Systemic Inflammatory Response Syndrome Patients Research Articles

Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records.

Systemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully elucidated for each population as well as the possible differences between the two. The aim of this study is to highlight the differences between the prognostic clinical features for the survival of patients diagnosed with SIRS and those of patients diagnosed with sepsis by using a multi-variable predictive modeling approach with a reduced set of easily available measurements collected at the admission to the intensive care unit (ICU). Data were collected from 1,257 patients (816 non-sepsis SIRS and 441 sepsis) admitted to the ICU. We compared the performance of five machine learning models in predicting patient survival. Matthews correlation coefficient (MCC) was used to evaluate model performances and feature importance, and by applying Monte Carlo stratified Cross-Validation. Extreme Gradient Boosting (MCC = 0.489) and Logistic Regression (MCC = 0.533) achieved the highest results for SIRS and sepsis cohorts, respectively. In order of importance, APACHE II, mean platelet volume (MPV), eosinophil counts (EoC), and C-reactive protein (CRP) showed higher importance for predicting sepsis patient survival, whereas, SOFA, APACHE II, platelet counts (PLTC), and CRP obtained higher importance in the SIRS cohort. By using complete blood count parameters as predictors of ICU patient survival, machine learning models can accurately predict the survival of SIRS and sepsis ICU patients. Interestingly, feature importance highlights the role of CRP and APACHE II in both SIRS and sepsis populations. In addition, MPV and EoC are shown to be important features for the sepsis population only, whereas SOFA and PLTC have higher importance for SIRS patients.

Risk of Systemic Inflammatory Response Syndrome Following Preoperative Glucocorticoids Administration in Patients After Percutaneous Nephrolithotomy: A Retrospective Cohort Study.

Systemic inflammatory response syndrome (SIRS) is one of the most serious complications in patients undergoing percutaneous nephrolithotomy (PCNL). Although glucocorticoids are increasingly used during PCNL, few studies have been concerned about the association between glucocorticoids and postoperative SIRS. The study aims to explore whether preoperative use of glucocorticoids is associated with SIRS after PCNL. A total of 1259 patients who underwent PCNL between January 2015 and April 2021 were enrolled in the retrospective cohort study. Risk factors for post-PCNL SIRS were identified by univariate and multivariate regression analysis. To further explore the association between preoperative administration of glucocorticoids and SIRS, 113 pairs of patients were matched for the confounding factors using propensity score matching (PSM) analysis. The odds ratios (OR) and 95 % confidence intervals (CI) for the above variables were analyzed. The incidence of SIRS after PCNL was 9.6 % (121/1259) and the patients who suffered from postoperative SIRS had longer hospital stays and higher hospital costs (all p < 0.05). Multivariate logistic regression analysis indicated that female, preoperative leukocyte count, insertion of central vein catheter, serum albumin, preoperative high-sensitive C-reactive protein/albumin ratio, preoperative transfusion, preoperative administration of glucocorticoids were independent risk factors for SIRS (all p < 0.05). After minimization, the effects of confounding factors by PSM, preoperative administration of glucocorticoids was significantly correlated with SIRS in patients after PCNL (OR=2.44, 95 %CI: 1.31-4.55, p = 0.005). Preoperative administration of glucocorticoids is an independent risk factor for SIRS in patients undergoing PCNL.

Management of Systemic Inflammatory Response Syndrome (SIRS) and Evaluation of Survival Rate in Cats Exposed to Trauma

Background: Systemic inflammatory response syndrome (SIRS) is a complex condition that may be associated with sepsis or may progress with trauma, burns, and major surgery. The aim of this study was to evaluate the systemic inflammatory response syndrome and survival rate in traumatized cats. Methods: The study material consisted of 34(22.66%) cats diagnosed with SIRS among 150 cats who were traumatized by falling from a height. Cats were monitored and vital signs such as pulse, respiration, arterial blood pressure, oxygen saturation and body temperature were recorded. In addition, laboratory whole blood and biochemical tests were performed. In the treatment of cats with SIRS, it was aimed to provide circulatory support and support measures to keep the animal alive. Result: 27(79.41%) cats with SIRS died due to lung failure or multi-organ failure and 7(20.58%) cats survived. The prognosis in SIRS patients with trauma depends on the patient’s response to aggressive treatment and supportive care. SIRS has a high mortality rate. Common causes of death include cardiovascular collapse, multi-organ dysfunction and acute lung injury.

