolites in infants with sepsis and without infections. The random forest algorithm was applied to determine key differentially abundant metabolites between sepsis infants with and without comorbidities. Receiver operating characteristic (ROC) curves were generated for biomarker value testing. Finally, a metabolic pathway analysis was conducted to explore the metabolic and signaling pathways associated with the identified differentially abundant metabolites. A total of 189 metabolites exhibited significant differences between infectious infants and noninfectious infants, while 137 distinct metabolites exhibited differences between septic infants with and without comorbidities. After screening for the key differentially abundant metabolites using LASSO and SVM-RFE analyses, hexylamine, psychosine sulfate, LysoPC (18:1 (9Z)/0:0), 2,4,6-tribromophenol, and 25-cinnamoyl-vulgaroside were retained for the diagnosis of infant sepsis. ROC curve analysis revealed that the area under the curve (AUC) was 0.9200 for hexylamine, 0.9749 for psychosine sulfate, 0.9684 for LysoPC (18:1 (9Z)/0:0), 0.7405 for 2,4,6-tribromophenol, 0.8893 for 25-cinnamoyl-vulgaroside, and 1.000 for the combination of all metabolites. When the septic infants with comorbidities were compared to those without comorbidities, four endogenous metabolites with the greatest importance were identified using the random forest algorithm, namely, 12-oxo-20-trihydroxy-leukotriene B4, dihydrovaltrate, PA (8:0/12:0), and 2-heptanethiol. The ROC curve analysis of these four key differentially abundant metabolites revealed that the AUC was 1 for all four metabolites. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and porphyrin metabolism play important roles in infant sepsis. Serum metabolite profiles were identified, and machine learning was applied to identify the key differentially abundant metabolites in septic infants with comorbidities, septic infants without comorbidities, and infants without infectious diseases. The findings obtained are expected to facilitate the early diagnosis of sepsis in infants and determine the severity of the disease.

An analytical observational study for diagnostic accuracy of volume, conductivity & scatter (VCS) indices of neutrophils for diagnosis of sepsis in an emergency hospital setting.

Background & objectives The newer technique using an innovative volume conductivity scatter (VCS) technology is emerging as a surrogate for sepsis diagnosis. The VCS technology offers a more objective method to measure cell volume (V), characterize conductivity (C) and light scatter (S) directly from more than 8,000 white blood cells (WBCs). However, diagnostic performance of VCS parameters in sepsis has not been extensively tested in routine hospital emergency settings. Therefore, the present study aimed to investigate the diagnostic and prognostic performance of VCS markers of neutrophils in our local hospital emergency setting. Methods It was an observational analytical study with 41 cases of sepsis and 43 healthy controls aged above 18 yr. Individuals with acute coronary syndrome and individuals with already diagnosed Human Immunodeficiency Virus (HIV) infection were excluded from the study. Results The mean neutrophil volume (MNV) values were not significantly different between cases and controls (P 0.138) whereas mean neutrophil conductance (MNC) and mean neutrophil scatter (MNS) measurements were significantly higher among cases as compared to controls (both P-values <0.001). According to Receiver Operating Characteristics (ROCs) curve analysis, MNV in our study failed to show statistically significant discriminatory ability in sepsis (AUC 0.54) whereas MNC (AUC 0.98) and MNS (AUC 0.95) showed marked discriminatory ability in diagnosing sepsis in this study cohort. Interpretation & conclusions Among VCS parameters, MNV failed as a standalone biomarker of sepsis in routine emergency setting whereas MNC and MNS had statistically significant diagnostic and discriminatory accuracies among hospitalized affected individuals with sepsis.

Clinical Utility of Recently FDA Approved IntelliSep test (Sepsis biomarker) for early Diagnosis of Sepsis: Comparison with other Biomarkers and von Willebrand Factor/ADAMTS13 Ration

