Intracellular immune sensing promotes inflammation via gasdermin D-driven release of a lectin alarmin.

Inflammatory caspase sensing of cytosolic lipopolysaccharide (LPS) triggers pyroptosis and the concurrent release of damage-associated molecular patterns (DAMPs). Collectively, DAMPs are key determinants that shape the aftermath of inflammatory cell death. However, the identity and function of the individual DAMPs released are poorly defined. Our proteomics study revealed that cytosolic LPS sensing triggered the release of galectin-1, a ß-galactoside-binding lectin. Galectin-1 release is a common feature of inflammatory cell death, including necroptosis. In vivo studies using galectin-1-deficient mice, recombinant galectin-1 and galectin-1-neutralizing antibody showed that galectin-1 promotes inflammation and plays a detrimental role in LPS-induced lethality. Mechanistically, galectin-1 inhibition of CD45 (Ptprc) underlies its unfavorable role in endotoxin shock. Finally, we found increased galectin-1 in sera from human patients with sepsis. Overall, we uncovered galectin-1 as a bona fide DAMP released as a consequence of cytosolic LPS sensing, identifying a new outcome of inflammatory cell death.

The Value of Systemic Immune-Inflammation Index and T Cell Subsets in the Severity and Prognosis of Sepsis.

Systemic immune-inflammation index (SII) and T cell subsets show involvement in mortality risk in septic patients, and we explored their predictive value in sepsis. Subjects were categorized into the Sepsis (SP)/Septic Shock (SSP)/Septic Shock (SPS) groups. T cell subsets [T-helper (Th)1, Th2, regulatory T cells (Treg), Th17]/platelets (PLT)/neutrophils (NEU)/lymphocytes (LYM)/C-reactive protein (CRP)/procalcitonin (PCT)/interleukin (IL)-4/IL-10/fibrinogen (FIB) were measured by an automatic blood biochemical analyzer/flow cytometry/Countess II FL automatic blood cell analyzer, with SII calculated. The correlations between SII/T cell subsets with Acute Physiology and Chronic Health Evaluation (APACH) II/Sequential Organ Failure Assessment (SOFA) scores and the predictive value of SII/Th1/Th2 for septic diagnosis/prognosis were analyzed using Spearman/ROC curve/Kaplan-Meier. The three groups varied in PLT/NEU/LYM/CRP/PCT/IL-4/IL-10/FIB levels and APACH II/SOFA scores. Compared with the SP group, the other two groups showed elevated APACH II/SOFA scores and SII/Th1/Th2/Th17/Treg levels. SII/Th1/Th2 levels significantly positively correlated with APACH II/SOFA scores. SII/Th1/Th2 levels had high predictive value for septic diagnosis/prognosis, with their combination exhibiting higher predictive value. Septic patients with high SII/Th1/Th2 levels exhibited lower survival rates. Altogether, SII, Th1, and Th2 had good predictive value for the diagnosis and prognosis of patients with varying severity of sepsis, with their high levels increasing mortality in septic patients.

A culture-free biphasic approach for sensitive and rapid detection of pathogens in dried whole-blood matrix.

Blood stream infections (BSIs) cause high mortality, and their rapid detection remains a significant diagnostic challenge. Timely and informed administration of antibiotics can significantly improve patient outcomes. However, blood culture, which takes up to 5 d for a negative result, followed by PCR remains the gold standard in diagnosing BSI. Here, we introduce a new approach to blood-based diagnostics where large blood volumes can be rapidly dried, resulting in inactivation of the inhibitory components in blood. Further thermal treatments then generate a physical microscale and nanoscale fluidic network inside the dried matrix to allow access to target nucleic acid. The amplification enzymes and primers initiate the reaction within the dried blood matrix through these networks, precluding any need for conventional nucleic acid purification. High heme background is confined to the solid phase, while amplicons are enriched in the clear supernatant (liquid phase), giving fluorescence change comparable to purified DNA reactions. We demonstrate single-molecule sensitivity using a loop-mediated isothermal amplification reaction in our platform and detect a broad spectrum of pathogens, including gram-positive methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteria, gram-negative Escherichia coli bacteria, and Candida albicans (fungus) from whole blood with a limit of detection (LOD) of 1.2 colony-forming units (CFU)/mL from 0.8 to 1 mL of starting blood volume. We validated our assay using 63 clinical samples (100% sensitivity and specificity) and significantly reduced sample-to-result time from over 20 h to <2.5 h. The reduction in instrumentation complexity and costs compared to blood culture and alternate molecular diagnostic platforms can have broad applications in healthcare systems in developed world and resource-limited settings.

