Identification of a sub-group of critically ill patients with high risk of intensive care unit-acquired infections and poor clinical course using a transcriptomic score.

BACKGROUND: The development of stratification tools based on the assessment of circulating mRNA of genes involved in the immune response is constrained by the heterogeneity of septic patients. The aim of this study is to develop a transcriptomic score based on a pragmatic combination of immune-related genes detected with a prototype multiplex PCR tool. METHODS: As training cohort, we used the gene expression dataset obtained from 176 critically ill patients enrolled in the REALISM study (NCT02638779) with various etiologies and still hospitalized in intensive care unit (ICU) at day 5-7. Based on the performances of each gene taken independently to identify patients developing ICU-acquired infections (ICU-AI) after day 5-7, we built an unweighted score assuming the independence of each gene. We then determined the performances of this score to identify a subgroup of patients at high risk to develop ICU-AI, and both longer ICU length of stay and mortality of this high-risk group were assessed. Finally, we validated the effectiveness of this score in a retrospective cohort of 257 septic patients. RESULTS: This transcriptomic score (TScore) enabled the identification of a high-risk group of patients (49%) with an increased rate of ICU-AI when compared to the low-risk group (49% vs. 4%, respectively), with longer ICU length of stay (13 days [95% CI 8-30] vs. 7 days [95% CI 6-9], p < 0.001) and higher ICU mortality (15% vs. 2%). High-risk patients exhibited biological features of immune suppression with low monocytic HLA-DR levels, higher immature neutrophils rates and higher IL10 concentrations. Using the TScore, we identified 160 high-risk patients (62%) in the validation cohort, with 30% of ICU-AI (vs. 18% in the low-risk group, p = 0.06), and significantly higher mortality and longer ICU length of stay. CONCLUSIONS: The transcriptomic score provides a useful and reliable companion diagnostic tool to further develop immune modulating drugs in sepsis in the context of personalized medicine.

Sepsis in the Pediatric Cardiac Intensive Care Unit: An Updated Review.

Sepsis remains among the most common causes of mortality in children with congenital heart disease (CHD). Extensive literature is available regarding managing sepsis in pediatric patients without CHD. Because the cardiovascular pathophysiology of children with CHD differs entirely from their typical peers, the available diagnosis and management recommendations for sepsis cannot be implemented directly in children with CHD. This review discusses the risk factors, etiopathogenesis, available diagnostic tools, resuscitation protocols, and anesthetic management of pediatric patients suffering from various congenital cardiac lesions. Further research should focus on establishing a standard guideline for managing children with CHD with sepsis and septic shock admitted to the intensive care unit.

Host Response Biomarkers for Sepsis in the Emergency Room.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

The utility of the Rapid Emergency Medicine Score (REMS) compared with three other early warning scores in predicting in-hospital mortality among COVID-19 patients in the emergency department: a multicenter validation study.

BACKGROUND: Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes of COVID-19 in the Emergency Department (ED), including the quick Sequential Organ Failure Assessment (qSOFA), the Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has not been widely validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of qSOFA, MEWS, and NEWS for predicting mortality in emergency COVID-19 patients. METHODS: We conducted a multi-center retrospective study at five EDs of various levels of care in Thailand. Adult patients visiting the ED who tested positive for COVID-19 prior to ED arrival or within the index hospital visit between January and December 2021 were included. Their EWSs at ED arrival were calculated and analysed. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. RESULTS: A total of 978 patients were included in the study; 254 (26%) died at hospital discharge, and 155 (15.8%) were intubated. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.771 (95% confidence interval (CI) 0.738, 0.804)), which was significantly higher than qSOFA (AUROC 0.620 (95%CI 0.589, 0.651); p < 0.001), MEWS (AUROC 0.657 (95%CI 0.619, 0.694); p < 0.001), and NEWS (AUROC 0.732 (95%CI 0.697, 0.767); p = 0.037). REMS was also the best EWS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cutoff. REMS also performed better than other EWSs for mechanical ventilation. CONCLUSION: REMS was the early warning score with the highest prognostic utility as it outperformed qSOFA, MEWS, and NEWS in predicting in-hospital mortality in COVID-19 patients in the ED.

Recent nanoengineered diagnostic and therapeutic advancements in management of Sepsis.