Abstract Background Sepsis, a relatively common presentation in emergency departments, is a life-threatening disease. Recently, IntelliSep test by Cytovale (San Francisco, CA) received FDA approval as sepsis biomarker. We investigated clinical utility of this test using 44 patients (19 disease group and 25 non-disease group). Materials and Methods IntelliSep assesses cellular host response via deformability cytometry of leukocyte biophysical properties and intended for use in conjunction with clinical assessments and laboratory findings for early detection of sepsis. Test results are available within 10 minutes. Results consists of IntelliSep Band 1 (low risk, score: 0.1-4.9) to Band 3 (high risk, score: 6.3-10; total scale: 0.1-10). We measured IntelliSep score in 44 patients using EDTA blood. Left over plasma was used for measuring both plasma von Willebrand factor (vWF) antigen and ADAMTS13 antigen by ELISA assays. Monocyte distribution width (MDW) were obtained during routine CBC analysis using Beckman hematology analyzer. Lactate and high sensitivity troponin I were measured using Beckman analyzer. Results The median IntelliSep score was two-fold higher in the disease group (mean score 7.1, SD: 1.5, median: 7.4) compared to non-disease group (mean score 4.0, SD: 1.4, Median: 3.7) indicating that this new test can identify patients with high risk of developing sepsis and also rule out low risk patients (p&amp;lt;0.01 by both Mann-Whitney U test two tailed and t-test two tailed). The median MDW was also 34.8 % higher in the disease group (mean:25.6, SD: 5.0, Median: 24.4) compared to non-disease group (mean: 18.9, SD: 2.4, Median: 18.1) and difference was also statistically significant (p &amp;lt; 0.01 by both Mann-Whitney U test and t-test, two tailed). However, correlation between IntelliSep scores and MDW was only modest (overall, r= 0.66). Both lactate and procalcitonin concentrations were significantly higher in the disease group compared to the non-disease group. Interestingly, high-sensitivity troponin I concentrations were also significantly higher in the disease group. In addition, we observed significantly higher plasma vWF antigen in the disease group compared to the non-disease group (mean: 67.4 ug/mL in disease group vs 34.1 ug/mL in the non-disease group. However, in some patients in the disease group plasma vWF values were 6.8-fold higher than normal. In addition, vWF/ADAMTS13 ratio was also significantly elevated in the disease group compared to non-disease group. Discussion New IntelliSep test is useful in early prediction of sepsis in patients admitted to emergency department and may be superior to MDW and lactate for early diagnosis. Significant increase in plasma vWF in these patients indicate that vWF may also be a surrogate marker for endotheliopathy in sepsis. Lack of correlation between plasma VWF and Intellisep results indicates that these two markers probably measure different pathways for sepsis (endothelial activation vs neutrophil activation).

Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection

ObjectiveTo evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients compared to other hematologic parameters and vital signs, and to determine whether routine parameters could substitute MDW in machine learning models. MethodsWe conducted a retrospective analysis of data from 10,229 ED patients admitted to a large regional safety-net hospital in Cleveland, Ohio who had suspected infections and developed sepsis-associated poor outcomes. We developed a new analytical framework consisting of seven data models and an ensemble of high accuracy machine learning (ML) algorithms (accuracy values ranging from 0.83 to 0.90) to predict sepsis-associated poor outcomes (3-day intensive care unit stay or death). Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Value (SHAP) interpretability methods were utilized to assess the contributions of individual hematologic parameters. ResultsThe ML interpretability analysis indicated that the predictive value of MDW is significantly reduced when other hematological parameters and vital signs are considered. The results suggest that complete blood count with differential (CBD-DIFF) alongside vital signs can effectively replace MDW in high accuracy machine learning algorithms for screening poor outcome associated with sepsis. ConclusionMDW, although a newly approved biomarker for sepsis, does not significantly enhance prediction models when combined with routinely available parameters and vital signs. Hospitals, especially those with resource constraints, can rely on existing parameters with high accuracy machine learning models to predict sepsis outcomes effectively, thereby reducing the need for specialized tests like MDW.

The Prospective Analysis of Biomarkers in Sepsis: Correlation With Clinical Outcomes.

Background Sepsis is a medical emergency and necessitates immediate diagnosis and treatment to prevent the progression to severe sepsis, septic shock, and potentially mortality. Aim This study aims to study the diagnostic significance of conventional and new markers, interleukin-10 (IL-10), in predicting the severity of sepsis. Methodology A prospective observational study was conducted in the department of surgery in a tertiary care hospital in Pune, India. The study included 100 patients diagnosed with a quick Sequential Organ Failure Assessment (qSOFA) score of ≥2. Serum C-reactive protein (CRP), procalcitonin (PCT), and interleukin-10 (IL-10) levels were measured. Receiver operating characteristic (ROC) curves were plotted to assess the diagnostic performance of these biomarkers. Results The mean serum CRP level on day 7 was significantly higher than the baseline, day 1, and day 3 groups (p=0.0001). On analysis by repeated measure, the ANOVA test revealed that the mean CRP levels on day 7were significantly higher. The mean PCT levels on day 7, day 3, and day 1 groups were significantly lower than those on day 1, day 2, and day 2, respectively (p=0.0001). The mean p-value of 3.3 g/L CRP was significantly lower on day 1 than that on day 3. IL-10 levels showed a significant upward trend, rising from 5.21 pg/mL at baseline to 7.57 pg/mL by day 7, with a p-value of <0.0001. Our cohort population showed elevated IL-10 values on the day of admission in a total of 15 patients. In our study, we observed that 11 patients with elevated IL-10 levels progressed towardmultiple organ dysfunction syndrome (MODS) and four mortalities. IL-10 is a crucial marker for identifying patients with worsening surgical sepsis. Conclusion IL-10, CRP, and PCT have potential as prognostic markers in assessing and predicting disease severity.The dynamic changes in these biomarkers correlate strongly with clinical outcomes, suggesting their role in guiding treatment decisions.

