Virological characteristics of the SARS-CoV-2 Omicron BA.2 spike.

Soon after the emergence and global spread of the SARS-CoV-2 Omicron lineage BA.1, another Omicron lineage, BA.2, began outcompeting BA.1. The results of statistical analysis showed that the effective reproduction number of BA.2 is 1.4-fold higher than that of BA.1. Neutralization experiments revealed that immunity induced by COVID vaccines widely administered to human populations is not effective against BA.2, similar to BA.1, and that the antigenicity of BA.2 is notably different from that of BA.1. Cell culture experiments showed that the BA.2 spike confers higher replication efficacy in human nasal epithelial cells and is more efficient in mediating syncytia formation than the BA.1 spike. Furthermore, infection experiments using hamsters indicated that the BA.2 spike-bearing virus is more pathogenic than the BA.1 spike-bearing virus. Altogether, the results of our multiscale investigations suggest that the risk of BA.2 to global health is potentially higher than that of BA.1.

Elevated Myl9 reflects the Myl9-containing microthrombi in SARS-CoV-2-induced lung exudative vasculitis and predicts COVID-19 severity.

The mortality of coronavirus disease 2019 (COVID-19) is strongly correlated with pulmonary vascular pathology accompanied by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-triggered immune dysregulation and aberrant activation of platelets. We combined histological analyses using field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy analyses of the lungs from autopsy samples and single-cell RNA sequencing of peripheral blood mononuclear cells to investigate the pathogenesis of vasculitis and immunothrombosis in COVID-19. We found that SARS-CoV-2 accumulated in the pulmonary vessels, causing exudative vasculitis accompanied by the emergence of thrombospondin-1-expressing noncanonical monocytes and the formation of myosin light chain 9 (Myl9)-containing microthrombi in the lung of COVID-19 patients with fatal disease. The amount of plasma Myl9 in COVID-19 was correlated with the clinical severity, and measuring plasma Myl9 together with other markers allowed us to predict the severity of the disease more accurately. This study provides detailed insight into the pathogenesis of vasculitis and immunothrombosis, which may lead to optimal medical treatment for COVID-19.

Suppression of Type I Interferon Signaling in Myeloid Cells by Autoantibodies in Severe COVID-19 Patients.

PURPOSE: Auto-antibodies (auto-abs) to type I interferons (IFNs) have been identified in patients with life-threatening coronavirus disease 2019 (COVID-19), suggesting that the presence of auto-abs may be a risk factor for disease severity. We therefore investigated the mechanism underlying COVID-19 exacerbation induced by auto-abs to type I IFNs. METHODS: We evaluated plasma from 123 patients with COVID-19 to measure auto-abs to type I IFNs. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from the patients with auto-abs and conducted epitope mapping of the auto-abs. RESULTS: Three of 19 severe and 4 of 42 critical COVID-19 patients had neutralizing auto-abs to type I IFNs. Patients with auto-abs to type I IFNs showed no characteristic clinical features. scRNA-seq from 38 patients with COVID-19 revealed that IFN signaling in conventional dendritic cells and canonical monocytes was attenuated, and SARS-CoV-2-specific BCR repertoires were decreased in patients with auto-abs. Furthermore, auto-abs to IFN-α2 from COVID-19 patients with auto-abs recognized characteristic epitopes of IFN-α2, which binds to the receptor. CONCLUSION: Auto-abs to type I IFN found in COVID-19 patients inhibited IFN signaling in dendritic cells and monocytes by blocking the binding of type I IFN to its receptor. The failure to properly induce production of an antibody to SARS-CoV-2 may be a causative factor of COVID-19 severity.

Early Elevation of Cell-Free DNA After Acute Mesenteric Ischemia in Rats.