Elevated Delta Neutrophil Index (DNI) as a Predictor of Systemic Inflammatory Response Syndrome (SIRS) In Patients with Generalized Peritonitis at Prof Dr. I.G.N.G. Ngoerah Hospital, Denpasar

Background: Generalized peritonitis is an emergency case in the field of surgery. Which includes an inflammatory process in the peritoneum. Systemic inflammatory response syndrome (SIRS) is a defense response to a stressor. Delta Neutrophil Index (DNI) is a parameter that reflects the ratio of infection and inflammation. This study examines the role of DNI as a predictor of SIRS in generalized peritonitis patients at Prof. Dr. IGNG Ngoerah General Hospital. Method: Cross-sectional analytic observational research. Data collection was from July 2022 to October 2022. Patients diagnosed with generalized peritonitis were taken for blood samples. The DNI examination was carried out at the Surya Husadha Hospital Laboratory. Samples were excluded if they suffered from immunodeficiency diseases, autoimmune diseases and refused to participate. The DNI value is determined by the cut-off point on the ROC curve. Bivariate analysis using Chi square test and multivariate analysis using logistic regression test. The p value &lt;0.05 indicates a significant result. Result: This study involved 55 respondents with 41 positive SIRS respondents and 14 negative SIRS respondents. ROC curve analysis showed the DNI value with a cut-off point of 8.9 (sensitivity 85.4% and specificity 85.7%). Bivariate analysis of the DNI 8.9 with the incidence of SIRS obtained RR 3 (p = 0.000; 95% C1 1.11- 6.90). Multivariate analysis of DNI values with the incidence of SIRS showed a significant relationship (p = 0.000). Conclusion: DNI value of 8.9 is an excellent predictor of SIRS in patients with generalized peritonitis.

Exploring the Relationship between Plasma Adiponectin, Gender, and Underlying Diseases in Severe Illness.

Adiponectin is low in obesity, plays a crucial role in metabolic health, and, moreover, possesses immunoregulatory properties. However, studies examining its levels in patients with systemic inflammatory response syndrome (SIRS) or sepsis have yielded conflicting results. While females typically have higher systemic adiponectin levels than males, research on sex-specific associations in this context is limited. In this study of 156 SIRS/sepsis patients, including those with liver cirrhosis, we aimed to explore the relationship between plasma adiponectin, body mass index (BMI), gender, disease severity, and underlying etiological conditions. Our findings revealed that patients with liver cirrhosis, who are susceptible to infections, exhibited elevated circulating adiponectin levels, irrespective of sex. When excluding cirrhosis patients, plasma adiponectin levels were similar between male SIRS/sepsis patients and controls but lower in female patients compared to female controls. Plasma adiponectin was inversely related to BMI in female but not male patients. Further analysis within the non-cirrhosis subgroup demonstrated no significant differences in adiponectin levels between sexes among SIRS, sepsis, and septic shock patients. Ventilation, dialysis, and vasopressor therapy had no discernible impact on adiponectin levels in either sex. A negative correlation between adiponectin and C-reactive protein (CRP) existed in males only. Notably, patients with pancreatitis showed the lowest plasma adiponectin concentrations, although sex-specific differences were not significant. Infection with Gram-negative or Gram-positive bacteria had minimal effects on plasma adiponectin levels in both sexes. However, infection with the severe acute respiratory syndrome coronavirus type 2 led to decreased adiponectin levels in females exclusively. Multivariate analysis considering all factors affecting plasma adiponectin levels in males or females identified BMI in females and CRP levels in males to predict plasma adiponectin levels in SIRS/sepsis patients. Additionally, our study observed a trend where the 25 patients who did not survive had higher plasma adiponectin levels, particularly among males. In summary, our investigation highlights the influence of underlying diseases and sex on plasma adiponectin levels in SIRS/sepsis patients, shedding light on potential implications for disease management and prognosis.