Ultrasensitive and Rapid Detection of Procalcitonin via Waveguide-Enhanced Nanogold-Linked Immunosorbent Assay for Early Sepsis Diagnosis.

Sepsis, a life-threatening inflammatory response, demands economical, accurate, and rapid detection of biomarkers during the critical "golden hour" to reduce the patient mortality rate. Here, we demonstrate a cost-effective waveguide-enhanced nanogold-linked immunosorbent assay (WENLISA) based on nanoplasmonic waveguide biosensors for the rapid and sensitive detection of procalcitonin (PCT), a sepsis-related inflammatory biomarker. To enhance the limit of detection (LOD), we employed sandwich assays using immobilized capture antibodies and detection antibodies conjugated to gold nanoparticles to bind the target analyte, leading to a significant evanescent wave redistribution and strong nanoplasmonic absorption near the waveguide surface. Experimentally, we detected PCT for a wide linear response range of 0.1 pg/mL to 1 ng/mL with a record-low LOD of 48.7 fg/mL (3.74 fM) in 8 min. Furthermore, WENLISA has successfully identified PCT levels in the blood plasma of patients with sepsis and healthy individuals, offering a promising technology for early sepsis diagnosis.

Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes.

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

Improving Sepsis Outcomes in the Era of Pay-for-Performance and Electronic Quality Measures: A Joint IDSA/ACEP/PIDS/SHEA/SHM/SIDP Position Paper.

The Centers for Medicare & Medicaid Services (CMS) introduced the Severe Sepsis/Septic Shock Management Bundle (SEP-1) as a pay-for-reporting measure in 2015 and is now planning to make it a pay-for-performance measure by incorporating it into the Hospital Value-Based Purchasing Program. This joint IDSA/ACEP/PIDS/SHEA/SHM/SIPD position paper highlights concerns with this change. Multiple studies indicate that SEP-1 implementation was associated with increased broad-spectrum antibiotic use, lactate measurements, and aggressive fluid resuscitation for patients with suspected sepsis but not with decreased mortality rates. Increased focus on SEP-1 risks further diverting attention and resources from more effective measures and comprehensive sepsis care. We recommend retiring SEP-1 rather than using it in a payment model and shifting instead to new sepsis metrics that focus on patient outcomes. CMS is developing a community-onset sepsis 30-day mortality electronic clinical quality measure (eCQM) that is an important step in this direction. The eCQM preliminarily identifies sepsis using systemic inflammatory response syndrome (SIRS) criteria, antibiotic administrations or diagnosis codes for infection or sepsis, and clinical indicators of acute organ dysfunction. We support the eCQM but recommend removing SIRS criteria and diagnosis codes to streamline implementation, decrease variability between hospitals, maintain vigilance for patients with sepsis but without SIRS, and avoid promoting antibiotic use in uninfected patients with SIRS. We further advocate for CMS to harmonize the eCQM with the Centers for Disease Control and Prevention's (CDC) Adult Sepsis Event surveillance metric to promote unity in federal measures, decrease reporting burden for hospitals, and facilitate shared prevention initiatives. These steps will result in a more robust measure that will encourage hospitals to pay more attention to the full breadth of sepsis care, stimulate new innovations in diagnosis and treatment, and ultimately bring us closer to our shared goal of improving outcomes for patients.

Rare Spiroplasma Bloodstream Infection in Patient after Surgery, China, 2022.

We report a case of Spiroplasma bloodstream infection in a patient in China who developed pulmonary infection, acute respiratory distress syndrome, sepsis, and septic shock after emergency surgery for type A aortic dissection. One organism closely related to Spiroplasma eriocheiris was isolated from blood culture and identified by whole-genome sequencing.

Identification of four mitochondria-related genes in sepsis based on RNA sequencing technology.