B-137 Sepsis risk prediction using machine learning (ML) and routine blood markers up to 1 week before emergency admission

Abstract Background Sepsis is a major global health concern, responsible for approximately 50 million cases and 11 million deaths annually, representing 20% of global mortality. It is the leading cause of hospital readmissions and deaths, with associated costs exceeding $38 billion per year. Alarmingly, 80% of sepsis cases occur outside the hospital, and 70% of patients visit an outpatient facility within a week of admission. Early diagnosis and appropriate treatment could prevent 80% of sepsis-related deaths. Our study demonstrates that machine learning models hold significant promise in identifying sepsis risks using routine blood tests up to 1 week before admission. Methods For our study, we utilized a dataset of over 25,000 patient records, including both sepsis and non-sepsis cases, sourced from MIMIC-IV (version 2.2), a comprehensive electronic health record dataset from Beth Israel Deaconess Medical Center, Boston, MA. We employed a gradient boosted model with 300 trees, a maximal depth of 30 layers, and gain ratio as the criterion for attribute selection. The model's performance was evaluated using 10-fold cross-validation, demonstrating optimal performance. Input parameters included age, gender, and results of routine blood markers such as complete blood counts, differential counts, comprehensive metabolic panels, and lipid panels recorded up to 1 week before sepsis diagnosis. Results Our model achieved remarkable predictive accuracy for sepsis risk, with an area under the receiver operating characteristic curve (AUC) of 0.99 and accuracy of 0.99. Notably, the model achieved 1.0 sensitivity, 0.99 specificity, 0.99 positive predictive value, 1.0 negative predictive value, 0.99 F measure, and 0.99 Youden index. Calcium, protein, liver enzymes, hematocrit, white blood cells, and cholesterol were identified as key contributors to sepsis risk prediction. Importantly, prediction accuracy remained consistent up to 1 week prior to diagnosis. Conclusions In conclusion, dysregulated intracellular calcium handling during sepsis is associated with an intensified inflammatory response, cellular death, and subsequent organ dysfunction. Low serum protein levels, particularly albumin, are linked to poor outcomes in sepsis. Recent studies have identified liver dysfunction as an early event in sepsis, underscoring its significance in the disease process. Elevated bilirubin levels are associated with an increased risk of mortality. Low hematocrit (HCT) levels are linked to higher 30-day mortality and serve as an independent prognostic biomarker in sepsis. Monocytes play a significant role in sepsis pathophysiology, undergoing various changes including preserved phagocytic activity, increased reactive oxygen species and nitric oxide generation, and decreased production of inflammatory cytokines such as IL-6 and TNF-alpha. Septic patients often exhibit lymphopenia, affecting CD4 and CD8 T cells, B cells, and natural killer (NK) cells. Hypocholesterolemia is common in sepsis and serves as an important early prognostic factor, with low plasma concentrations associated with poor outcomes. Since every hour of delayed treatment increases the risk of death by 4%-9%, changes in routine blood markers detected by machine learning models can provide early indications of impending sepsis up to 1 week before diagnosis.

A-246 Can CRP be used to rule in or rule out elevated procalcitonin concentrations in the emergency department?