COVID-19 acquired symptoms have affected the worldwide population and increased the load of Intensive care unit (ICU) patient admissions. A large number of patients admitted to ICU end with a deadly fate of mortality. A high mortality rate of patients was reported with hospital-acquired septic shock that leads to multiple organ failures and ultimately ends with death. The patients who overcome this septic shock suffer from morbidity that also affects their caretakers. To overcome these situations, scientists are exploring progressive theragnostic techniques with advanced techniques based on biosensors, biomarkers, biozymes, vesicles, and others. These advanced techniques pave the novel way for early detection of sepsis-associated symptoms and timely treatment with appropriate antibiotics and immunomodulators and prevent the undue effect on other parts of the body. There are other techniques like externally modulated electric-based devices working on the principle of piezoelectric mechanism that not only sense the endotoxin levels but also target them with a loaded antibiotic to neutralize the onset of inflammatory response. Recently researchers have developed a lipopolysaccharide (LPS) neutralizing cartridge that not only senses the LPS but also appropriately neutralizes with dual mechanistic insights of antibiotic and anti-inflammatory effects. This review will highlight recent developments in the new nanotechnology-based approaches for the diagnosis and therapeutics of sepsis that is responsible for the high number of deaths of patients suffering from this critical disease.

Presepsin as a diagnostic and prognostic biomarker of severe bacterial infections and COVID-19.

We aimed to develop presepsin as a marker of diagnosis of severe infections of either bacterial and viral origin. The derivation cohort was recruited from 173 hospitalized patients with acute pancreatitis or post-operative fever or infection suspicion aggravated by at least one sign of the quick sequential organ failure assessment (qSOFA). The first validation cohort was recruited from 57 admissions at the emergency department with at least one qSOFA sign and the second validation cohort from 115 patients with COVID-19 pneumonia. Presepsin was measured in plasma by the PATHFAST assay. Concentrations more than 350 pg/ml had sensitivity 80.2% for sepsis diagnosis in the derivation cohort (adjusted odds ratio 4.47; p < 0.0001). In the derivation cohort, sensitivity for 28-day mortality prognosis was 91.5% (adjusted odds ratio 6.82; p: 0.001). Concentrations above 350 pg/ml had sensitivity 93.3% for the diagnosis of sepsis in the first validation cohort; this was 78.3% in the second validation cohort of COVID-19 aiming at the early diagnosis of acute respiratory distress syndrome necessitating mechanical ventilation. The respective sensitivity for 28-day mortality was 85.7% and 92.3%. Presepsin may be a universal biomarker for the diagnosis of severe infections of bacterial origin and prediction of unfavorable outcome.

Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

BACKGROUND: Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the extent of data drift and provide insights about its characteristics for sepsis onset prediction. This study will help elucidate the nature of data drift for prediction of sepsis and similar diseases. This may aid with the development of more effective patient monitoring systems that can stratify risk for dynamic disease states in hospitals. METHODS: We devise a series of simulations that measure the effects of data drift in patients with sepsis, using electronic health records (EHR). We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of the predictor variables (covariate shift), the change in the statistical relationship between the predictors and the target (concept shift), and the occurrence of a major healthcare event (major event) such as the COVID-19 pandemic. We measure the impact of data drift on model performances, identify the circumstances that necessitate model retraining, and compare the effects of different retraining methodologies and model architecture on the outcomes. We present the results for two different MLAs, eXtreme Gradient Boosting (XGB) and Recurrent Neural Network (RNN). RESULTS: Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area under the receiver operating characteristic curve (AUROC) at the end of the simulation period is 0.811 for the baseline XGB model and 0.868 for the retrained XGB model. In the covariate shift scenario, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.853 and 0.874 respectively. In the concept shift scenario and under the mixed labeling method, the retrained XGB models perform worse than the baseline model for most simulation steps. However, under the full relabeling method, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.852 and 0.877 respectively. The results for the RNN models were mixed, suggesting that retraining based on a fixed network architecture may be inadequate for an RNN. We also present the results in the form of other performance metrics such as the ratio of observed to expected probabilities (calibration) and the normalized rate of positive predictive values (PPV) by prevalence, referred to as lift, at a sensitivity of 0.8. CONCLUSION: Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates that a machine learning system for sepsis prediction will probably need less infrastructure for performance monitoring and retraining compared to other applications in which data drift is more frequent and continuous. Our results also show that in the event of a concept shift, a full overhaul of the sepsis prediction model may be necessary because it indicates a discrete change in the definition of sepsis labels, and mixing the labels for the sake of incremental training may not produce the desired results.

