Early PREdiction of sepsis using leukocyte surface biomarkers: the ExPRES-sepsis cohort study

Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study

BACKGROUND

Sepsis is a life-threatening condition frequently observed in patients with intracerebral hemorrhage (ICH) who are critically ill. Early and accurate identification and prediction of sepsis are crucial. Machine learning (ML)-based predictive models exhibit promising sepsis prediction capabilities in emergency settings. However, their application in predicting sepsis among patients with ICH is still limited.

OBJECTIVE

The aim of the study is to develop an ML-driven risk calculator for early prediction of sepsis in patients with ICH who are critically ill and to clarify feature importance and explain the model using the Shapley Additive Explanations method.

METHODS

Patients with ICH admitted to the intensive care unit (ICU) from the Medical Information Mart for Intensive Care IV database between 2008 and 2022 were divided into training and internal test sets. The external test was performed using the eICU Collaborative Research Database, which includes over 200,000 ICU admissions across the United States between 2014 and 2015. Sepsis following ICU admission was identified using Sepsis-3.0 through clinical diagnosis combining elevation of the Sequential Organ Failure Assessment by ≥2 points with suspected infection. The Boruta algorithm was used for feature selection, confirming 29 features. Nine ML algorithms were used to construct the prediction models. Predictive performance was compared using several evaluation metrics, including the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanations technique was used to interpret the final model, and a web-based risk calculator was constructed for clinical practice.

RESULTS

Overall, 2414 patients with ICH were enrolled from the Medical Information Mart for Intensive Care IV database, with 1689 and 725 patients assigned to the training and internal test sets, respectively. An external test set of 2806 patients with ICH from the eICU database was used. Among the 9 ML models tested, the categorical boosting (CatBoost) model demonstrated the best discriminative ability. After reducing features based on their importance, an explainable final CatBoost model was developed using 8 features. The final model accurately predicted sepsis in internal (AUC=0.812) and external (AUC=0.771) tests.

CONCLUSIONS

We constructed a web-based risk calculator with 8 features based on the CatBoost model to assist clinicians in identifying people at high risk for sepsis in patients with ICH who are critically ill.

COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES

ABSTRACT

Background : Extracorporeal membrane oxygenation (ECMO) is an effective technique for providing short-term mechanical support to the heart, lungs, or both. During ECMO treatment, the inflammatory response, particularly involving cytokines, plays a crucial role in pathophysiology. However, the potential effects of cytokines on patients receiving ECMO are not comprehensively understood. Methods : We acquired three ECMO support datasets, namely two bulk and one single-cell RNA sequencing (RNA-seq), from the Gene Expression Omnibus (GEO) combined with hospital cohorts to investigate the expression pattern and potential biological processes of cytokine-related genes (CRGs) in patients under ECMO. Subsequently, machine learning approaches, including support vector machine (SVM), random forest (RF), modified Lasso penalized regression, extreme gradient boosting (XGBoost), and artificial neural network (ANN), were applied to identify hub CRGs, thus developing a prediction model called CRG classifier. The predictive and prognostic performance of the model was comprehensively evaluated in GEO and hospital cohorts. Finally, we mechanistically analyzed the relationship between hub cytokines, immune cells, and pivotal molecular pathways. Results : Analyzing bulk and single-cell RNA-seq data revealed that most CRGs were significantly differentially expressed; the enrichment scores of cytokine and cytokine-cytokine receptor (CCR) interaction were significantly higher during ECMO. Based on multiple machine learning algorithms, nine key CRGs (CCL2, CCL4, IFNG, IL1R2, IL20RB, IL31RA, IL4, IL7, and IL7R) were used to develop the CRG classifier. The CRG classifier exhibited excellent prognostic values (AUC > 0.85), serving as an independent risk factor. It performed better in predicting mortality and yielded a larger net benefit than other clinical features in GEO and hospital cohorts. Additionally, IL1R2, CCL4, and IL7R were predominantly expressed in monocytes, NK cells, and T cells, respectively. Their expression was significantly positively correlated with the relative abundance of corresponding immune cells. Gene set variation analysis (GSVA) revealed that para-inflammation, complement and coagulation cascades, and IL6/JAK/STAT3 signaling were significantly enriched in the subgroup that died after receiving ECMO. Spearman correlation analyses and Mantel tests revealed that the expression of hub cytokines (IL1R2, CCL4, and IL7R) and pivotal molecular pathways scores (complement and coagulation cascades, IL6/JAK/STAT3 signaling, and para-inflammation) were closely related. Conclusion : A predictive model (CRG classifier) comprising nine CRGs based on multiple machine learning algorithms was constructed, potentially assisting clinicians in guiding individualized ECMO treatment. Additionally, elucidating the underlying mechanistic pathways of cytokines during ECMO will provide new insights into its treatment.

PD-L1 and PD-1 Are Associated with Clinical Outcomes and Alveolar Immune Cell Activation in Acute Respiratory Distress Syndrome

The relationship between the PD-L1 (Programmed Death-Ligand 1)/PD-1 pathway, lung inflammation, and clinical outcomes in acute respiratory distress syndrome (ARDS) is poorly understood. We sought to determine whether PD-L1/PD-1 in the lung or blood is associated with ARDS and associated severity. We measured soluble PD-L1 (sPD-L1) in plasma and lower respiratory tract samples (ARDS1 [n = 59] and ARDS2 [n = 78]) or plasma samples alone (ARDS3 [n = 149]) collected from subjects with ARDS and tested for associations with mortality using multiple regression. We used mass cytometry to measure PD-L1/PD-1 expression and intracellular cytokine staining in cells isolated from BAL fluid (n = 18) and blood (n = 16) from critically ill subjects with or without ARDS enrolled from a fourth cohort. Higher plasma concentrations of sPD-L1 were associated with mortality in ARDS1, ARDS2, and ARDS3. In contrast, higher concentrations of sPD-L1 in the lung were either not associated with mortality (ARDS2) or were associated with survival (ARDS1). Alveolar PD-1POS T cells had more intracellular cytokine staining than PD-1NEG T cells. Subjects without ARDS had a higher ratio of PD-L1POS alveolar macrophages to PD-1POS T cells than subjects with ARDS. We conclude that sPD-L1 may have divergent cellular sources and/or functions in the alveolar versus blood compartments, given distinct associations with mortality. Alveolar leukocyte subsets defined by PD-L1 or PD-1 cell-surface expression have distinct cytokine secretion profiles, and the relative proportions of these subsets are associated with ARDS.