Abstract Background C-reactive Protein (CRP) and procalcitonin (PCT) are commonly used sepsis biomarkers but have very different properties. PCT rises earlier, has a shorter half-life and is more specific for sepsis than CRP. It is however significantly more expensive. This study examined whether CRP measurement can be used to screen for the need for PCT measurement in emergency department patients. Methods This was a retrospective study of anonymized records of paired CRP and PCT requests from the emergency department during 2023. All testing was performed using Beckman Coulter AU 5800 (CRP) and Roche e601 (PCT). PCT results were interpreted using the 2023 AACC guidelines on PCT use (PCT&amp;lt;0.1 ug/L: strongly discourage antibiotic use; PCT 0.1-0.25: discourage antibiotic use; PCT 0.26-0.5: encourage antibiotic use; PCT&amp;gt;0.5: strongly encourage antibiotic use) and the ability of CRP to predict PCT exceeding the various cutoffs was calculated using Microsoft Excel and Analyse-It statistical add-on. Results There were 1645 paired results available. The CRP concentrations at 95% and 99% sensitivity and specificity levels for the various PCT cutoffs are shown in the table, together with the projected reductions in PCT testing volumes if PCT testing is restricted to samples with CRP within the ranges defined by the 95% or 99% sensitivity/specificity values. For example, from the table, for PCT&amp;gt;0.1 ug/L cutoff at 95% confidence, subsequent PCT measurement on the sample is only performed if CRP is between 3.4 and 90.5 mg/L. Conclusions Limiting PCT measurements to samples with CRP concentrations inside ranges based on the PCT cutoff of interest and the appropriate confidence level (95% or 99%) can potentially reduce PCT testing by 20-40% in emergency department patients. For laboratories which perform CRP and PCT analysis sequentially on different platforms, this approach allows significant reductions in PCT test volumes while ensuring testing is performed in equivocal cases.

Early Analysis of Endothelial Markers to predict Sepsis in the Emergency Department.

Acute infections and sepsis are a leading cause of death. These patients are primarily encountered at the emergency department (ED), where early assessment for sepsis is necessary to improve outcome. In sepsis, the inflammatory response causes several characteristic pathophysiological changes, including a dysregulated and generalized activation of the endothelium. This study aimed to analyse endothelial markers released to the blood as diagnostic biomarkers for acute infection and sepsis in the ED, as smaller studies have previously shown promising results in other settings. Serum samples from n = 312 adult patients with suspected acute infections at presentation to the ED were utilized. Patients' courses of disease and outcomes were assessed by clinical adjudication. E-Selectin, P-Selectin, ICAM-1, and VCAM-1 were measured by ELISAs. The accuracy of each marker for predicting bacterial infection, sepsis, and in-hospital mortality, was evaluated. For sepsis, E-Selectin and ICAM-1 both showed an AUROC of 0.62, lower than procalcitonin with 0.77 (both p < 0.01) and lactate with 0.73 (p = 0.030 and 0.046, respectively), but similar to CRP with 0.60 (p = 0.758 and 0.876, respectively). For 28-day in-hospital mortality among patients with infection, ICAM-1 performed best with an AUROC of 0.75. Despite promising results in small studies and specific cohorts, particularly in intensive care units, this large-scale evaluation of four endothelial biomarkers highlights their limited diagnostic utility in a broader inclusion set-up design at the earliest possible time-point of evaluation.

A-249 Monocyte distribution width (MDW) as a reliable diagnostic biomarker for sepsis in HIV-infected patients

Abstract Background Sepsis is the main cause of death in patients with acquired immunodeficiency syndrome (AIDS) and early diagnosis of sepsis in AIDS remains a significant challenge. Monocyte distribution width (MDW), as a new type of sepsis auxiliary diagnostic marker, has attracted widespread attention in the research and exploration of its clinical application. This study aims to evaluate the diagnostic efficacy of MDW in the occurrence of sepsis in HIV-infected patients and to construct a new clinical diagnostic model in combination MDW with other indicators. Methods A prospective cohort study was conducted at Shanghai Public Health Center with a total of 602 patients enrolled between December 2022 and August 2023. Sepsis-3 criteria were employed as the diagnostic standard for sepsis. MDW was measured at admission, using the hematology analyzer DxH900 (Beckman Coulter Inc.). Comprehensive clinical data, including complete blood counts and biochemical profiles, were collected. Enrolled participants received a follow-up about their survivals on day 30 and day 90. Five machine learning models were evaluated and compared to select the best performing model. Results The diagnostic performance of MDW for sepsis yielded an AUC of 0.818. With a cut-off value of 25.26, the sensitivity and specificity reached 0.822 and 0.754 respectively. The best performing variable among all five ML algorithms was Random Forest model. The top three variables with the highest importance ranking were platelet, albumin, and hemoglobin. Incorporating MDW and the top three laboratory indicators into a random forest model, the AUC value improved to 0.931. With a cut-off value of 0.5, the sensitivity was 1.00 while the specificity was 0.732. Conclusions MDW was an auxiliary diagnostic biomarker for sepsis in HIV-infected patients with reliable sensitivity and specificity. In combination MDW with other common laboratory biomarkers, a diagnostic model can be established to further improve the diagnostic efficiency of sepsis.