A predictive model for the classification of emergency intensive care unit patients with systemic inflammatory response syndrome based on a similarity network fusion algorithm

BackgroundIntensive care unit-acquired weakness (ICU-AW) is a prevalent and severe neuromuscular complication in critically ill patients. It is a consequence of critical illness and is characterized by systemic inflammatory response syndrome (SIRS)-induced metabolic stress and multiple organ dysfunctions. Moreover, ICU-AW is one of the most important factors affecting the prognosis of patients with SIRS, Electrophysiological examination is an effective method for early identification and monitoring of the course of the disease and is essential for accurate diagnosis of critical illness neuromyopathy (CINM). The data-intensive ICU environment is ideal for implementing the similarity network fusion (SNF) method. The objective of this study was to establish and validate a ICU-AW predictive model in SIRS patients, providing a practical tool for early clinical prediction. MethodsClinical characteristics, demographic data, longitudinal neurophysiological data, and disease severity indicators of the enrolled patients were recorded. The patient data included nerve conduction, F-wave, and direct muscle stimulation (DMS) data from 94 follow-up visits as well as various scores, including Medical Research Council (MRC), sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II) scores and C-reactive protein (CRP). This algorithm was used to analyze electrophysiological data of emergency intensive care unit (EICU) patients with SIRS and fully exploit their similarities in age, sex, body mass index, and electrophysiological data by fusing the similarity networks of these patients with different sets of attributes. Existing patients was performed a clustering analysis and predicted the classification of new patients using spectral clustering and label propagation algorithms on the fusion network, respectively. ResultsClassification prediction model categorical of ICUAW in Patients with SIRS was highly consistent with the clinical diagnosis and had high accuracy and discriminative ability. The model captures the importance of advanced age and lung infections as risk factors for ICU-AW and also demonstrates the significant prognostic value of DMS in EICU patients with SIRS and its ability to predict the development of clinical muscle weakness. ConclusionsElectrophysiological abnormalities are a critical feature of both ICU-AW and non-ICU-AW. Modeling the prediction of SIRS patients progressing to ICUAW which is conducive to early intervention, mechanism studies, and patient rehabilitation.

Systemic Inflammatory Response Syndrome and Prosthetic Joint Infection

BackgroundThe development of systemic inflammatory response syndrome (SIRS) criteria leads to increased mortality. Little is known about development of SIRS in patients who have prosthetic joint infection (PJI). We aimed to determine the incidence, risk factors, clinical outcomes, and causative organisms in patients who develop SIRS with PJI. MethodsWe retrospectively identified 655 patients (321 men, 334 women; 382 total hip, 273 total knee) who have hip or knee PJI at 1 institution between July 1, 2015 and December 31, 2020. We formed 2 groups: patients who have SIRS alert (PJI + SIRS) and patients who do not have SIRS alert (PJI). We analyzed clinical outcomes, comorbidities, and operating room culture results. ResultsOf 655 patients, 63 developed SIRS with PJI (9.6%). Intensive care unit (ICU) admission rates (27.0 versus. 6.9%, P < .001) and length of stay (7.7 versus. 5.6 days, P = .003) were greater in PJI + SIRS. At 2 years, reoperation (36.5 versus. 22.3%, P = .01) and mortality rates (17.5 versus. 8.8%, P = .03) were greater in PJI + SIRS. Risk factors included deficiency anemia (P = .001), blood loss anemia (P = .013), uncomplicated diabetes (P = .006), diabetes with complication (P = .001), electrolyte disorder (P < .00001), neurological disorder (P = .0001), paralysis (P = .026), renal failure (P = .005), and peptic ulcer disease (P = .004). Staphylococcus aureus more commonly speciated on tissue cultures in PJI + SIRS (P = .002). ConclusionThe incidence of SIRS is 10% among patients who have PJI. Development of PJI + SIRS is associated with increased lengths of stay, ICU admissions, and 2-year reoperation and mortality rates. Identifying certain comorbidities can stratify patients' risk of developing PJI + SIRS.