Causal relationship between lymphocyte subsets and the risk of sepsis: A Mendelian randomization study

Recent empirical research posits a link between lymphocyte subgroups and both the incidence and prognosis of sepsis. Nevertheless, the potential influence of multiple confounding variables obscures any clear causative correlation. Utilizing a 2-sample Mendelian randomization approach, we conducted a meta-analysis of lymphocyte subgroups. In a genome-wide association study, flow cytometry was applied to a lymphocyte subgroup comprising 3757 Sardinians to identify genes influenced by blood immune cells. The sepsis meta-analysis data were sourced from the UK Biobank database, including 11,643 treatment groups and 47,841 control groups. Inverse variance-weighted, Mendelian randomization-Egger regression, weighted median, simple mode, and weighted mode methods were deployed to ascertain the causative relationship between lymphocyte subgroup and sepsis. Cochran Q test, the Mendelian randomization-Egger intercept test, and funnel plots were leveraged to assess the robustness of study findings. The inverse variance-weighted analysis disclosed that the absolute count of CD4 regulatory T cells (CD4 Treg AC) within the lymphocyte subgroup has a causative link to an elevated risk of sepsis, with an odds ratio of 1.08 and a 95% confidence interval of 1.02 to 1.15 (P = .011). Compared to individuals not subjected to this factor, those exposed to CD4 Treg AC have a marginally elevated sepsis risk by approximately 0.08%. No causative relationships were observed between sepsis risk and the absolute counts of other lymphocyte subgroups such as CD8+ T cells, CD4+ CD8dim T cells, natural killer T cells, B cells (B cell absolute count), and HLA DR+ natural killer cells. The 2-sample Mendelian randomization study indicated a causal relationship between the level of CD4 Treg AC and the increased risk of sepsis. The elevation in circulating lymphocyte subgroups suggests higher susceptibility to sepsis, affirming the immune susceptibility inherent to this condition. The findings from our study may propose potential targets for diagnosis and intervention of sepsis.

HETEROGENEOUS EXPANSION OF POLYMORPHONUCLEAR MYELOID-DERIVED SUPPRESSOR CELLS DISTINGUISHES HIGH-RISK SEPSIS IMMUNOPHENOTYPES IN UGANDA

ABSTRACT

Background: Understanding of immune cell phenotypes associated with inflammatory and immunosuppressive host responses in sepsis is imprecise, particularly in low- and middle-income countries, where the global sepsis burden is concentrated. In these settings, elucidation of clinically relevant immunophenotypes is necessary to determine the relevance of emerging therapeutics and refine mechanistic investigations of sepsis immunopathology. Methods: In a prospective cohort of adults hospitalized with suspected sepsis in Uganda (N = 43; median age 46 years [IQR 36-59], 24 [55.8%] living with HIV, 16 [37.2%] deceased at 60 days), we combined high-dimensional flow cytometry with unsupervised machine learning and manual gating to define peripheral immunophenotypes associated with increased risk of 60-day mortality. Results: Patients who died showed heterogeneous expansion of polymorphonuclear myeloid-derived suppressor cells, with increased and decreased abundance of CD16 - PD-L1 dim and CD16 bright PD-L1 bright subsets, respectively, significantly associated with mortality. While differences between CD16 - PD-L1 dim cell abundance and mortality risk appeared consistent throughout the course of illness, those for the CD16 bright PD-L1 bright subset were more pronounced early after illness onset. Independent of HIV co-infection, depletion of CD4 + T cells, dendritic cells, and CD56 - CD16 bright NK cells were significantly associated with mortality risk, as was expansion of immature, CD56 + CD16 - CD11c + NK cells. Abundance of T cells expressing inhibitory checkpoint proteins (PD-1, CTLA-4, LAG-3) was similar between patients who died versus those who survived. Conclusions: This is the first study to define high-risk immunophenotypes among adults with sepsis in sub-Saharan Africa, an immunologically distinct region where biologically informed treatment strategies are needed. More broadly, our findings highlight the clinical importance and complexity of myeloid derived suppressor cell expansion during sepsis and support emerging data that suggest a host-protective role for PD-L1 myeloid checkpoints in acute critical illness.

Sepsis in elderly patients: the role of neutrophils in pathophysiology and therapy

Sepsis is among the most important causes of mortality, particularly within the elderly population. Sepsis prevalence is on the rise due to different factors, including increasing average population age and the concomitant rise in the prevalence of frailty and chronic morbidities. Recent investigations have unveiled a "trimodal" trajectory for sepsis-related mortality, with the ultimate zenith occurring from 60 to 90 days until several years after the original insult. This prolonged temporal course ostensibly emanates from the sustained perturbation of immune responses, persevering beyond the phase of clinical convalescence. This phenomenon is particularly associated with the aging immune system, characterized by a broad dysregulation commonly known as "inflammaging." Inflammaging associates with a chronic low-grade activation of the innate immune system preventing an appropriate response to infective agents. Notably, during the initial phases of sepsis, neutrophils-essential in combating pathogens-may exhibit compromised activity. Paradoxically, an overly zealous neutrophilic reaction has been observed to underlie multi-organ dysfunction during the later stages of sepsis. Given this scenario, discovering treatments that can enhance neutrophil activity during the early phases of sepsis while curbing their overactivity in the later phases could prove beneficial in fighting pathogens and reducing the detrimental effects caused by an overactive immune system. This narrative review delves into the potential key role of neutrophils in the pathological process of sepsis, focusing on how the aging process impacts their functions, and highlighting possible targets for developing immune-modulatory therapies. Additionally, the review includes tables that outline the principal potential targets for immunomodulating agents.

A meta-analysis of diabetes risk prediction models applied to prediabetes screening

AIM

To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.

METHODS

The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.

RESULTS

A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.

CONCLUSIONS

Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

Advances in sepsis biomarkers

Sepsis, a dysregulated host immune response to an infectious agent, significantly increases morbidity and mortality for hospitalized patients worldwide. This chapter reviews (1) the basic principles of infectious diseases, pathophysiology and current definition of sepsis, (2) established sepsis biomarkers such lactate, procalcitonin and C-reactive protein, (3) novel, newly regulatory-cleared/approved biomarkers, such as assays that evaluate white blood cell properties and immune response molecules, and (4) emerging biomarkers and biomarker panels to highlight future directions and opportunities in the diagnosis and management of sepsis.

Association of Leukocyte Subpopulations Identified by Flow Cytometry with Outcomes of Sepsis in a Respiratory Intensive Care Unit: An Observational Study

INTRODUCTION

The dysregulated host immune response in sepsis is orchestrated by peripheral blood leukocytes. This study explored the associations of the peripheral blood leukocyte subpopulations with early clinical deterioration and mortality in sepsis.

METHODS

We performed a prospective observational single-center study enrolling adult subjects with sepsis within 48 h of hospital admission. Peripheral blood flow cytometry was performed for the patients at enrolment and after 5 days. The primary outcome was to explore the association between various leukocyte subpopulations at enrolment and early clinical deterioration [defined as an increase in the sequential organ failure assessment (SOFA) score between enrolment and day 5, or death before day 5]. Other pre-specified outcomes explored associations of leukocyte subpopulations at enrolment and on day 5 with in-hospital mortality.