BACKGROUND: Acute mesenteric ischemia (AMI) is challenging to diagnose in the early phase. We tested the hypothesis that blood levels of cell-free DNA would increase early after AMI. In addition, proteome analysis was conducted as an exploratory analysis to identify other potential diagnostic biomarkers. METHODS: Mesenteric ischemia, abdominal sepsis, and sham model were compared in Sprague-Dawley rats. The abdominal sepsis model was induced by cecum puncture and mesenteric ischemia model by ligation of the superior mesenteric artery. Blood levels of cell-free DNA were measured 2 h and 6 h after wound closure. Shotgun proteome analysis was performed using plasma samples obtained at the 2 h timepoint; quantitative analysis was conducted for proteins detected exclusively in the AMI models. RESULTS: Blood cell-free DNA levels at 2 h after wound closure were significantly higher in the AMI model than in the sham and the abdominal sepsis models (P < 0.05). Cell-free DNA was positively correlated with the pathologic ischemia severity score (correlation coefficient 0.793-0.834, P < 0.001). Derivative proteome analysis in blood at 2-h time point revealed higher intensity of paraoxonase-1 in the AMI models than in the abdominal sepsis models; the significantly high blood paraoxonase-1 levels in the AMI models were confirmed in a separate quantitative analysis (P = 0.015). CONCLUSIONS: Cell-free DNA was demonstrated to be a promising biomarker for the early diagnosis of mesenteric ischemia in a rat model of AMI. Paraoxonase-1 may also play a role in the differential diagnosis of mesenteric ischemia from abdominal sepsis. The current results warrant further investigation in human studies.

Very Low Density Lipoprotein Receptor Sequesters Lipopolysaccharide Into Adipose Tissue During Sepsis.

OBJECTIVES: Obese patients have lower sepsis mortality termed the "obesity paradox." We hypothesized that lipopolysaccharide, known to be carried within lipoproteins such as very low density lipoprotein, could be sequestered in adipose tissue during sepsis; potentially contributing a survival benefit. DESIGN: Retrospective analysis. SETTING: University research laboratory. SUBJECTS AND PATIENTS: Vldlr knockout mice to decrease very low density lipoprotein receptors, Pcsk9 knockout mice to increase very low density lipoprotein receptor, and Ldlr knockout mice to decrease low density lipoprotein receptors. Differentiated 3T3-L1 adipocytes. Caucasian septic shock patients. INTERVENTIONS: We measured lipopolysaccharide uptake into adipose tissue 6 hours after injection of fluorescent lipopolysaccharide into mice. Lipopolysaccharide uptake and very low density lipoprotein receptor protein expression were measured in adipocytes. To determine relevance to humans, we genotyped the VLDLR rs7852409 G/C single-nucleotide polymorphism in 519 patients and examined the association of 28-day survival with genotype. MEASUREMENTS AND MAIN RESULTS: Lipopolysaccharide injected into mice was found in adipose tissue within 6 hours and was dependent on very low density lipoprotein receptor but not low density lipoprotein receptors. In an adipocyte cell line decreased very low density lipoprotein receptor expression resulted in decreased lipopolysaccharide uptake. In septic shock patients, the minor C allele of VLDLR rs7852409 was associated with increased survival (p = 0.010). Previously published data indicate that the C allele is a gain-of-function variant of VLDLR which may increase sequestration of very low density lipoprotein (and lipopolysaccharide within very low density lipoprotein) into adipose tissue. When body mass index less than 25 this survival effect was accentuated and when body mass index greater than or equal to 25 this effect was diminished suggesting that the effect of variation in very low density lipoprotein receptor function is overwhelmed when copious adipose tissue is present. CONCLUSIONS: Lipopolysaccharide may be sequestered in adipose tissue via the very low density lipoprotein receptor and this sequestration may contribute to improved sepsis survival.

Associations between fluid overload and outcomes in critically ill patients with acute kidney injury: a retrospective observational study.

Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear. We retrospectively screened intensive care unit (ICU) admitted patients with AKI between January 2011 and December 2015. Logistic or linear regression analyses were performed to determine when hourly %FO was significant on 90-day in-hospital mortality (primary outcome) or ventilator-free days (VFDs). In total, 1120 patients were enrolled in this study. Univariate analysis showed that a higher %FO was significantly associated with higher mortality from the first hour of ICU admission (odds ratio 1.34, 95% confidence interval 1.15-1.56, P < 0.001), whereas multivariate analysis adjusted with age, sex, APACHE II score, and sepsis etiology showed the association was significant from the 27th hour. Both univariate and multivariate analyses showed that a higher %FO was significantly associated with shorter VFDs from the 1st hour. The significant associations were retained during all following observation periods after they showed significance. In patients with AKI, a higher %FO was associated with higher mortality and shorter VFDs from the early phase after ICU admission. FO should be administered with a physiological target or goal in place from the initial phase of critical illness.

Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest.

Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population-based repository database of OHCA and weather information. The Tokyo data (2005-2012) was used as the training cohort and datasets of the top six populated prefectures (2013-2015) as the test. Eight various algorithms were evaluated to predict the high-incidence OHCA days, defined as the daily events exceeding 75% tile of our dataset, using meteorological and chronological values: temperature, humidity, air pressure, months, days, national holidays, the day before the holidays, the day after the holidays, and New Year's holidays. Additionally, we evaluated the contribution of each feature by Shapley Additive exPlanations (SHAP) values. The training cohort included 96,597 OHCA patients. The eXtreme Gradient Boosting (XGBoost) had the highest area under the receiver operating curve (AUROC) of 0.906 (95% confidence interval; 0.868-0.944). In the test cohorts, the XGBoost algorithms also had high AUROC (0.862-0.923). The SHAP values indicated that the "mean temperature on the previous day" impacted the most on the model. Algorithms using machine learning with meteorological and chronological information could predict OHCA events accurately.

Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Prediction algorithm for ICU mortality and length of stay using machine learning.

Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study.

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775-0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830-0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829-0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

Outcome prediction in sepsis combined use of genetic polymorphisms - A study in Japanese population.

Genetic polymorphisms have recently been found to be related to clinical outcome in septic patients. The present study investigated to evaluate the influence of genetic polymorphisms in Japanese septic patients on clinical outcome and whether use of genetic polymorphisms as predictors would enable more accurate prediction of outcome. Effects of 16 genetic polymorphisms related to pro-inflammatory mediators and conventional demographic/clinical parameters (age, sex, past medical history, and APACHE II score) on ICU mortality as well as disease severity during ICU stay were examined in the septic patients (n=123) admitted to the ICU between October 2001 and November 2007 by multivariable logistic regression analysis. ICU mortality was significantly associated with TNF -308GA, IL1ß -31CT/TT, and APACHE II score. Receiver-operating characteristics (ROC) analysis demonstrated that, compared with APACHE II score alone (ROC-AUC=0.68), use of APACHE II score and two genetic parameters (TNF -308 and IL1ß -31) enabled more accurate prediction of ICU mortality (ROC-AUC=0.80). Significant association of two genetic polymorphisms, TNF -308 and IL1ß -31, with ICU mortality was observed in septic patients. In addition, combined use of these genetic parameters with APACHE II score may enable more accurate prediction of outcome in septic patients.

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

Association between low body mass index and increased 28-day mortality of severe sepsis in Japanese cohorts.

Current research regarding the association between body mass index (BMI) and altered clinical outcomes of sepsis in Asian populations is insufficient. We investigated the association between BMI and clinical outcomes using two Japanese cohorts of severe sepsis (derivation cohort, Chiba University Hospital, n = 614; validation cohort, multicenter cohort, n = 1561). Participants were categorized into the underweight (BMI < 18.5) and non-underweight (BMI ≥ 18.5) groups. The primary outcome was 28-day mortality. Univariate analysis of the derivation cohort indicated increased 28-day mortality trend in the underweight group compared to the non-underweight group (underweight 24.4% [20/82 cases] vs. non-underweight 16.0% [85/532 cases]; p = 0.060). In the primary analysis, multivariate analysis adjusted for baseline imbalance revealed that patients in the underweight group had a significantly increased 28-day mortality compared to those in the non-underweight group (p = 0.031, adjusted odds ratio [OR] 1.91, 95% confidence interval [CI] 1.06-3.46). In a repeated analysis using a multicenter validation cohort (underweight n = 343, non-underweight n = 1218), patients in the underweight group had a significantly increased 28-day mortality compared to those in the non-underweight group (p = 0.045, OR 1.40, 95% CI 1.00-1.97). In conclusion, patients with a BMI < 18.5 had a significantly increased 28-day mortality compared to those with a BMI ≥ 18.5 in Japanese cohorts with severe sepsis.