Early Identification of Serum Biomarkers and Pathways of Sepsis Through GC-MS-Based Metabolomics Analysis.

Early identification of sepsis improves the survival rate; however, it is one of the most challenging tasks for physicians, especially since symptoms are easily confused with those of systemic inflammatory response syndrome (SIRS). Our aim was to explore biomarkers for early identification of sepsis that would aid in its differential diagnosis. Eight patients with SIRS, eight with sepsis, and eight healthy controls were included in this study. Metabolites were screened using gas chromatography-mass spectrometry (GC-MS). Metabolism profiles were analyzed using the untargeted database of GC-MS from Lumingbio (LUG) database, and metabolic pathways were enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The S-plot was used to screen the potential biomarkers distinguishing between patients with SIRS, sepsis, and healthy controls. Receiver operating characteristic (ROC) curve analysis was used to evaluate potential biomarkers between SIRS and sepsis patients. Correlation analysis was used to measure the degree of correlation between differential metabolites. Correlation analysis between 2-deoxy-d-erythro-pentofuranose-5-phosphate and clinical indicators was performed. There were 51 metabolites that were distributed in the SIRS group, and they were enriched with 18 metabolic pathways compared with healthy controls. Moreover, 63 metabolites in the sepsis group were significantly distinguishable compared to the healthy controls, and were associated with 21 metabolic pathways. Methyl 3-o-acetyl-d-galactopyranoside and N-acetylputrescine were found to be candidate biomarkers for distinguishing between SIRS, sepsis, and healthy controls using the S-plot model. Only four differential metabolites, including 2-deoxy-d-erythro-pentofuranose-5-phosphate, terbutaline, allantoic acid, and homovanillic acid (HVA), were enriched in the dopaminergic synapse and tyrosine metabolism pathways when sepsis patients were compared with SIRS patients. The Area Under Curve (AUC) of 2-deoxy-d-erythro-pentofuranose-5-phosphate was 0.9297, indicating a strong diagnostic ability for sepsis. A significant negative correlation was identified between 2-deoxy-d-erythro-pentofuranose-5-phosphate and lactate (r = -0.8756, p = 0.0044). Methyl 3-o-acetyl-d-galactopyranoside and N-acetylputrescine may be used as candidate biomarkers to distinguish SIRS and sepsis patients from healthy controls using GC-MS. 2-deoxy-d-erythro-pentofuranose-5-phosphate may be the candidate biomarker to distinguish sepsis from SIRS. Our study explored candidate biomarkers for the early identification of sepsis, which is vital for improving its prognosis.

Systemic Inflammatory Response Syndrome After Extracorporeal Membrane Oxygenation Decannulation in COVID-19 Patients.