RESULTS

A total of 100 patients, including 47 with septic shock were enrolled. The mean (SD) age of the patients was 53.99 (14.93) years. Among them, 26 patients had early clinical deterioration, whereas 41 died during hospitalization. There was no significant association between the leukocyte subpopulations at enrolment and early clinical deterioration on day 5. On multivariate logistic regression, a reduced percentage of CD8 + CD25+ T-cells at enrolment was associated with in-hospital mortality [odds ratio (OR), 0.82 (0.70-0.97); p-value = 0.02]. A reduced lymphocyte percentage on day 5 was associated with in-hospital mortality [OR, 0.28 (0.11-0.69); p-value = 0.01]. In a post-hoc analysis, patients with "very early" deterioration within 48 h had an increased granulocyte CD64 median fluorescent intensity (MFI) [OR, 1.07 (1.01-1.14); p-value = 0.02] and a reduced granulocyte CD16 MFI [OR, 0.97 (0.95-1.00); p-value = 0.04] at enrolment.

CONCLUSIONS

None of the leukocyte subpopulations showed an association with early clinical deterioration at day 5. Impaired lymphocyte activation and lymphocytopenia indicative of adaptive immune dysfunction may be associated with in-hospital mortality.

An integrative model with HLA-DR, CD64, and PD-1 for the diagnostic and prognostic evaluation of sepsis

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and progressive immunosuppression with high mortality. HLA-DR, CD64, and PD-1 were assumed to be useful biomarkers for sepsis prediction. However, the ability of a combination of these biomarkers has not been clarified.

METHODS

An observational case-control study was conducted that included 30 sepsis patients, 30 critically ill patients without sepsis admitted to the intensive care unit (ICU), and 32 healthy individuals. The levels of HLA-DR, CD64, and PD-1 expression in peripheral blood immune cells and subsets was assayed on Days 1, 3, and 5, and the clinical information of patients was collected. We compared these biomarkers between groups and evaluated the predictive validity of single and combined biomarkers on sepsis mortality.

RESULTS

The results indicate that PD-1 expression on CD4- CD8- T (PD-1+ CD4- CD8- T) (19.19% ± 10.78% vs. 9.88% ± 1.79%, p = .004) cells and neutrophil CD64 index (nCD64 index) (9.15 ± 5.46 vs. 5.33 ± 2.34, p = .001) of sepsis patients were significantly increased, and HLA-DR expression on monocytes (mHLA-DR+ ) was significantly reduced (13.26% ± 8.06% vs. 30.17% ± 21.42%, p = 2.54 × 10-4 ) compared with nonsepsis critically ill patients on the first day. Importantly, the expression of PD-1+ CD4- CD8- T (OR = 0.622, 95% CI = 0.423-0.916, p = .016) and mHLA-DR+ (OR = 1.146, 95% CI = 1.014-1.295, p = .029) were significantly associated with sepsis mortality. For sepsis diagnosis, the mHLA-DR+ , PD-1+ CD4- CD8- T, and nCD64 index showed the moderate individual performance, and combinations of the three biomarkers achieved greater diagnostic value (AUC = 0.899, 95% CI = 0.792-0.962). When adding PCT into the combined model, the AUC increased to 0.936 (95% CI = 0.840-0.983). For sepsis mortality, combinations of PD-1+ CD4- CD8- T and mHLA-DR+ , have a good ability to predict the prognosis of sepsis patients, with an AUC = 0.921 (95% CI = 0.762-0.987).

CONCLUSION

These findings indicate that the combinations of HLA-DR, CD64, and PD-1 outperformed each of the single indicator in diagnosis and predicting prognosis of sepsis.

The implication of targeting PD-1:PD-L1 pathway in treating sepsis through immunostimulatory and anti-inflammatory pathways

Sepsis currently remains a major contributor to mortality in the intensive care unit (ICU), with 48.9 million cases reported globally and a mortality rate of 22.5% in 2017, accounting for almost 20% of all-cause mortality worldwide. This highlights the urgent need to improve the understanding and treatment of this condition. Sepsis is now recognized as a dysregulation of the host immune response to infection, characterized by an excessive inflammatory response and immune paralysis. This dysregulation leads to secondary infections, multiple organ dysfunction syndrome (MODS), and ultimately death. PD-L1, a co-inhibitory molecule expressed in immune cells, has emerged as a critical factor in sepsis. Numerous studies have found a significant association between the expression of PD-1/PD-L1 and sepsis, with a particular focus on PD-L1 expressed on neutrophils recently. This review explores the role of PD-1/PD-L1 in immunostimulatory and anti-inflammatory pathways, illustrates the intricate link between PD-1/PD-L1 and sepsis, and summarizes current therapeutic approaches against PD-1/PD-L1 in the treatment and prognosis of sepsis in preclinical and clinical studies.

The non-canonical inflammasome activators Caspase-4 and Caspase-5 are differentially regulated during immunosuppression-associated organ damage

The non-canonical inflammasome, which includes caspase-11 in mice and caspase-4 and caspase-5 in humans, is upregulated during inflammatory processes and activated in response to bacterial infections to carry out pyroptosis. Inadequate activity of the inflammasome has been associated with states of immunosuppression and immunopathological organ damage. However, the regulation of the receptors caspase-4 and caspase-5 during severe states of immunosuppression is largely not understood. We report that CASP4 and CASP5 are differentially regulated during acute-on-chronic liver failure and sepsis-associated immunosuppression, suggesting non-redundant functions in the inflammasome response to infection. While CASP5 remained upregulated and cleaved p20-GSDMD could be detected in sera from critically ill patients, CASP4 was downregulated in critically ill patients who exhibited features of immunosuppression and organ failure. Mechanistically, downregulation of CASP4 correlated with decreased gasdermin D levels and impaired interferon signaling, as reflected by decreased activity of the CASP4 transcriptional activators IRF1 and IRF2. Caspase-4 gene and protein expression inversely correlated with markers of organ dysfunction, including MELD and SOFA scores, and with GSDMD activity, illustrating the association of CASP4 levels with disease severity. Our results document the selective downregulation of the non-canonical inflammasome activator caspase-4 in the context of sepsis-associated immunosuppression and organ damage and provide new insights for the development of biomarkers or novel immunomodulatory therapies for the treatment of severe infections.

Neutrophil CD64-a prognostic marker of sepsis in intensive care unit: a prospective cohort study

Background

Little is known about the prognostic ability of nCD64 in critically ill patients. This study aimed to assess the prognostic values of nCD64 in adult ICU patients with sepsis.

Methods

A prospective cohort study was conducted at the ICU of Cho Ray Hospital in Vietnam between January 2019 to September 2020. All newly admitted 86 septic patients diagnosed based on sepsis-3 criteria were included. An evaluation of nCD64 was performed at admission (T0) and 48 h thereafter (T48). Delta nCD64 (nCD64 T48 - nCD64 T0), %delta nCD64 [(nCD64 T48 - nCD64 T0)/nCD64 T0 x 100%], APACHE II and SOFA scores were calculated and examined. Serum procalcitonin levels and white blood cell counts were documented. Spearman's rank correlation coefficient was used to test the correlation between nCD64 and severity scores. Receiver-operating characteristic (ROC) curve was performed to evaluate the predictive efficacy of the sepsis parameters.