Correlation of Triethylamine N-oxide (TMAO), LPS, and TNF-Alpha Levels With Clinical Features of the Disease in Patients With and Without Septic Shock Infected With COVID-19 Virus

BackgroundInflammation is a response of the immune system to protect the body against various diseases or injuries. Serum trimethylamine N-oxide (TMAO) levels may vary depending on age, gender, habits, comorbidities, and microbiota. AimsIn this study, we investigated whether TMAO levels have diagnostic significance and their potential as a marker in the early diagnosis of the disease. Another aim of the research was to identify changes in TMAO levels as a reflection of the deterioration in the microflora, and IL-6, IL-10, IL-1β, TNF-alpha, and LPS levels in patient groups. Then, we recognized relationships between these parameters in patients infected with COVID-19 without septic shock and with COVID-19 who were without transmission of COVID-19 in septic shock. Study DesignA total of 160 patients were investigated, including 40 patients infected with COVID-19 without septic contact, 40 patients with COVID-19 positive septic shock, 40 patients with COVID-19 negative septic shock, and 40 healthy individuals as the control group. ResultsTNF-α and IL-1β levels were significantly lower (P < 0.001) and IL-6 and IL-10 levels were significantly higher (P < 0.001) in patient groups than in control groups. IL-1β showed a significant decrease, especially in the groups infected with COVID-19. Although IL-6, increased even more in the groups infected with COVID-19. ConclusionsLPS level was remarkably high in the sepsis group infected with COVID-19 compared to the other groups. TMAO level was significantly higher (P < 0.001) in the sepsis group. Therefore, TMAO is a potential biomarker in sepsis and septic shock.

Heparin-binding protein and sepsis-induced coagulopathy: Modulation of coagulation and fibrinolysis via the TGF-β signalling pathway

BackgroundHeparin-binding protein (HBP) levels have been linked to organ failure and may represent an inflammatory biomarker of sepsis. We found disseminated intravascular coagulation (DIC) is associated with higher HBP levels in patients and in in vivo and in vitro models. This prospective, single-center observational study investigated the effects and underlying mechanisms of HBP on the coagulation cascade in sepsis. Methods538 patients with sepsis from June 2016 to December 2019 were enrolled. Mechanisms underlying HBP and the coagulation system were investigated in human umbilical vein endothelial cells (HUVEC) and C57 mice. ResultsIncreased HBP was associated with sepsis-induced DIC. The optimal cutoff value was 37.5 ng/mL (sensitivity: 56 %, specificity: 65 %). Antithrombin-III (AT-III) activity, plasmin-a2 plasmin inhibitor complex (PIC), procalcitonin (PCT), hemoglobin, and HBP ≥37.5 ng/mL were associated with of DIC occurrence. In HUVECs &C57 mice models, Western blotting, qPCR, and immunohistochemistry analysis showed that the binding between HBP and TGF-β receptor 2 (TGFBR2) caused elevation of plasminogen activator inhibitor-1 (PAI-1) levels. Furthermore, we found that mice stimulated with HBP had higher levels of fibrinogen and D-dimer in the blood. HBP treatment caused the accumulation of fibrinogen in mice lung tissue. Treatment with TGFBR2-small interfering RNAs inhibited the effects. ConclusionPatients with sepsis having HBP ≥37.5 ng/mL at admission were more likely to develop DIC. HBP upregulates the expression of fibrinogen and PAI-1 via TGFBR2 and the TGF-β signalling pathway.

ANXA3 as a novel biomarker for sepsis diagnosis: Evidence from integrative WGCNA analysis