Systemic inflammatory response syndrome (SIRS) is frequently observed following decannulation from extracorporeal membrane oxygenation (ECMO). Differentiating cytokine release due to infection from endothelial injury from cannula removal and/or discontinuation from the ECMO circuit has been shown to impact treatment and outcomes. This response, however, may be complicated in COVID-19 patients due to prevalent glucocorticoid and immune modulator use. It remains unclear whether COVID-19 infection and/or associated immune modulator use impact the incidence of SIRS following decannulation. The aim of this study is to investigate the incidence of the SIRS phenomenon and associated outcomes in patients with COVID-19 after ECMO decannulation. An IRB-approved retrospective chart review of all patients who survived ECMO between June 31, 2010 and July 7, 2021 was done to identify patients who experienced SIRS within 48 hours of decannulation from ECMO support. Patients with COVID-19 were confirmed by a positive reverse transcription polymerase chain reaction (RT-PCR) assay for SARS-CoV2. SIRS was confirmed when two out of three of the following criteria were met: fever, leukocytosis, and/or initiation/escalation of vasopressors. Patients who developed post-ECMO SIRS were then distinguished based on the presence of infection. Infection was defined by the presence of either a new or positive culture following decannulation. We compared the incidence of SIRS and infection within 48 hours of decannulation in patients with and without COVID-19. We identified 227 eligible patients who survived ECMO. Twenty-eight patients (12%) had COVID-19. Of these patients, ten patients with COVID-19 (36%) experienced post-ECMO SIRS, including those with true SIRS (n=3) and associated infections (n=7). Five of the ten patients with COVID-19 who experienced post-ECMO SIRS were exposed to immune modulators within two weeks of decannulation. Ninety-five (42%) patients without COVID-19 developed post-ECMO SIRS. Thirty-day survival in COVID patients who experienced post-ECMO SIRS compared to COVID patients who did not experience post-ECMO SIRS was 73% vs. 94%. (p=0.11). Post-ECMO SIRS is common. The incidence of SIRS following decannulation was similar when historically compared to non-COVID patients who survived ECMO in a previously reported cohort from our institution. Immune-modulation exposure within two weeks of decannulation did not affect the incidence of SIRS in patients with COVID-19.

Analysis of risk factors for systemic inflammatory response syndrome in patients after transcatheter aortic valve replacement.

Our aim was to determine the risk factors of postoperative systemic inflammatory response syndrome (SIRS) in patients with transcatheter aortic valve replacement (TAVR), identify those with ahigh risk of SIRS, and help reduce SIRS occurrence. Aretrospective cohort study was conducted to collect the clinical data of patients who underwent TAVR from January 2014 to December 2019 atatertiary hospital in Zhejiang Province. The study included 156 men and 94women. Patients were divided into SIRS and non-SIRS groups. The pre-, intra-, and postoperative indices of the two groups were recorded. The data of the two groups were compared, and univariate analysis was performed. All statistically significant factors were assessed using binary logistic regression analysis to clarify the risk factors of SIRS after TAVR. Overall, 30 patients developed SIRS after TAVR, with an incidence rate of 12%, an odds ratio (OR) of 0.571, and a95% confidence interval (CI) of 0.469-0.694 (p = 0.000). There was asignificant correlation between SIRS and glucose (OR: 0.823, 95%CI: 0.678-1.000, p = 0.049), albumin (OR: 0.938, 95%CI: 0.881-0.998, p = 0.044), brain natriuretic peptide (OR: 1.000, 95%CI: 1.000-1.000, p = 0.010), sex (OR: 0.412, 95%CI: 0.190-0.892, p = 0.025), and history of hypertension (OR: 0.375, 95%CI: 0.169-0.819, p = 0.014). Multivariate regression analysis demonstrated that age (OR: 1.190, 95%CI: 1.073-1.319, p = 0.001) and body mass index (BMI; OR: 0.559, 95%CI: 0.447-0.698, p = 0.000) were independent risk factors for postoperative SIRS in patients with TAVR. The incidence of SIRS after TAVR was 12%. There was asignificant correlation between SIRS and albumin, glucose, and hypertension. The independent risk factors for SIRS after TAVR were age and BMI.

Systemic Metabolomic Profiles in Adult Patients with Bacterial Sepsis: Characterization of Patient Heterogeneity at the Time of Diagnosis.

Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas the remaining 25 patients only fulfilled the previous Sepsis-2 criteria and could therefore be classified as having systemic inflammatory response syndrome (SIRS). A total of 1011 identified metabolites were detected in our serum samples. Ninety-seven metabolites differed significantly when comparing Sepsis-3 and Sepsis-2/SIRS patients; 40 of these metabolites constituted a heterogeneous group of amino acid metabolites/peptides. When comparing patients with and without bacteremia, we identified 51 metabolites that differed significantly, including 16 lipid metabolites and 11 amino acid metabolites. Furthermore, 42 metabolites showed a highly significant association with the maximal total Sequential Organ Failure Assessment (SOFA )score during the course of the disease (i.e., Pearson's correlation test, p-value < 0.005, and correlation factor > 0.6); these top-ranked metabolites included 23 amino acid metabolites and a subset of pregnenolone/progestin metabolites. Unsupervised hierarchical clustering analyses based on all 42 top-ranked SOFA correlated metabolites or the subset of 23 top-ranked amino acid metabolites showed that most Sepsis-3 patients differed from Sepsis-2/SIRS patients in their systemic metabolic profile at the time of hospital admission. However, a minority of Sepsis-3 patients showed similarities with the Sepsis-2/SIRS metabolic profile even though several of them showed a high total SOFA score. To conclude, Sepsis-3 patients are heterogeneous with regard to their metabolic profile at the time of hospitalization.

Patients with Bacterial Sepsis Are Heterogeneous with Regard to Their Systemic Lipidomic Profiles.

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. In the present study, we investigated the systemic/serum lipidomic profile at the time of hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria (referred to as Sepsis-3) whereas the remaining 25 patients had sepsis only according to the previous Sepsis-2 definition and could be classified as having Systemic Inflammatory Response Syndrome (SIRS). A total of 966 lipid metabolites were identified. Patients fulfilling the Sepsis-3 criteria differed from the Sepsis-2 patients with regard to only 15 lipid metabolites, and especially sphingolipids metabolism differed between these patient subsets. A total of only 43 metabolites differed between patients with and without bacteremia, including 12 lysophosphatidylcholines and 18 triacylglycerols (15 C18/C20 fatty acid metabolites decreased and three C14 myristate acid metabolites that were increased in bacteremia). Unsupervised hierarchical clustering analyses based on the identified sphingolipids, phosphatidylcholine and triacylglycerols showed that (i) the majority of Sepsis-3 patients differed from SIRS patients especially with regard to lysophosphatidylcholine levels; (ii) the minority of Sepsis-3 patients that clustered together with the majority of SIRS patients showed lower Sequential Organ Failure Assessment (SOFA) scores than the other Sepsis-3 patients; and (iii) the variation between the patients in the identified/altered sphingolipid and triacylglycerol metabolites further increased the heterogeneity of Sepsis-3 patients with regard to their systemic lipidomic profile at the time of diagnosis. To conclude, patients fulfilling the Sepsis-3 criteria differ with regard to their metabolic profile, and this variation depends on disease severity.

Acute Endotoxemia-Induced Respiratory and Intestinal Dysbiosis.

Systemic inflammatory response syndrome (SIRS) is a severe condition characterized by systemic inflammation, which may lead to multiple organ failure, shock and death. SIRS is common in burn patients, pancreatitis and sepsis. SIRS is often accompanied by intestinal dysbiosis. However, the mechanism, role and details of microbiome alterations during the early phase of acute SIRS are not completely understood. The current study aimed to characterize the dynamic alterations of both the intestinal and respiratory microbiome at two timepoints during the early phase of acute SIRS (4 and 8 h after LPS) and link these to the host response in a mouse model of a LPS-induced lethal SIRS. Acute SIRS had no effect on the microbiome in the large intestine but induced a rapid dysbiosis in the small intestine, which resembled the microbiome alterations commonly observed in SIRS patients. Later in the disease progression, a dysbiosis of the respiratory microbiome was observed, which was associated with the MMP9 expression in the lungs. Although similar bacteria were increased in both the lung and the small intestine, no evidence for a gut-lung translocation was observed. Gut dysbiosis is commonly observed in diseases involving inflammation in the gut. However, whether the inflammatory response associated with SIRS and sepsis can directly cause gut dysbiosis was still unclear. In the current study we provide evidence that a LPS-induced SIRS can directly cause dysbiosis of the small intestinal and respiratory microbiome.