Results

Patients with septic shock had significantly higher nCD64 levels than septic patients [3,568 (2,589; 5,999) vs. 1,514 (1,416;2,542) molecules/cell, p < 0.001]. nCD64 T0 and SOFA scores had a moderately positive linear correlation (R = 0.31, p = 0.004). In the survivor group, nCD64 levels significantly decreased within the first 48 h of admission (p < 0.001), while this trend was not statistically significant in the non-survivor group (p = 0.866). The area under the ROC curve (AUC) value of %delta nCD64 combined with APACHE II score (0.81) was higher than that of any other parameter alone or in combination with each other.

Conclusion

The nCD64 index may serve as a valuable biomarker for predicting the course of sepsis. Monitoring changes in nCD64 during the initial 48 h of admission can aid in predicting the prognosis of septic patients. The use of a combination of the trends of nCD64 index in the first 48 h with APACHE II score would further enhance the predictive accuracy. More studies with longer follow-ups are needed to fully understand the implications of serial trend and kinetics of nCD64 in septic patients.

Multimodal analysis of granulocytes, monocytes, and platelets in patients with cystic fibrosis before and after Elexacaftor-Tezacaftor-Ivacaftor treatment

Cystic fibrosis (CF) is a monogenetic disease caused by an impairment of the cystic fibrosis transmembrane conductance regulator (CFTR). CF affects multiple organs and is associated with acute and chronic inflammation. In 2020, Elexacaftor-Tezacaftor-Ivacaftor (ETI) was approved to enhance and restore the remaining CFTR functionality. This study investigates cellular innate immunity, with a focus on neutrophil activation and phenotype, comparing healthy volunteers with patients with CF before (T1, n = 13) and after six months (T2, n = 11) of ETI treatment. ETI treatment reduced sweat chloride (T1: 95 mmol/l (83|108) vs. T2: 32 mmol/l (25|62), p < 0.01, median, first|third quartile) and significantly improved pulmonal function (FEV₁ T1: 2.66 l (1.92|3.04) vs. T2: 3.69 l (3.00|4.03), p < 0.01). Moreover, there was a significant decrease in the biomarker human epididymis protein 4 (T1: 6.2 ng/ml (4.6|6.3) vs. T2: 3.0 ng/ml (2.2|3.7), p < 0.01) and a small but significant decrease in matrix metallopeptidase 9 (T1: 45.5 ng/ml (32.5|140.1) vs. T2: 28.2 ng/ml (18.2|33.6), p < 0.05). Neutrophil phenotype (CD10, CD11b, CD62L, and CD66b) and function (radical oxygen species generation, chemotactic and phagocytic activity) remained largely unaffected by ETI treatment. Likewise, monocyte phenotype and markers of platelet activation were similar at T1 and T2. In summary, the present study confirmed a positive impact on patients with CF after ETI treatment. However, neither beneficial nor harmful effects of ETI treatment on cellular innate immunity could be detected, possibly due to the study population consisting of patients with well-controlled CF.

Serum CD64 as a Marker for Chronic Periprosthetic Joint Infection

Background

Serum cluster of differentiation 64 (CD64) has emerged as a diagnostic test for musculoskeletal infections. The purpose of this study was to evaluate the utility of serum CD64 in diagnosing periprosthetic joint infections (PJIs) compared to conventional markers like white blood count (WBC), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and interleukin-6 (IL-6).

Methods

A prospective case-control study on patients undergoing revision hip or knee arthroplasty surgery >6 weeks after their index surgery was performed at a single institution. Whole blood samples were drawn within 24 hours prior to revision surgery for white blood count, ESR, CRP, IL-6, and CD64. Intraoperative cultures were obtained during the revision, and PJI was defined using the major criteria from the 2018 Musculoskeletal Infection Society criteria. Two-sample Wilcoxon rank-sum test and Fisher's exact test were used to determine if there were significant differences in serum laboratory values between patients with and without infection. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy of each test were calculated.

Results

With an average age of 67 years, 39 patients with 15 revision THAs and 24 TKAs, were included. 19 patients (48.7%) were determined to have PJI. Patients with PJI had significantly higher CD64 (P = .036), CRP (P = .016), and ESR (P = .045). CD64 had the highest specificity (100%) and PPV (100%), moderate accuracy (69.2%), but low sensitivity (37.0%) and negative predictive value (62.5%).

Conclusions

Given the high specificity, PPV, and accuracy, CD64 may be an excellent confirmatory test to help diagnose PJI.

Cytokine Biomarker Phenotype for Early Prediction and Triage of Sepsis in Blunt Trauma Patients

BACKGROUND

Altered levels of inflammatory markers secondary to severe trauma present a major problem to physicians and are prone to interfering with the clinical identification of sepsis events. This study aimed to establish the profiles of cytokines in trauma patients to characterize the nature of immune responses to sepsis, which might enable early prediction and individualized treatments to be developed for targeted intervention.

METHODS

A 15-plex human cytokine magnetic bead assay system was used to measure analytes in citrated plasma samples. Analysis of the kinetics of these cytokines was performed in 40 patients with severe blunt trauma admitted to our trauma center between March 2016 and February 2017, with an Injury Severity Score (ISS) greater than 20 with regard to sepsis (Sepsis-3) over a 14-d time course.

RESULTS

In total, the levels of six cytokines were altered in trauma patients across the 1-, 3-, 5-, 7-, and 14-d time points. Additionally, IL-6, IL-10, IL-15, macrophage derived chemokine (MDC), GRO, sCD40 L, granulocyte colony-stimulating factor (G-CSF), and fibroblast growth factor (FGF)-2 levels could be used to provide a significant discrimination between sepsis and nonsepsis patients at day 3 and afterward, with an area under the curve (AUC) of up to 0.90 through a combined analysis of the eight biomarkers (P < 0.001). Event-related analysis demonstrated 1.5- to 4-fold serum level changes for these cytokines within 72 h before clinically apparent sepsis.

CONCLUSIONS

Cytokine profiles demonstrate a high discriminatory ability enabling the timely identification of evolving sepsis in trauma patients. These abrupt changes enable sepsis to be detected up to 72 h before clinically overt deterioration. Defining cytokine release patterns that distinguish sepsis risk from trauma patients might enable physicians to initiate timely treatment and reduce mortality. Large prospective studies are needed to validate and operationalize the findings.

TRIAL REGISTRATION

Clinicaltrials, NCT01713205. Registered October 22, 2012, https://register.

CLINICALTRIALS

gov/NCT01713205.

Role of immunosuppression in an antibiotic stewardship intervention and its association with clinical outcomes and antibiotic use: protocol for an observational study (RISC-sepsis)

INTRODUCTION

Sepsis is characterised by a dysregulated immune response to infection, with exaggerated pro-inflammatory and anti-inflammatory responses. A predominant immunosuppressive profile affecting both innate and adaptive immune responses is associated with increased hospital-acquired infection and reduced infection-free survival. While hospital-acquired infection leads to additional antibiotic use, the role of the immunosuppressive phenotype in guiding complex decisions, such as those affecting antibiotic stewardship, is uncertain. This study is a mechanistic substudy embedded within a multicentre clinical and cost-effectiveness trial of biomarker-guided antibiotic stewardship. This mechanistic study aims to determine the effect of sepsis-associated immunosuppression on the trial outcome measures.