S-100B and neuron-specific enolase as predictors of neurological outcome in patients after cardiac arrest and return of spontaneous circulation: a systematic review.

INTRODUCTION: Neurological prognostic factors after cardiopulmonary resuscitation (CPR) in patients with cardiac arrest (CA) as early and accurately as possible are urgently needed to determine therapeutic strategies after successful CPR. In particular, serum levels of protein neuron-specific enolase (NSE) and S-100B are considered promising candidates for neurological predictors, and many investigations on the clinical usefulness of these markers have been published. However, the design adopted varied from study to study, making a systematic literature review extremely difficult. The present review focuses on the following three respects for the study design: definitions of outcome, value of specificity and time points of blood sampling. METHODS: A Medline search of literature published before August 2008 was performed using the following search terms: "NSE vs CA or CPR", "S100 vs CA or CPR". Publications examining the clinical usefulness of NSE or S-100B as a prognostic predictor in two outcome groups were reviewed. All publications met with inclusion criteria were classified into three groups with respect to the definitions of outcome; "dead or alive", "regained consciousness or remained comatose", and "return to independent daily life or not". The significance of differences between two outcome groups, cutoff values and predictive accuracy on each time points of blood sampling were investigated. RESULTS: A total of 54 papers were retrieved by the initial text search, and 24 were finally selected. In the three classified groups, most of the studies showed the significance of differences and concluded these biomarkers were useful for neurological predictor. However, in view of blood sampling points, the significance was not always detected. Nevertheless, only five studies involved uniform application of a blood sampling schedule with sampling intervals specified based on a set starting point. Specificity was not always set to 100%, therefore it is difficult to indiscriminately assess the cut-off values and its predictive accuracy of these biomarkers in this meta analysis. CONCLUSIONS: In such circumstances, the findings of the present study should aid future investigators in examining the clinical usefulness of these markers and determination of cut-off values.

Prehospital lactate improves prediction of the need for immediate interventions for hemorrhage after trauma.

The blood lactate level is used to guide the management of trauma patients with circulatory disturbance. We hypothesized that blood lactate levels at the scene (Lac scene) could improve the prediction for immediate interventions for hemorrhage. We prospectively measured blood lactate levels and assessed retrospectively in 435 trauma patients both at the scene and on arrival at the emergency room (ER) of a level I trauma center. Primary outcome was immediate intervention for hemorrhage defined as surgical/radiological intervention and/or blood transfusion within 24 h. Physiological variables plus Lac scene significantly increased the predictive value for immediate intervention (area under the curve [AUC] 0.882, 95% confidence interval [CI] 0.839-0.925) compared to that using physiological variables only (AUC 0.837, 95% CI 0.787-0.887, P = 0.0073), replicated in the validation cohort (n = 85). There was no significant improvement in predicting value of physiological variables plus Lac scene for massive transfusion compared to physiological variables (AUC 0.903 vs 0.895, P = 0.32). The increased blood lactate level per minute from scene to ER was associated with increased probability for immediate intervention (P < 0.0001). Both adding Lac scene to physiological variables and the temporal elevation of blood lactate levels from scene to ER could improve the prediction of the immediate intervention.

Genetic Polymorphisms in Sepsis and Cardiovascular Disease: Do Similar Risk Genes Suggest Similar Drug Targets?

Genetic variants are associated with altered clinical outcome of patients with sepsis and cardiovascular diseases. Common gene signaling pathways may be involved in the pathophysiology of these diseases. A better understanding of genetic commonality among these diseases may enable the discovery of important genes, signaling pathways, and therapeutic targets for these diseases. We investigated the common genetic factors by a systematic search of the literature. Twenty-four genes (ADRB2, CD14, FGB, FV, HMOX1, IL1B, IL1RN, IL6, IL10, IL17A, IRAK1, MASP2, MBL, MIR608, MIF, NOD2, PCSK9, PPARG, PROC, SERPINE1, SOD2, SVEP1, TF, TIRAP, TLR1) were extracted as reported genetic variations associated with altered outcome of both sepsis and cardiovascular diseases. Of these genes, the adverse allele (or combinations) was same in nine (ADRB2, FV, HMOX1, IL6, MBL, MIF, NOD2, PCSK9, SERPINE1), and the effect appears to be in the same direction in both sepsis and cardiovascular disease. Shared gene signaling pathways suggest that these are true biological results and could point to overlapping drug targets in sepsis and cardiovascular disease.