Correlation of Immature Granulocytes and C-Reactive Protein with Blood Culture in Neonatal-SIRS at Gambiran Hospital

Sepsis can occur in patients with Systemic Inflammatory Response Syndrome (SIRS) symptoms, especially in neonates due to their underdeveloped immune systems. Blood culture as the gold standard test to diagnose sepsis in SIRS patients takes a long time to perform. The other rapid test parameters are needed to support the early diagnosis of infection (sepsis) in SIRS patients, including Immature Granulocytes (IG%) and C-Reactive Protein (CRP). This study aimed to determine the correlation between IG% with blood culture and CRP with blood culture in neonatal SIRS patients at Gambiran Hospital, Kediri. The research design used retrospectively using secondary data. A total of 63 samples were obtained using simple random sampling on the medical record of neonatal SIRS patients who met the inclusion criteria, without any exclusion criteria. There were significant results with a value of p=0.000 (p &lt; 0.05) between IG% and blood culture using the Mann-Whitney test, as well as on CRP and blood culture using the independent samples T-test. The bivariate statistical test between IG% and CRP was carried out using the Spearman test and showed significant results with p=0.000 (p &lt; 0.05) with a correlation coefficient (rs) of 0.740, which indicated a strong positive relationship. It was concluded that there was a correlation between IG% and CRP with blood culture in neonatal SIRS patients.

CT Characteristics of Acute Pancreatitis with Preexisting Fatty Liver and Its Impact on Pancreatitis Severity and Persistent Systemic Inflammatory Response Syndrome.

PurposeTo study the CT characteristics of acute pancreatitis (AP) associated with preexisting fatty liver (FL) and the impact of preexisting FL on the severity of AP and persistent systemic inflammatory response syndrome (SIRS).Patients and MethodsA total of 189 patients with AP were divided into AP with and without preexisting FL. The CT features, clinical characteristics, severity of AP, and presence of persistent SIRS between the two groups were compared. Univariate and multivariate analyses were performed to determine the risk factors for predicting SIRS. The diagnostic performances of the risk factors were evaluated by receiver operating characteristic (ROC) curve analysis.ResultsAmong the 189 patients, 49.7% (94/189) had preexisting FL. On CT, AP patients with preexisting FL were more likely to develop necrosis (23.4% vs 10.5%, p=0.021), local complications (45.7% vs 29.5%, p=0.025) and persistent SIRS (59.6% vs 27.4%, p<0.001). Multivariate analysis showed that preexisting FL (OR=2.863, 95% CI: 1.264–6.486, p=0.012), APACHE II≥6 (OR=1.334, 95% CI: 1.117–1.594, p=0.002), and MCTSI ≥4 (OR=1.489, 95% CI: 1.046–2.119, p=0.027) could be independent risk factors for persistent SIRS. The areas under the ROC curve of preexisting FL, APACHE II, and MCISI in diagnosing AP patients with persistent SIRS were 0.664, 0.703, and 0.783, respectively.ConclusionPatients with preexisting FL were more likely to develop necrosis and local complications on CT and present more severe AP and persistent SIRS. Preexisting FL can be an independent risk factor in predicting the presence of persistent SIRS in patients with AP.