METHODS AND ANALYSIS

RISC-sepsis is a prospective, multicentre, exploratory, observational study embedded within the ADAPT-sepsis trial. A subgroup of 180 participants with antibiotics commenced for suspected sepsis, enrolled in the ADAPT-sepsis trial, will be recruited. Blood samples will be collected on alternate days until day 7. At each time point, blood will be collected for flow cytometric analysis into cell preservation tubes. Immunophenotyping will be performed at a central testing hub by flow cytometry. The primary outcome measures are monocyte human leucocyte antigen-DR; neutrophil CD88; programmed cell death-1 on monocytes, neutrophils and T lymphocytes and the percentage of regulatory T cells. Secondary outcome measures will link to trial outcomes from the ADAPT-sepsis trial including antibiotic days; occurrence of hospital-acquired infection and length of ICU-stay and hospital-stay.

ETHICS AND DISSEMINATION

Ethical approval has been granted (IRAS 209815) and RISC-sepsis is registered with the ISRCTN (86837685). Study results will be disseminated by peer-reviewed publications, presentations at scientific meetings and via patient and public participation groups and social media.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Standardization of neutrophil CD64 and monocyte HLA-DR measurement and its application in immune monitoring in kidney transplantation

Background

Infections cause high mortality in kidney transplant recipients (KTRs). The expressions of neutrophil CD64 (nCD64) and monocyte HLA-DR (mHLA-DR) provide direct evidence of immune status and can be used to evaluate the severity of infection. However, the intensities of nCD64 and mHLA-DR detected by flow cytometry (FCM) are commonly measured by mean fluorescence intensities (MFIs), which are relative values, thus limiting their application. We aimed to standardize nCD64 and mHLA-DR expression using molecules of equivalent soluble fluorochrome (MESF) and to explore their role in immune monitoring for KTRs with infection.

Methods

The study included 50 KTRs diagnosed with infection, 65 immunologically stable KTRs and 26 healthy controls. The blood samples were collected and measured simultaneously by four FCM protocols at different flow cytometers. The MFIs of nCD64 and mHLA-DR were converted into MESF by Phycoerythrin (PE) Fluorescence Quantitation Kit. The intraclass correlation coefficients (ICCs) and the Bland-Altman plots were used to evaluate the reliability between the four FCM protocols. MESFs of nCD64 and mHLA-DR, nCD64 index and sepsis index (SI) with the TBNK panel were used to evaluate the immune status. Comparisons among multiple groups were performed with ANOVA one-way analysis. Receiver operating characteristics (ROC) curve analysis was performed to diagnose infection or sepsis. Univariate and multivariate logistic analysis examined associations of the immune status with infection.

Results

MESFs of nCD64 and mHLA-DR measured by four protocols had excellent reliability (ICCs 0.993 and 0.957, respectively). The nCD64, CD64 index and SI in infection group were significantly higher than those of stable KTRs group. Patients with sepsis had lower mHLA-DR but higher SI than non-sepsis patients. ROC analysis indicated that nCD64 had the highest area under the curve (AUC) for infection, and that mHLA-DR had the highest AUC for sepsis. Logistic analysis indicated that nCD64 > 3089 and B cells counts were independent risk factors for infection.

Conclusion

The standardization of nCD64 and mHLA-DR made it available for widespread application. MESFs of nCD64 and mHLA-DR had good diagnostic performance on infection and sepsis, respectively, which could be promising indicators for immune status of KTRs and contributed to individualized treatment.

Construction of Autophagy-Related Gene Classifier for Early Diagnosis, Prognosis and Predicting Immune Microenvironment Features in Sepsis by Machine Learning Algorithms

Background

The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood.

Methods

A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier.

Results

A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein.

Conclusion

Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

Immune checkpoint inhibitors for the treatment of sepsis:insights from preclinical and clinical development

INTRODUCTION

Sepsis represents one-fifth of all deaths worldwide and is associated with huge costs. Regarding disease progression, it is now well established that sepsis induces a state of acquired immunosuppression, with an increased risk of secondary infections that contributes to patients' worsening. Thus, tackling sepsis-induced immunosuppression represents a promising perspective.

AREAS COVERED

Of mechanisms responsible for sepsis-induced immunosuppression, the increased expression of co-inhibitory receptors (aka immune checkpoint) such as PD-1, CTLA4, TIM-3, LAG-3 or BTLA and their ligands recently received considerable interest since their inhibition, thanks to the so-called checkpoint inhibitors (CPI), provided astonishing results in cancer by rebooting immune functions. This review reports on the first landmarks of these molecules in sepsis. We introduce them in terms of basic immunology in line with sepsis pathophysiology both in experimental models and observational works and assess the first human clinical studies.

EXPERT OPINION

Preclinical results are positive and the first human clinical trials, although currently limited to the early phase, showed a beneficial effect on immunological functions and/or markers and suggested that tolerance of CPIs side effects, mainly auto-immune disorders, is acceptable in sepsis. Elsewhere, in some specific infections leading to ICU admission (or occurring during ICU stay), such as fungal infections, preliminary convincing case reports have been published. Overall, the first results regarding CPIs in sepsis appear encouraging. However, further efforts are warranted, especially in defining the right patients to be treated (i.e., in an individualized approach) and establishing the optimal time to start an immune restoration. Larger trials are now mandatory to confirm CPIs' potential in sepsis.

nCD64 index as a novel inflammatory indicator for the early prediction of prognosis in infectious and non-infectious inflammatory diseases: An observational study of febrile patients

Background

Generally, febrile patients admitted to the Department of Infectious Diseases, Fudan University Affiliated Huashan Hospital, China may eventually be diagnosed as infectious (ID) or non-infectious inflammatory diseases (NIID). Furthermore, mortality from sepsis remains incredibly high. Thus, early diagnosis and prognosis evaluation of sepsis is necessary. Here, we investigated neutrophil (n)CD64 index profile in a cohort of febrile patients and explored its diagnostic and prognostic value in ID and NIID.

Methods

This observational cohort study enrolled 348 febrile patients from the Emergency Department and Department of Infectious Diseases. nCD64 index were detected using flow cytometry, and dynamically measured at different timepoints during follow-up. Procalcitonin (PCT), C-reactive protein (CRP), and ferritin levels were measured routinely. Finally, the diagnostic and prognostic value of nCD64 index were evaluated by receiver operating characteristic (ROC) analysis and Kaplan-Meier curve analysis.

Results

Of included 348 febrile patients, 238, 81, and 29 were categorized into ID, NIID, and lymphoma groups, respectively. In ID patients, both SOFA score and infection site had impact on nCD64 index expression. In NIID patients, adult-onset Still’s disease patients had the highest nCD64 index value, however, nCD64 index couldn’t distinguish between ID and NIID. Regardless of the site of infection, nCD64 index was significantly higher in bacterial and viral infections than in fungal infections, but it could not discriminate between bacterial and viral infections. In bloodstream infections, gram-negative (G-) bacterial infections showed an obvious increase in nCD64 index compared to that of gram-positive (G+) bacterial infections. nCD64 index has the potential to be a biomarker for distinguishing between DNA and RNA virus infections. The routine measurement of nCD64 index can facilitate septic shock diagnosis and predict 28-day hospital mortality in patients with sepsis. Serial monitoring of nCD64 index in patients with sepsis is helpful for evaluating prognosis and treatment efficacy. Notably, nCD64 index is more sensitive to predict disease progression and monitor glucocorticoid treatment in patients with NIID.