Immune and metabolic markers for identifying and investigating severe Coronavirus disease and Sepsis in children and young people (pSeP/COVID ChYP study): protocol for a prospective cohort study.

INTRODUCTION: Early recognition and appropriate management of paediatric sepsis are known to improve outcomes. A previous system's biology investigation of the systemic immune response in neonates to sepsis identified immune and metabolic markers that showed high accuracy for detecting bacterial infection. Further gene expression markers have also been reported previously in the paediatric age group for discriminating sepsis from control cases. More recently, specific gene signatures were identified to discriminate between COVID-19 and its associated inflammatory sequelae. Through the current prospective cohort study, we aim to evaluate immune and metabolic blood markers which discriminate between sepses (including COVID-19) from other acute illnesses in critically unwell children and young persons, up to 18 years of age. METHODS AND ANALYSIS: We describe a prospective cohort study for comparing the immune and metabolic whole-blood markers in patients with sepsis, COVID-19 and other illnesses. Clinical phenotyping and blood culture test results will provide a reference standard to evaluate the performance of blood markers from the research sample analysis. Serial sampling of whole blood (50 µL each) will be collected from children admitted to intensive care and with an acute illness to follow time dependent changes in biomarkers. An integrated lipidomics and RNASeq transcriptomics analyses will be conducted to evaluate immune-metabolic networks that discriminate sepsis and COVID-19 from other acute illnesses. This study received approval for deferred consent. ETHICS AND DISSEMINATION: The study has received research ethics committee approval from the Yorkshire and Humber Leeds West Research Ethics Committee 2 (reference 20/YH/0214; IRAS reference 250612). Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites. TRIAL REGISTRATION NUMBER: NCT04904523.

Metapneumovirus Infections are Uncommon in Infants Younger than 60 Days of Age Admitted for Sepsis Evaluation.

The epidemiology and clinical manifestations of human metapneumovirus are not well studied in infants younger than 60 days of age. In this retrospective review of infants admitted for sepsis evaluation, we identified HMPV less frequently than other viral etiologies via nasopharyngeal multiplex polymerase chain reaction testing; in only 16 (1.9%) infants. Two infants had apneic episodes, but none had wheezing.

Thiamine pharmaconutrition in sepsis: Monotherapy, combined therapy, or neither? Current evidence on safety and efficacy.

Sepsis is a life-threatening condition characterized by multiorgan dysfunction due to an exaggerated host response to infection associated with a homeostatic failure. In sepsis, different interventions, aimed at improving clinical outcomes, have been tested over the past decades. Among these most recent strategies, intravenous high-dose micronutrients (vitamins and/or trace elements) have been investigated. According to current knowledge, sepsis is characterized by low thiamine levels, which are associated with illness severity, hyperlactatemia, and poor clinical outcomes. However, caution is needed about the clinical interpretation of thiamine blood concentration in critically ill patients, and the inflammatory status, based on C-reactive protein levels, should always be measured. In sepsis, parenteral thiamine has been administered as monotherapy or in combination with vitamin C and corticosteroids. Nevertheless, most of those trials failed to report clinical benefits with high-dose thiamine. The purpose of this review is to summarize the biological properties of thiamine and to examine current knowledge regarding the safety and efficacy of high-dose thiamine as pharmaconutrition strategy when administering singly or in combination with other micronutrients in critically ill adult patients with sepsis or septic shock. Our examination of the most up-to-date evidence concludes that Recommended Daily Allowance supplementation is relatively safe for thiamine-deficient patients. However, current evidence does not support pharmaconutrition with high-dose thiamine as a single therapy or as combination therapy aimed at improving clinical outcomes in critically ill septic patients. The best nutrient combination still needs to be determined, based on the antioxidant micronutrient network and the multiple interactions among different vitamins and trace elements. In addition, a better understanding of the pharmacokinetic and pharmacodynamic profiles of intravenous thiamine is needed. Future well-designed and powered clinical trials are urgently warranted before any specific recommendations can be made regarding supplementation in the critical care setting.

Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.

Introduction: Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients. Methods: In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool. Results: We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients. Discussion: In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review.

PURPOSE OF REVIEW: Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking. RECENT FINDINGS: Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome. SUMMARY: There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.

Monocyte Distribution Width (MDW) as a biomarker of sepsis: An evidenced-based laboratory medicine approach.