Abstract P3121: Serological Mechanisms Driving Cardiotoxicity In Covid-19

Cardiac involvement is common in patients severely ill with COVID-19 and is associated with increased morbidity and mortality. The mechanism for myocardial injury in COVID-19 has been proposed to be at least in part due to a direct viral cytotoxic effect on cardiomyocytes. Preclinical data corroborate the notion that SARSCoV-2 can enter and replicate within hiPSC-derived cardiomyocytes. However, clinical evidence demonstrating direct viral effects on the heart remains limited. Alternatively, cardiac injury may result from excessive inflammatory cytokine production which is the main driver for the pathogenesis in COVID-19. Despite this, cardiotoxicity secondary to hyperinflammation constitutes a potentially amenable mechanism that remains largely unexplored. To investigate serological drivers of cardiotoxicity in COVID-19 we have established a bioassay that assessed the effects of serum from COVID-19 confirmed patients on human embryonic stem cell-derived cardiomyocytes (hESC-CMs). We demonstrate that serum from COVID-19 positive patients significantly reduced cardiomyocyte viability and contractility independent of viral transduction. Serum from patients with greater disease severity led to worse cardiomyocyte viability and this significantly correlated with levels of key inflammatory cytokines, including IL-6, TNF-α, IL1-β, IL-10, CRP and neutrophil to lymphocyte ratio with a specific reduction of CD4+ and CD8+ cells. Combinatorial blockade of IL-6 and TNF-α partially reversed the phenotype and preserved cardiomyocyte viability and function. Interestingly, treatment with serum from Systemic Inflammatory Response Syndrome (SIRS) patients recapitulates the effect observed with COVID-19 patients’ serum indicating that the phenotype observed is most likely not unique to COVID-19 disease. In conclusion, our results provide direct evidence that inflammatory cytokines are at least in part responsible for the cardiovascular damage seen in COVID-19 and indicate possible targets for therapeutic intervention.

FYN and CD247: Key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis.

Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. FYN and CD247 are expected to become new biomarkers of septic shock.

SIRS tanılı hastalarda enflamasyon ve kemik döngüsü arasındaki ilişkinin sklerostin ve Dickkopf-1 (DKK-1) düzeyleri ile değerlendirilmesi

Introduction: In intensive care units (ICU), patients remain bedridden for a long time. In addition, severe infections are frequently seen in ICUs. Both prolonged immobilization and serious infections are associated with bone tissue loss. The Wnt pathway has recently been focused on evaluating bone tissue loss. The Wnt pathway participates in both infections and the formation of bone tissue. Wnt pathway inhibitors sclerostin and Dickkopf-1 (DKK-1) inhibit bone formation and increase osteoclastic activity. In this study, we aimed to examine bone turnover by the Wnt inhibitors sclerostin and DKK-1 and their possible associations with inflammation in SIRS patients.Methods: We included 30 patients diagnosed with systemic inflammatory response syndrome (SIRS) in the study group and 16 in the control group. Serum sclerostin, DKK-1, white blood cell (WBC), and C-Reactive Protein (CRP) levels on the day of SIRS diagnosis (basal), the 7th, 14th, and 21stdays were evaluated in the study group, and the results were compared with the control group.Results: When the control group was compared with the basal SIRS, there was a significant elevation in both sclerostin (p=0.003) and DKK-1 (p=0.001). Statistical analysis showed significant decreases in sclerostin levels between basal and the 7th, 14th, and 21st days (p=0.033, p=0.003, p=0.002, respectively). Similarly, significant decreases in DKK-1 levels between basal and the 7th and 21st days (p=0.015, p=0.001, respectively) and an insignificant decrease on the 14th day (p=0.191) was observed. Sclerostin was positively and significantly correlated with WBC and CRP in basal and 7th-day measurements and WBC in 7th and 14th days. DKK-1 is positively and significantly correlated with WBC in basal and 7th-daymeasurements, while DKK-1 negatively correlates with CRP in basal-7th-day measurements.Conclusion: In this study, it was shown for the first time that the Wnt antagonists sclerostin and DKK-1 values are high in SIRS patients in ICU. Both biomarker levels decreased in parallel with the treatment. However, it could not be associated with disease severity and inflammatory marker levels. We believe that monitoring the change of Wnt antagonists will be useful in demonstrating bone turnover in patients with SIRS.Keywords: Dickkopf-1, Intensive care unit, Sclerostin, Systemic inflammatory response syndrome, Wnt signaling pathway, Bone turnover

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

ABSTRACTObjective: 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.

Key Signature Genes of Early Terminal Granulocytic Differentiation Distinguish Sepsis From Systemic Inflammatory Response Syndrome on Intensive Care Unit Admission.

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15+ cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.