Conclusions

nCD64 index can be used to predict 28-day hospital mortality in patients with sepsis and to evaluate the prognosis. Serial determinations of nCD64 index can be used to predict and monitor disease progression in patients with NIID.

Biomarkers Predicting Tissue Pharmacokinetics of Antimicrobials in Sepsis: A Review

The pathophysiology of sepsis alters drug pharmacokinetics, resulting in inadequate drug exposure and target-site concentration. Suboptimal exposure leads to treatment failure and the development of antimicrobial resistance. Therefore, we seek to optimize antimicrobial therapy in sepsis by selecting the right drug and the correct dosage. A prerequisite for achieving this goal is characterization and understanding of the mechanisms of pharmacokinetic alterations. However, most infections take place not in blood but in different body compartments. Since tissue pharmacokinetic assessment is not feasible in daily practice, we need to tailor antibiotic treatment according to the specific patient’s pathophysiological processes. The complex pathophysiology of sepsis and the ineffectiveness of current targeted therapies suggest that treatments guided by biomarkers predicting target-site concentration could provide a new therapeutic strategy. Inflammation, endothelial and coagulation activation markers, and blood flow parameters might be indicators of impaired tissue distribution. Moreover, hepatic and renal dysfunction biomarkers can predict not only drug metabolism and clearance but also drug distribution. Identification of the right biomarkers can direct drug dosing and provide timely feedback on its effectiveness. Therefore, this might decrease antibiotic resistance and the mortality of critically ill patients. This article fills the literature gap by characterizing patient biomarkers that might be used to predict unbound plasma-to-tissue drug distribution in critically ill patients. Although all biomarkers must be clinically evaluated with the ultimate goal of combining them in a clinically feasible scoring system, we support the concept that the appropriate biomarkers could be used to direct targeted antibiotic dosing.

Immunomonitoring of Monocyte and Neutrophil Function in Critically Ill Patients: From Sepsis and/or Trauma to COVID-19

Immune cells and mediators play a crucial role in the critical care setting but are understudied. This review explores the concept of sepsis and/or injury-induced immunosuppression and immuno-inflammatory response in COVID-19 and reiterates the need for more accurate functional immunomonitoring of monocyte and neutrophil function in these critically ill patients. in addition, the feasibility of circulating and cell-surface immune biomarkers as predictors of infection and/or outcome in critically ill patients is explored. It is clear that, for critically ill, one size does not fit all and that immune phenotyping of critically ill patients may allow the development of a more personalized approach with tailored immunotherapy for the specific patient. In addition, at this point in time, caution is advised regarding the quality of evidence of some COVID-19 studies in the literature.

SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care

Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22-2.77] adjusted for age and sex). RNAemia is comparable in performance to the best protein predictors. Mannose binding lectin 2 and pentraxin-3 (PTX3), two activators of the complement pathway of the innate immune system, are positively associated with mortality. Machine learning identified 'Age, RNAemia' and 'Age, PTX3' as the best binary signatures associated with 28-day ICU mortality. In longitudinal comparisons, COVID-19 ICU patients have a distinct proteomic trajectory associated with mortality, with recovery of many liver-derived proteins indicating survival. Finally, proteins of the complement system and galectin-3-binding protein (LGALS3BP) are identified as interaction partners of SARS-CoV-2 spike glycoprotein. LGALS3BP overexpression inhibits spike-pseudoparticle uptake and spike-induced cell-cell fusion in vitro.

Sepsis-Pathophysiology and Therapeutic Concepts

Sepsis is a life-threatening condition and a global disease burden. Today, the heterogeneous syndrome is defined as severe organ dysfunction caused by a dysregulated host response to infection, with renewed emphasis on immune pathophysiology. Despite all efforts of experimental and clinical research during the last three decades, the ability to positively influence course and outcome of the syndrome remains limited. Evidence-based therapy still consists of basic causal and supportive measures, while adjuvant interventions such as blood purification or targeted immunotherapy largely remain without proof of effectiveness so far. With this review, we aim to provide an overview of sepsis immune pathophysiology, to update the choice of therapeutic approaches targeting different immunological mechanisms in the course of sepsis and septic shock, and to call for a paradigm shift from the pathogen to the host response as a potentially more promising angle.

Optimal combination of early biomarkers for infection and sepsis diagnosis in the emergency department: The BIPS study

OBJECTIVE

To define the best combination of biomarkers for the diagnosis of infection and sepsis in the emergency room.

METHODS

In this prospective study, consecutive patients with a suspicion of infection in the emergency room were included. Eighteen different biomarkers measured in plasma, and twelve biomarkers measured on monocytes, neutrophils, B and T-lymphocytes were studied and the best combinations determined by a gradient tree boosting approach.

RESULTS

Overall, 291 patients were included and analysed, 148 with bacterial infection, and 47 with viral infection. The best biomarker combination which first allowed the diagnosis of bacterial infection, included HLA-DR (human leukocyte antigen DR) on monocytes, MerTk (Myeloid-epithelial-reproductive tyrosine kinase) on neutrophils and plasma metaloproteinase-8 (MMP8) with an area under the curve (AUC) = 0.94 [95% confidence interval (IC95): 0.91;0.97]. Among patients in whom a bacterial infection was excluded, the combination of CD64 expression, and CD24 on neutrophils and CX3CR1 on monocytes ended to an AUC = 0.98 [0.96;1] to define those with a viral infection.

CONCLUSION

In a convenient cohort of patients admitted with a suspicion of infection, two different combinations of plasma and cell surface biomarkers were performant to identify bacterial and viral infection.

PD-1 blockade improves Kupffer cell bacterial clearance in acute liver injury

Patients with acute liver failure (ALF) have systemic innate immune suppression and increased susceptibility to infections. Programmed cell death 1 (PD-1) expression by macrophages has been associated with immune suppression during sepsis and cancer. We therefore examined the role of the programmed cell death 1/programmed death ligand 1 (PD-1/PD-L1) pathway in regulating Kupffer cell (KC) inflammatory and antimicrobial responses in acetaminophen-induced (APAP-induced) acute liver injury. Using intravital imaging and flow cytometry, we found impaired KC bacterial clearance and systemic bacterial dissemination in mice with liver injury. We detected increased PD-1 and PD-L1 expression in KCs and lymphocyte subsets, respectively, during injury resolution. Gene expression profiling of PD-1⁺ KCs revealed an immune-suppressive profile and reduced pathogen responses. Compared with WT mice, PD-1–deficient mice and anti–PD-1–treated mice with liver injury showed improved KC bacterial clearance, a reduced tissue bacterial load, and protection from sepsis. Blood samples from patients with ALF revealed enhanced PD-1 and PD-L1 expression by monocytes and lymphocytes, respectively, and that soluble PD-L1 plasma levels could predict outcomes and sepsis. PD-1 in vitro blockade restored monocyte functionality. Our study describes a role for the PD-1/PD-L1 axis in suppressing KC and monocyte antimicrobial responses after liver injury and identifies anti–PD-1 immunotherapy as a strategy to reduce infection susceptibility in ALF.