Reduced Proprotein convertase subtilisin/kexin 9 (PCSK9) function increases lipoteichoic acid clearance and improves outcomes in Gram positive septic shock patients.

Previous studies have shown lipopolysaccharide from Gram-negative bacteria is cleared from the circulation via LDL receptors on hepatocytes, which are downregulated by PCSK9. Whether clearance of Gram positive bacterial lipoteichoic acid (LTA) shows similar dependence on PCSK9, and whether this is clinically relevant in Gram positive human sepsis, is unknown. We examined survival data from three cohorts of patients who had Gram positive septic shock (n = 170, n = 130, and n = 59) and found that patients who carried a PCSK9 loss-of-function (LOF) allele had significantly higher 28-day survival (73.8%) than those with no LOF alleles (52.8%) (p = 0.000038). Plasma clearance of LTA was also found to be increased in PCSK9 knockout mice compared to wildtype control mice (p = 0.002). In addition, hepatocytes pre-treated with recombinant wildtype PCSK9 showed a dose-dependent decrease in uptake of fluorescently-labeled LTA (p < 0.01). In comparison to wildtype PCSK9, hepatocytes pre-treated with 3 different LOF variants of recombinant PCSK9 showed an increase in LTA uptake. This study shows the clearance of LTA follows a similar route as lipopolysaccharide, which is dependent on hepatic LDL receptors. This has important implications in health as strategies aimed at inhibiting PCSK9 function may be an effective treatment option for both Gram-positive and negative sepsis.

The Understanding and Management of Organism Toxicity in Septic Shock.

The toxicity caused by different organisms in septic shock is substantially complex and characterized by an intricate pathogenicity that involves several systems and pathways. Immune cells' pattern recognition receptors initiate the host response to pathogens after the recognition of pathogen-associated molecular patterns. In essence, the subsequent activation of downstream pathways may progress to infection resolution or to a dysregulated host response that represents the hallmark of organ injury in septic shock. Likewise, the management of organism toxicity in septic shock is complicated and comprises a multiplicity of suitable targets. In this review, the classic immune responses to pathogens are discussed as well as other factors that are relevant in the pathogenicity of septic shock, including sepsis-induced immune suppression, inflammasome activation, intestinal permeability, and the role of lipids and proprotein convertase subtilisin/kexin type 9. Current therapies aiming to eliminate the organisms causing septic shock, recent and ongoing trials in septic shock treatment, and potential new therapeutic strategies are also explored.

The IL20 Genetic Polymorphism Is Associated with Altered Clinical Outcome in Septic Shock.

BACKGROUND: The IL10 family of genes includes crucial immune regulators. We tested the hypothesis that single nucleotide polymorphisms (SNPs) in IL10, IL19, IL20, and IL24 of the IL10 family gene cluster alter the clinical outcome of septic shock. METHODS: Patients with septic shock (n = 1,193) were genotyped for 13 tag SNPs of IL10, IL19, IL20, and IL24. IL20 gene expression was measured in genotyped lymphoblastoid cells in vitro. Cardiac surgical ICU patients (n = 981) were genotyped for IL20 rs2981573 A/G. The primary outcome variable was 28-day mortality. RESULTS: Patients with the G allele of IL20 rs2981573 had a significantly increased hazard of death over the 28-day period compared to patients with the A allele in the septic shock cohort (adjusted hazard ratio 1.27; 95% confidence interval 1.10-1.47; p = 8.0 × 10-4). Patients with the GG genotype had more organ dysfunction (p < 0.05). The GG genotype was associated with increased IL20 gene expression in stimulated lymphoblastoid cells in vitro (p < 0.05). The cardiac surgical ICU patients with the GG genotype had an increased length of ICU stay (p = 0.032). CONCLUSIONS: The GG genotype of IL20 rs2981573 SNP was associated with increased IL20 gene expression and increased adverse outcomes in patients with septic shock and following cardiac surgery.