OBJECTIVES: The purpose of this study was to identify and analyze the mitochondrial genes associated with sepsis patients in order to elucidate the underlying mechanism of sepsis immunity and provide new ideas for the clinical treatment of sepsis. METHODS: The hospitalized cases of sepsis (n = 20) and systemic inflammatory response syndrome (SIRS) (n = 12) admitted to the Emergency Intensive Care Unit (EICU) of the Affiliated Hospital of Southwest Medical University from January 2019 to December 2019 were collected consecutively. RNA-seq was used to sequence the RNA (mRNA) of peripheral blood cells. Bioinformatics techniques were used to screen and identify differentially expressed RNAs, with an absolute value of fold change (FC) greater than or equal to 1.2 and a false discovery rate (FDR) less than 0.05. At the same time, mitochondrial genes were obtained from the MitoCarta 3.0 database. Differential genes were then intersected with mitochondrial genes. The resulting crossover genes were subjected to GO, KEGG, and PPI analysis. Subsequently, the GSE65682 dataset was downloaded from the GEO database for survival analysis to assess the prognostic value of core genes, and GSE67652 was downloaded for ROC curve analysis to validate the diagnostic value of core genes. Finally, the localization of core genes was clarified through 10X single-cell sequencing. RESULTS: The crossing of 314 sepsis differential genes and 1136 mitochondrial genes yielded 28 genes. GO and KEGG analysis showed that the crossover genes were mainly involved in the mitochondrion, mitochondrial matrix, and mitochondrial inner membrane. Survival analysis screened four genes that were significantly negatively associated with the prognosis of sepsis, namely FIS1, FKBP8, GLRX5, and GUK1. A comparison of peripheral blood RNA-seq results between the sepsis group and the SIRS group showed that the expression levels of these four genes were significantly decreased in the sepsis group compared to the SIRS group. ROC curve analysis based on GSE67652 indicates these four genes' high sensitivity and specificity for sepsis detection. Additionally, single-cell RNA sequencing found that the core genes were mainly expressed in macrophages, T cells, and B cells. CONCLUSIONS: Mitochondria-related genes (FIS1, FKBP8, GLRX5, GUK1) were underexpressed in the sepsis group, negatively correlated with survival, and mainly distributed in immune cells. This finding may guide studying the immune-related mechanisms of sepsis. This study protocol was reviewed by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (ethics number: KY2018029), the clinical trial registration number is ChiCTR1900021261, and the registration date is February 4, 2019.

Rapid Electrochemical Detection of Bacterial Sepsis in Cirrhotic Patients: A Microscaffold-Based Approach for Early Intervention.

Sepsis is a dysregulated inflammatory response leading to multiple organ failure. Current methods of sepsis detection are time-consuming, involving nonspecific clinical signs, biomarkers, and blood cultures. Hence, efficient and rapid sepsis detection platforms are of utmost need for immediate antibiotic treatment. In the current study, a noninvasive rapid monitoring electrochemical sensing (ECS) platform was developed for the detection and classification of plasma samples of patients with liver cirrhosis by measuring the current peak shifts using the cyclic voltammetry (CV) technique. A total of 61 hospitalized cirrhotic patients with confirmed (culture-positive) or suspected (culture-negative) sepsis were enrolled. The presence of bacteria in the plasma was observed by growth kinetics, and for rapidness, the samples were co-encapsulated in microscaffolds with carbon nanodots that were sensitive enough to detect redox changes occurring due to the change in the pH of the surrounding medium, causing shifts in current peaks in the voltammograms within 2 h. The percentage area under the curve for confirmed infections was 94 and that with suspected cases was 87 in comparison to 69 and 71 with PCT, respectively. Furthermore, the charge was measured for class identification. The charge for LPS-absent bacteria ranged from -400 to -600 µC, whereas the charge for LPS-containing bacteria class ranged from -290 to -300 µC. Thus, the developed cost-effective system was sensitive enough to detect and identify bacterial sepsis.

On-Site Fluorescent Detection of Sepsis-Inducing Bacteria using a Graphene-Oxide CRISPR-Cas12a (GO-CRISPR) System.

Sepsis is an extremely dangerous medical condition that emanates from the body's response to a pre-existing infection. Early detection of sepsis-inducing bacterial infections can greatly enhance the treatment process and potentially prevent the onset of sepsis. However, current point-of-care (POC) sensors are often complex and costly or lack the ideal sensitivity for effective bacterial detection. Therefore, it is crucial to develop rapid and sensitive biosensors for the on-site detection of sepsis-inducing bacteria. Herein, we developed a graphene oxide CRISPR-Cas12a (GO-CRISPR) biosensor for the detection of sepsis-inducing bacteria in human serum. In this strategy, single-stranded (ssDNA) FAM probes were quenched with single-layer graphene oxide (GO). Target-activated Cas12a trans-cleavage was utilized for the degradation of the ssDNA probes, detaching the short ssDNA probes from GO and recovering the fluorescent signals. Under optimal conditions, we employed our GO-CRISPR system for the detection of Salmonella Typhimurium (S. Typhimurium) with a detection sensitivity of as low as 3 × 103 CFU/mL in human serum, as well as a good detection specificity toward other competing bacteria. In addition, the GO-CRISPR biosensor exhibited excellent sensitivity to the detection of S. Typhimurium in spiked human serum. The GO-CRISPR system offers superior rapidity for the detection of sepsis-inducing bacteria and has the potential to enhance the early detection of bacterial infections in resource-limited settings, expediting the response for patients at risk of sepsis.