Monocyte Distribution Width (MDW) is a new generation cell blood count parameter providing a measure of monocyte anisocytosis. In the last decades, it has emerged as a reliable biomarker of sepsis in the acute setting, especially emergency department, and intensive care unit. MDW has several advantages over commonly used sepsis biomarkers, including low-cost, ease and speed of measurement. The clinical usefulness of MDW has been established in several studies and some clinical laboratory medicines have already implemented it in their routine. In this article, we describe the analytical and clinical features of MDW to guide its appropriate use in clinical practice by integrating the research evidence with real-world laboratory experience. The proper use of a biomarker is critical for improving patients' care and outcome as well as ensuring healthcare quality.

Stratification of COVID-19 Severity Using SeptiCyte RAPID, a Novel Host Immune Response Test.

SeptiCyte® RAPID is a gene expression assay measuring the relative expression levels of host response genes PLA2G7 and PLAC8, indicative of a dysregulated immune response during sepsis. As severe forms of COVID-19 may be considered viral sepsis, we evaluated SeptiCyte RAPID in a series of 94 patients admitted to Foch Hospital (Suresnes, France) with proven SARS-CoV-2 infection. EDTA blood was collected in the emergency department (ED) in 67 cases, in the intensive care unit (ICU) in 23 cases and in conventional units in 4 cases. SeptiScore (0-15 scale) increased with COVID-19 severity. Patients in ICU had the highest SeptiScores, producing values comparable to 8 patients with culture-confirmed bacterial sepsis. Receiver operating characteristic (ROC) curve analysis had an area under the curve (AUC) of 0.81 for discriminating patients requiring ICU admission from patients who were immediately discharged or from patients requiring hospitalization in conventional units. SeptiScores increased with the extent of the lung injury. For 68 patients, a chest computed tomography (CT) scan was performed within 24 h of COVID-19 diagnosis. SeptiScore >7 suggested lung injury ≥50% (AUC = 0.86). SeptiCyte RAPID was compared to other biomarkers for discriminating Critical + Severe COVID-19 in ICU, versus Moderate + Mild COVID-19 not in ICU. The mean AUC for SeptiCyte RAPID was superior to that of any individual biomarker or combination thereof. In contrast to C-reactive protein (CRP), correlation of SeptiScore with lung injury was not impacted by treatment with anti-inflammatory agents. SeptiCyte RAPID can be a useful tool to identify patients with severe forms of COVID-19 in ED, as well as during follow-up.

Monocyte Distribution Width as a Diagnostic Marker for Infection: A Systematic Review and Meta-analysis.

BACKGROUND: Monocyte distribution width (MDW) is an emerging biomarker for infection. It is available easily and quickly as part of the CBC count, which is performed routinely on hospital admission. The increasing availability and promising results of MDW as a biomarker in sepsis has prompted an expansion of its use to other infectious diseases. RESEARCH QUESTION: What is the diagnostic performance of MDW across multiple infectious disease outcomes and care settings? STUDY DESIGN AND METHODS: A systematic review of the diagnostic performance of MDW across multiple infectious disease outcomes was conducted by searching PubMed, Embase, Scopus, and Web of Science through February 4, 2022. Meta-analysis was performed for outcomes with three or more reports identified (sepsis and COVID-19). Diagnostic performance measures were calculated for individual studies with pooled estimates created by linear mixed-effects models. RESULTS: We identified 29 studies meeting inclusion criteria. Most examined sepsis (19 studies) and COVID-19 (six studies). Pooled estimates of diagnostic performance for sepsis differed by reference standard (Second vs Third International Consensus Definitions for Sepsis and Septic Shock criteria) and tube anticoagulant used and ranged from an area under the receiver operating characteristic curve (AUC) of 0.74 to 0.94, with mean sensitivity of 0.69 to 0.79 and mean specificity of 0.57 to 0.86. For COVID-19, the pooled AUC of MDW was 0.76, mean sensitivity was 0.79, and mean specificity was 0.59. INTERPRETATION: MDW exhibited good diagnostic performance for sepsis and COVID-19. Diagnostic thresholds for sepsis should be chosen with consideration of reference standard and tube type used. TRIAL REGISTRY: Prospero; No.: CRD42020210074; URL: https://www.crd.york.ac.uk/prospero/.

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model.

OBJECTIVE: Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. METHODS: This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. RESULTS: A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. CONCLUSION: An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

Deep-learning model for screening sepsis using electrocardiography.

BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). METHODS: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. RESULTS: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). CONCLUSIONS: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.