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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.

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.

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.

Background Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. We tested the hypothesis that the machine learning-based prehospital algorithm shows a high predictive power for predicting ACS. Methods 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 (EMS) personnel with suspected ACS, were analyzed. The primary outcomes were binary classification models for ACS prediction based on eXtreme Gradient Boosting (XGBoost). Secondary outcomes were classification models for subcategories of ACS, including acute myocardial infarction (AMI) and ST-segment elevation myocardial infarction (STEMI). We evaluated the model performance based on the area under the receiver operating curve (AUC). Results Of the 555 enrolled patients, 388 (70%) were randomly assigned to a training cohort, and 167 (30%) were placed in a test cohort. The XGBoost model for ACS using 43 features performed well (AUC 0.879 [95% CI 0.815–0.935]) in the test cohort. We repeated the analysis with a limited number of selected features, and the performance of the XGBoost model using 17 features remained high (AUC 0.883 [95% CI 0.820–0.939]) in the test cohort. The XGBoost model for ACS using 17 features had a higher AUC than the other four common prediction models: logistic regression, random forest, a linear support vector machine (SVM), and a radial basis function (RBF) kernel SVM. The XGBoost algorithm for prediction of AMI and STEMI using the same 17 features also resulted in high AUC scores of 0.857 [95% CI 0.788–0.919] and 0.871 [95% CI 0.819–0.925] in the test cohort, respectively. Conclusions We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection., 「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.

Background: 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.Methods: 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, and 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.Results: Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945. In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 and 0.889, 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.Conclusion: 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.

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 scene to physiological variables and the temporal elevation of blood lactate levels from scene to ER could improve the prediction of the immediate intervention.

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 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

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.

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.Retrospective analysis.University research laboratory.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.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.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.Lipopolysaccharide may be sequestered in adipose tissue via the very low density lipoprotein receptor and this sequestration may contribute to improved sepsis survival.

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

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.

High-density cholesterol (HDL-C) levels are influenced by genetic variation in several genes. Low levels of HDL-C have been associated with increased risk of acute kidney injury (AKI). We investigated whether genetic polymorphisms in ten genes known to regulate HDL-C levels are associated with both HDL-C levels and AKI development during sepsis. Two cohorts were retrospectively analyzed: Derivation Cohort (202 patients with sepsis enrolled at the Emergency Department from 2011 to 2014 in Vancouver, Canada); Validation Cohort (604 septic shock patients enrolled into the Vasopressin in Septic Shock Trial (VASST)). Associations between HDL-related genetic polymorphisms and both HDL-C levels, and risk for clinically significant sepsis-associated AKI (AKI KDIGO stages 2 and 3) were evaluated. In the Derivation Cohort, one genetic variant in the Cholesteryl Ester Transfer Protein (CETP) gene, rs1800777 (allele A), was strongly associated with lower HDL-C levels (17.4 mg/dL vs. 32.9 mg/dL, P = 0.002), greater CETP mass (3.43 µg/mL vs. 1.32 µg/mL, P = 0.034), and increased risk of clinically significant sepsis-associated AKI (OR: 8.28, p = 0.013). Moreover, the same allele was a predictor of sepsis-associated AKI in the Validation Cohort (OR: 2.38, p = 0.020). Our findings suggest that CETP modulates HDL-C levels in sepsis. CETP genotype may identify patients at high-risk of sepsis-associated AKI.

Background Mechanical cardiopulmonary resuscitation ( mCPR ) for patients with out‐of‐hospital cardiac arrest attending the emergency department has become more widespread in Japan. The objective of this study is to determine the association between the mCPR in the emergency department and clinical outcomes. Methods and Results In a prospective, multicenter, observational study, adult patients with out‐of‐hospital cardiac arrest with sustained circulatory arrest on hospital arrival were identified. The primary outcome was survival to hospital discharge. The secondary outcomes included a return of spontaneous circulation and successful hospital admission. Multivariate analyses adjusted for potential confounders and within‐institution clustering effects using a generalized estimation equation were used to analyze the association of the mCPR with outcomes. Between January 1, 2012 and March 31, 2013, 6537 patients with out‐of‐hospital cardiac arrest were eligible; this included 5619 patients (86.0%) in the manual CPR group and 918 patients (14.0%) in the mCPR group. Of those patients, 28.1% (1801/6419) showed return of spontaneous circulation in the emergency department, 20.4% (1175/5754) had hospital admission, 2.6% (168/6504) survived to hospital discharge, and 1.2% (75/6419) showed a favorable neurological outcome at 1 month after admission. Multivariate analyses revealed that mCPR was associated with a decreased likelihood of survival to hospital discharge (adjusted odds ratio, 0.40; 95% confidence interval, 0.20–0.78; P =0.005), return of spontaneous circulation (adjusted odds ratio, 0.71; 95% confidence interval, 0.53–0.94; P =0.018), and hospital admission (adjusted odds ratio, 0.57; 95% confidence interval, 0.40–0.80; P =0.001). Conclusions After accounting for potential confounders, the mCPR in the emergency department was associated with decreased likelihoods of good clinical outcomes after adult nontraumatic out‐of‐hospital cardiac arrest. Further studies are needed to clarify circumstances in which mCPR may benefit these patients.

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.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.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 (p0.05). The GG genotype was associated with increased IL20 gene expression in stimulated lymphoblastoid cells in vitro (p0.05). The cardiac surgical ICU patients with the GG genotype had an increased length of ICU stay (p = 0.032).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.

Background The American Heart Association and European Resuscitation Council guidelines for cardiopulmonary resuscitation present rules for termination of resuscitation (TOR) in cases of out-of-hospital cardiac arrest (OHCA). In Japan, only doctors are legally allowed TOR in OHCA cases. Objective This study aimed to develop a new TOR rule that suits the actual situations of the Japanese emergency medical services system. Methods Five different combinations of the TOR rule criteria were compared regarding specificity and positive predictive value (PPV) for 1-month survival with unfavorable neurologic outcomes. The criteria were unwitnessed by emergency medical service personnel, unwitnessed by bystanders, initial unshockable rhythm in the field, initial asystole in the field, no shock delivered, no prehospital return of spontaneous circulation, unshockable rhythm at hospital arrival, and asystole at hospital arrival. Results A total of 13,291 cases were included. The following combination provided the highest specificity and PPV for predicting 1-month unfavorable neurologic outcomes and death: unwitnessed by bystanders, initial asystole in the field, and asystole at hospital arrival. The specificity and PPV for the combination of the three criteria for predicting 1-month unfavorable neurologic outcomes were 0.992 and 0.999, and for predicting death at 1 month after OHCA were 0.986 and 0.998, respectively. Conclusions OHCA patients fulfilling the criteria unwitnessed by bystanders and asystole in the field and at hospital arrival had universally poor outcomes. Termination of resuscitation after hospital arrival for these patients may decrease unwarranted treatments.

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, IL1β −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 IL1β −31) enabled more accurate prediction of ICU mortality (ROC–AUC = 0.80). Significant association of two genetic polymorphisms, TNF −308 and IL1β −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.

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.