Correlation of Immunoglobulins and Lymphocytes Levels With the Clinical and Microbiological Response of Septic Patients With Gram-Negative Bacteremia

Background

Immunoglobulins (Igs) and cells of the innate and adaptive immune systems play a critical role in a host's response to sepsis. The aim of this study was to investigate the possible differences in the levels of Igs, white blood cells (WBCs), and T and B lymphocytes cells in relation to the microbiological and clinical responses of patients with sepsis or septic shock from carbapenem non-susceptible Gram-negative bacteria (CnS-GNB).

Methods

This pilot cohort study involved 24 hospitalized patients with sepsis or septic shock due to bacteremia from CnS-GNB. The microbiological and clinical responses of the patients were evaluated in relation to their blood levels of IgA, IgE, IgM and IgG, as well as WBCs and subpopulations of T and B cells upon sepsis diagnosis. A microbiological response was determined as clearance of bacteremia at 14 days of active antibiotic treatment for the isolated bacterial pathogen. Clinical response was defined as the resolution of all clinical and laboratory signs of infection and sepsis at 14 days of active antibiotic treatment for the isolated pathogen.

Results

From the 24 patients included in the study 18 (75%) and six patients (25%) presented and did not present microbiological response respectively, while 16 patients presented clinical response (64%) and eight patients (36%) did not have clinical response. The levels of the Igs did not show statistically significant differences between patients with sepsis from CnS-GNB bacteremia who exhibited microbiological or clinical response. There were also no statistically significant differences in the levels of WBCs and the subpopulations of T and B cells levels for these patients (P > 0.05). According to this pilot study, peripheral blood Igs and lymphocyte subpopulations levels do not affect the clinical and microbiological response of septic patients with bacteremia from CnS-GNB.

Conclusions

In patients with sepsis or septic shock from CnSGNB, there were no differences in the levels of Igs, circulating WBCs and T and B cells subpopulations between those with microbiological or clinical response and non-responders.

Diagnosis and mortality prediction of sepsis via lysophosphatidylcholine 16:0 measured by MALDI-TOF MS

Sepsis remains a critical problem with high mortality worldwide, but there is still a lack of reliable biomarkers. We aimed to evaluate the serum lysophosphatidylcholine (LPC) 16:0 as a biomarker of sepsis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Patients admitted to intensive care unit at Severance Hospital from March 2017 through June 2018 were prospectively enrolled. The inclusion criteria were the fulfillment of at least two criteria of systemic inflammatory response syndrome (SIRS) or the presence of sepsis. Of the 127 patients, 14 had non-infectious SIRS, 41 had sepsis, and 72 had septic shock. The mean serum LPC 16:0 concentration (µmol/L) in non-infectious SIRS was significantly higher than in patients with sepsis and septic shock (101.1 vs. 48.92, p < 0.05; 101.1 vs. 25.88, p < 0.001, respectively). The area under the curve (AUC) predicting 28-day mortality using ΔLPC16:0 (D1-D0) levels was 0.7, which was comparable with the APACHE II score (AUC 0.692) and SOFA score (AUC 0.67). Mechanical ventilation, CRRT, lactate, Δ LPC16:0 (D1-D0) less than the cut-off value were significantly associated with 28-day mortality in multivariable analysis. Our results suggest that LPC16:0 could be a useful biomarker for sepsis diagnosis and mortality prediction in ICU patients.

Use of Intravenous Immunoglobulins in Sepsis Therapy-A Clinical View

Sepsis is a life-threatening organ dysfunction, defined by a dysregulated host immune response to infection. During sepsis, the finely tuned system of immunity, inflammation and anti-inflammation is disturbed in a variety of ways. Both pro-inflammatory and anti-inflammatory pathways are upregulated, activation of the coagulation cascade and complement and sepsis-induced lymphopenia occur. Due to the manifold interactions in this network, the use of IgM-enriched intravenous immunoglobulins seems to be a promising therapeutic approach. Unfortunately, there is still a lack of evidence-based data to answer the important questions of appropriate patient populations, optimal timing and dosage of intravenous immunoglobulins. With this review, we aim to provide an overview of the role of immunoglobulins, with emphasis on IgM-enriched formulations, in the therapy of adult patients with sepsis and septic shock.

Neutrophil and monocyte receptor expression in patients with sepsis: implications for diagnosis and prognosis of sepsis

Understanding the complex immune responses in sepsis is crucial to provide insight into the clinical syndrome. We evaluated the changes in the surface receptors of the cells of innate immunity, neutrophils and monocytes, in patients with sepsis. Since sepsis remains a clinical challenge, we aimed to assess the significance of altered receptor expression in diagnosis and prognosis. Critically ill patients with sepsis (n=31) were investigated for the expression of receptors for IgG heavy chain CD64 and CD16 on neutrophils and CD64 and the lipopolysaccharide receptor CD14 on monocytes by flow cytometry and compared to 23 patients with no sepsis. Patients with sepsis had increased expression of neutrophil CD64. Neutrophil CD64 was specific for discriminating patients with sepsis but showed weak sensitivity. When integrated in a scoring system, neutrophil CD64 in combination with C-reactive protein (CRP) and SOFA score showed a diagnostic accuracy of 0.93 for sepsis and significantly predicted increased mortality risk. While neutrophil CD16 did not discriminate for sepsis, decreased expression was associated with increased mortality risk. In contrast, monocyte CD64 and CD14 expression was unaltered in sepsis and was not associated with mortality risk. Our study demonstrates that unlike monocytes, neutrophil receptor expression is altered in patients with sepsis receiving intensive care. It is promising to apply a combination approach to diagnose sepsis especially in time-limited conditions.

Evaluation of synovial fluid neutrophil CD64 index as a screening biomarker of prosthetic joint infection

AIMS

Prosthetic joint infection (PJI) remains a major clinical challenge. Neutrophil CD64 index, Fc-gamma receptor 1 (FcγR1), plays an important role in mediating inflammation of bacterial infections and therefore could be a valuable biomarker for PJI. The aim of this study is to compare the neutrophil CD64 index in synovial and blood diagnostic ability with the standard clinical tests for discrimination PJI and aseptic implant failure.

METHODS

A total of 50 patients undergoing revision hip and knee arthroplasty were enrolled into a prospective study. According to Musculoskeletal Infection Society (MSIS) criteria, 25 patients were classified as infected and 25 as not infected. In all patients, neutrophil CD64 index and percentage of polymorphonuclear neutrophils (PMN%) in synovial fluid, serum CRP, ESR, and serum CD64 index levels were measured preoperatively. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were analyzed for each biomarker.