Sepsis is a dysregulated immune response to infection that comes with multiple organ dysfunction and high mortality. The management of sepsis relies heavily on early recognition and diagnosis, but current diagnostic methods have limitations in timeliness, sensitivity, and discriminability. This study aims to discover novel biomarkers for sepsis diagnosis. Four datasets from different regions were analyzed using weighted gene co-expression network analysis (WGCNA), and genes with high Gene Significance values across these datasets were overlapped. Finally, two genes, CD177 and ANXA3, were identified. ANXA3 was validated as a potential sepsis biomarker by checking multiple datasets and Receiver Operating Characteristic (ROC) Curve Analysis. Of note, ANXA3 could distinguish not only between adult and child sepsis patients and healthy controls, but also between septic shock and cardiogenic shock. Moreover, a murine sepsis model was established and the results showed that the transcription of ANXA3 in peripheral blood of septic mice was significantly higher than that of healthy controls, while Escherichia coli infection alone did not significantly increase the transcription level of this gene. Subsequent studies of sepsis in mice revealed that the predictive effect of Anxa3 on sepsis could be observed as early as 6 h post-modeling. Interestingly, ANXA3 expression was predominantly up-regulated in myeloid cells, up-regulated in spleen, down-regulated in lung, and not detected in liver after sepsis modeling. Taken together, this study provides a way for the discovery of biomarkers and finds that ANXA3 may be a novel diagnostic biomarker for sepsis.

Diagnostic and Prognostic Utilities of Pancreatic Stone Protein in Patients with Suspected Sepsis.

Background/Objectives: Pancreatic stone protein (PSP) is an emerging biomarker of sepsis that is secreted from pancreas sensing remote organ damages. We explored the diagnostic and prognostic utilities of PSP in patients with suspected sepsis. Methods: In a total of 285 patients (suspected sepsis, n = 148; sepsis, n = 137), we compared PSP with procalcitonin (PCT) and sequential organ failure assessment (SOFA) score. Sepsis diagnoses were explored using receiver operating characteristic curve analyses with area under the curves (AUCs). Clinical outcomes (in-hospital mortality, 30-day mortality, and kidney replacement therapy [KRT]) were explored using the Kaplan-Meier method and a multivariate analysis with hazard ratio (HR). Results: PCT and PSP were comparable for sepsis diagnosis (AUC = 0.71-0.72, p < 0.001). The sepsis proportion was significantly higher when both biomarkers increased than when either one or both biomarkers did not increase (89.0% vs. 21.3-47.7%, p < 0.001). Each biomarker quartile (Q1-Q4) differed significantly according to their SOFA score (all p < 0.001). Compared with Q1, the Q2-Q4 groups showed worse clinical outcomes (p = 0.002-0.041). Both biomarkers added to the SOFA score showed higher HRs than the SOFA score alone (3.3-9.6 vs. 2.8-4.2, p < 0.001-0.011), with nearly 2.5-fold higher HR (9.6 vs. 4.2) for predicting KRT. Conclusions: Although PCT and PSP did not independently predict clinical outcomes in the multivariate analysis, PSP demonstrated diagnostic and prognostic utilities in patients with suspected sepsis, especially for predicting kidney dysfunction. PSP, alone or in combination with PCT, would be a valuable tool that can be added to clinical assessments.

KC-like chemokine as a biomarker of sepsis in dogs with pyometra

BackgroundSepsis, defined as a dysregulated inflammatory response to infection inducing organ dysfunction, is a common cause of mortality in both humans and animals. Early detection and treatment is essential for survival, but accurate diagnosis is challenging due to the lack of specific biomarkers for sepsis. This study explored the potential of the keratinocyte-derived chemokine (KC)-like protein in dogs as a biomarker of sepsis in dogs with bacterial uterine infection (pyometra). The aim was to compare KC-like concentrations in dogs with pyometra with or without sepsis and to assess associations between KC-like and clinical variables, including days of hospitalization as an outcome.ResultsA mouse KC ELISA was validated and used to determine the concentrations of KC-like in serum from 34 dogs with pyometra and 18 healthy controls. Dogs with pyometra were classified as having sepsis based on two different criteria for systemic inflammatory response syndrome (SIRS), resulting in 74% and 30% sepsis-positive, respectively. The concentration of KC-like protein was higher in pyometra dogs with sepsis than in pyometra dogs without sepsis (p < 0.05) and in healthy controls (p < 0.0001) when using either of the two SIRS criteria. Moreover, KC-like was slightly increased in dogs with pyometra without sepsis compared with healthy controls when using the more stringent SIRS criteria (p < 0.05). Analyses of all dogs showed that KC-like concentrations correlated positively with hospitalization days, C-reactive protein (CRP) concentrations, white blood cells, and percentage of band neutrophils; however, KC-like correlated negatively with hemoglobin and did not correlate with circulating creatinine.ConclusionsOur results suggest that circulating KC-like protein increases in dogs with sepsis in pyometra and that KC-like is associated with more severe clinical illness. These findings support a potential role of KC-like as a biomarker of sepsis; however, the true identity of KC-like in dogs has yet to be uncovered.