RESULTS

Serum CD64 index showed no significant difference between the two groups (p = 0.091). Synovial fluid CD64 index and PMN% discriminated good differentiation between groups of PJI and aseptic failure with AUC of 0.946 (95% confidence interval (CI) 0.842 to 0.990) and 0.938 (95% CI 0.832 to 0.987) separately. The optimal threshold value of synovial CD64 index for the diagnosis of PJI was 0.85, with a sensitivity of 92.00%, a specificity of 96.00%, and diagnostic odds ratio (DOR) of 227.11.

CONCLUSION

The present study demonstrates that CD64 index in synovial fluid could be a promising laboratory marker for screening PJI. The cut-off values of 0.85 for synovial CD64 index has the potential to distinguish aseptic failure from PJI. Cite this article: Bone Joint J 2020;102-B(4):463-469.

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

PURPOSE

Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.

METHODS

A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.

RESULTS

After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.

CONCLUSION

This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

Differential Gene Expression in Circulating CD14+ Monocytes Indicates the Prognosis of Critically Ill Patients with Sepsis

Critical illness and sepsis are characterized by drastic changes in the systemic innate immune response, particularly involving monocytes. The exact monocyte activation profile during sepsis, however, has remained obscure. Therefore, we prospectively analyzed the gene expression profile of circulating CD14⁺ monocytes from healthy volunteers (n = 54) and intensive care unit (ICU) patients (n = 76), of which n = 36 had sepsis. RNA sequencing of selected samples revealed that monocytes from septic ICU patients display a peculiar activation pattern, which resembles characteristic functional stages of monocyte-derived macrophages and is distinct from controls or non-sepsis ICU patients. Focusing on 55 highly variable genes selected for further investigation, arachidonate 5-lipoxygenase-activating protein (ALOX5AP) was highly upregulated in monocytes of ICU patients and only normalized during 7 days in the ICU in non-sepsis patients. Strikingly, low monocytic guanine nucleotide exchange factor 10-like protein (ARHGEF10L) mRNA expression was associated with the disease severity and mortality of ICU patients. Collectively, our comprehensive analysis of circulating monocytes in critically ill patients revealed a distinct activation pattern, particularly in ICU patients with sepsis. The association with disease severity, the longitudinal recovery or lack thereof during the ICU stay, and the association with prognosis indicate the clinical relevance of monocytic gene expression profiles during sepsis.

How Clinical Flow Cytometry Rebooted Sepsis Immunology

On May 2017, the World Health Organization (WHO) recognized sepsis as a global health priority by adopting a resolution to improve the prevention, diagnosis, and management of this deadly disease. While it has long been known that sepsis deeply perturbs immune homeostasis by inducing a tremendous systemic inflammatory response, pivotal observations based on clinical flow cytometry indicate that sepsis indeed initiates a more complex immune response that varies over time, with the concomitant occurrence of both pro- and anti-inflammatory mechanisms. As a resultant, some septic patients enter a stage of protracted immunosuppression. This paved the way for immunostimulation approaches in sepsis. Clinical flow cytometry permitted this evolution by drawing a new picture of pathophysiology and reshaping immune trajectories in patients. Additional information from cytometry by time of flight mass cytometry and other high-dimensional flow cytometry platform should rapidly enrich our understanding of this complex disease. This review reports on landmarks of clinical flow cytometry in sepsis and how this single-cell analysis technique permitted to breach the wall of decades of unfruitful anti-inflammatory-based clinical trials in sepsis. © 2019 International Society for Advancement of Cytometry.

Prognostic Relevance of Altered Lymphocyte Subpopulations in Critical Illness and Sepsis

Lymphopenia and functional defects in lymphocytes may impact the prognosis in patients with critical illness or sepsis. Therefore, we prospectively analyzed peripheral blood leukocytes from 63 healthy volunteers, 50 non-critically ill standard care (SC) patients with infections, and 105 intensive care unit (ICU) patients (52 with sepsis, 53 without sepsis) using flow cytometry. Compared to healthy volunteers, SC and ICU patients showed significant leukocytosis, especially in sepsis, while lymphocyte numbers were significantly decreased. All major lymphocyte populations (B, T, and natural killer (NK) cells) decreased in ICU patients. However, we observed a relative reduction of T cells, alongside decreased CD8+ T cells, in critically ill patients, independent of sepsis. High absolute T cell counts (>0.36/nL) at ICU admission were associated with a significantly reduced mortality, independent of patient’s age. Moreover, patients that survived ICU treatment showed dynamic changes within 48 h towards restoration of lymphopenia and T cell depletion, while non-surviving patients failed to restore lymphocyte counts. In conclusion, the flow-cytometric analysis of peripheral blood revealed striking changes in circulating lymphocyte subsets in critically ill patients, independent of sepsis. Lymphopenia and T cell depletion at ICU admission were associated with increased mortality, supporting their relevance as predictive biomarkers and potential therapeutic targets in intensive care medicine.

Monocyte HLA-DR Assessment by a Novel Point-of-Care Device Is Feasible for Early Identification of ICU Patients With Complicated Courses-A Proof-of-Principle Study

Background: Critically ill patients, especially following trauma or extensive surgery, experience a systemic immune response, consisting of a pro-inflammatory as well as a counterbalancing anti-inflammatory response. Pro-inflammation is necessary for the initiation of homeostatic control and wound healing of the organism. However, when the counterbalancing mechanisms dominate, a condition of secondary immunodeficiency occurs, which renders the patient susceptible for opportunistic or secondary infections. However, the incidence of this condition is yet illusive.

Methods: For a period of 3 months (May to July 2017), 110 consecutive patients admitted to the surgical ICU of the Heidelberg University Hospital, a tertiary university hospital, were enrolled in the study. Monocyte HLA-DR (mHLA-DR), a long-known surrogate of monocyte function, was assessed quantitatively once on admission utilizing a novel point-of-care flow cytometer with single-use cartridges (Accelix system). Patients were followed up for further 28 days and data on ICU stay, antibiotic therapy, microbiological findings, and mechanical ventilation were recorded. Statistical analysis was performed to evaluate the incidence of immunosuppression—defined by different thresholds—as well as its consequence in terms of outcome and clinical course.

Results: Depending on the HLA-DR threshold applied for stratification (≤8,000/≤5,000/≤2,000 molecules/cell), a large group of patients (85.5/68.2/40.0%) already presented with a robust decrease of HLA-DR on admission, independent of the cause for critical illness. Analyzed for survival, neither threshold was able to stratify patients with a higher mortality. However, both thresholds of 2,000 and 5,000 were able to discriminate patients with longer ICU stay, ventilation time and duration of antibiotic therapy, as well as higher count of microbiological findings. Moreover, a mHLA-DR value ≤2,000 molecules/cell was associated with higher incidence of overall antibiotic therapy.

Conclusion: Single assessment of mHLA-DR using a novel point-of-care flow cytometer is able to stratify patients according to their risk of a complicated course. Therefore, this device overcomes the technical boundaries for measuring cellular biomarkers and paves the way for future studies involving personalized immunotherapy to patients with a high immunological risk profile independent of their background.

Trial Registration: German Clinical Trials Register; ID: DRKS00012348.