Comparison of three rapid diagnostic tests for bloodstream infections using Benefit-risk Evaluation Framework (BED-FRAME).

Rapid diagnostic tests (RDTs) for bloodstream infections have the potential to reduce time to appropriate antimicrobial therapy and improve patient outcomes. Previously, an in-house, lipid-based, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) method, Fast Lipid Analysis Technique (FLAT MS), has shown promise as a rapid pathogen identification method. In this study, FLAT MS for direct from blood culture identification was evaluated and compared to FDA-cleared identification methods using the Benefit-risk Evaluation Framework (BED-FRAME) analysis. FLAT MS was evaluated and compared to Bruker Sepsityper and bioMérieux BioFire FilmArray BCID2 using results from a previous study. For this study, 301 positive blood cultures were collected from the University of Maryland Medical Center. The RDTs were compared by their sensitivities, time-to-results, hands-on time, and BED-FRAME analysis. The overall sensitivity of all platforms compared to culture results from monomicrobial-positive blood cultures was 88.3%. However, the three RDTs differed in their accuracy for identifying Gram-positive bacteria, Gram-negative bacteria, and yeast. Time-to-results for FLAT MS, Sepsityper, and BioFire BCID2 were all approximately one hour. Hands-on times for FLAT MS, Sepsityper, and BioFire BCID2 were 10 (±1.3), 40 (±2.8), and 5 (±0.25) minutes, respectively. BED-FRAME demonstrated that each RDT had utility at different pathogen prevalence and relative importance. BED-FRAME is a useful tool that can used to determine which RDT is best for a healthcare center.

Culture-independent identification of bloodstream infections from whole blood: prospective evaluation in specimens of known infection status.

Sepsis caused by bloodstream infection (BSI) is a major healthcare burden and a leading cause of morbidity and mortality worldwide. Timely diagnosis is critical to optimize clinical outcome, as mortality rates rise every hour treatment is delayed. Blood culture remains the "gold standard" for diagnosis but is limited by its long turnaround time (1-7 days depending on the organism) and its potential to provide false-negative results due to interference by antimicrobial therapy or the presence of mixed (i.e., polymicrobial) infections. In this paper, we evaluated the performance of resistance and pathogen ID/BSI, a direct-from-specimen molecular assay. To reduce the false-positivity rate common with molecular methods, this assay isolates and detects genomic material only from viable microorganisms in the blood by incorporating a novel precursor step to selectively lyse host and non-viable microbial cells and remove cell-free genomic material prior to lysis and analysis of microbial cells. Here, we demonstrate that the assay is free of interference from host immune cells and common antimicrobial agents at elevated concentrations. We also demonstrate the accuracy of this technology in a prospective cohort pilot study of individuals with known sepsis/BSI status, including samples from both positive and negative individuals. IMPORTANCE: Blood culture remains the "gold standard" for the diagnosis of sepsis/bloodstream infection (BSI) but has many limitations which may lead to a delay in appropriate and accurate treatment in patients. Molecular diagnostic methods have the potential for markedly improving the management of such patients through faster turnaround times and increased accuracy. But molecular diagnostic methods have not been widely adopted for the identification of BSIs. By incorporating a precursor step of selective lysis of host and non-viable microorganisms, our resistance and pathogen ID (RaPID)/BSI molecular assay addresses many limitations of blood culture and other molecular assay. The RaPID/BSI assay has an approximate turnaround time of 4 hours, thereby significantly reducing the time to appropriate and accurate diagnosis of causative microorganisms in such patients. The short turnaround time also allows for close to real-time tracking of pathogenic clearance of microorganisms from the blood of these patients or if a change of antimicrobial regimen is required. Thus, the RaPID/BSI molecular assay helps with optimization of antimicrobial stewardship; prompt and accurate diagnosis of sepsis/BSI could help target timely treatment and reduce mortality and morbidity in such patients.

Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures.

Manual microscopy of Gram stains from positive blood cultures (PBCs) is crucial for diagnosing bloodstream infections but remains labor intensive, time consuming, and subjective. This study aimed to evaluate a scan and analysis system that combines fully automated digital microscopy with deep convolutional neural networks (CNNs) to assist the interpretation of Gram stains from PBCs for routine laboratory use. The CNN was trained to classify images of Gram stains based on staining and morphology into seven different classes: background/false-positive, Gram-positive cocci in clusters (GPCCL), Gram-positive cocci in pairs (GPCP), Gram-positive cocci in chains (GPCC), rod-shaped bacilli (RSB), yeasts, and polymicrobial specimens. A total of 1,555 Gram-stained slides of PBCs were scanned, pre-classified, and reviewed by medical professionals. The results of assisted Gram stain interpretation were compared to those of manual microscopy and cultural species identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The comparison of assisted Gram stain interpretation and manual microscopy yielded positive/negative percent agreement values of 95.8%/98.0% (GPCCL), 87.6%/99.3% (GPCP/GPCC), 97.4%/97.8% (RSB), 83.3%/99.3% (yeasts), and 87.0%/98.5% (negative/false positive). The assisted Gram stain interpretation, when compared to MALDI-TOF MS species identification, also yielded similar results. During the analytical performance study, assisted interpretation showed excellent reproducibility and repeatability. Any microorganism in PBCs should be detectable at the determined limit of detection of 105 CFU/mL. Although the CNN-based interpretation of Gram stains from PBCs is not yet ready for clinical implementation, it has potential for future integration and advancement.

Surviving Sepsis Campaign Research Priorities 2023.

OBJECTIVES: To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock. DESIGN: Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities. METHODS: Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article. RESULTS: A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis? CONCLUSIONS: Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

Central Line-Associated Bloodstream Infection Misclassifications-Rethinking the Centers for Disease Control and Prevention's Central Line-Associated Bloodstream Infection Definition and Its Implications.

Centers for Medicare and Medicaid Services imparts financial penalties for central line-associated bloodstream infections (CLABSIs) and other healthcare-acquired infections. Data for this purpose is obtained from the Centers for Disease Control and Prevention (CDC)'s National Health Safety Network. We present examples of misclassification of bloodstream infections into CLABSI by the CDC's definition and present the financial implications of such misclassification and potential long-term implications.

Biomarkers Improve Diagnostics of Sepsis in Adult Patients With Suspected Organ Dysfunction Based on the Quick Sepsis-Related Organ Failure Assessment (qSOFA) Score in the Emergency Department.

OBJECTIVES: Consensus regarding biomarkers for detection of infection-related organ dysfunction in the emergency department is lacking. We aimed to identify and validate biomarkers that could improve risk prediction for overt or incipient organ dysfunction when added to quick Sepsis-related Organ Failure Assessment (qSOFA) as a screening tool. DESIGN: In a large prospective multicenter cohort of adult patients presenting to the emergency department with a qSOFA score greater than or equal to 1, admission plasma levels of C-reactive protein, procalcitonin, adrenomedullin (either bioavailable adrenomedullin or midregional fragment of proadrenomedullin), proenkephalin, and dipeptidyl peptidase 3 were assessed. Least absolute shrinkage and selection operator regression was applied to assess the impact of these biomarkers alone or in combination to detect the primary endpoint of prediction of sepsis within 96 hours of admission. SETTING: Three tertiary emergency departments at German University Hospitals (Jena University Hospital and two sites of the Charité University Hospital, Berlin). PATIENTS: One thousand four hundred seventy-seven adult patients presenting with suspected organ dysfunction based on qSOFA score greater than or equal to 1. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The cohort was of moderate severity with 81% presenting with qSOFA = 1; 29.2% of these patients developed sepsis. Procalcitonin outperformed all other biomarkers regarding the primary endpoint (area under the curve for receiver operating characteristic [AUC-ROC], 0.86 [0.79-0.93]). Adding other biomarkers failed to further improve the AUC-ROC for the primary endpoint; however, they improved the model regarding several secondary endpoints, such as mortality, need for vasopressors, or dialysis. Addition of procalcitonin with a cutoff level of 0.25 ng/mL improved net (re)classification by 35.2% compared with qSOFA alone, with positive and negative predictive values of 60.7% and 88.7%, respectively. CONCLUSIONS: Biomarkers of infection and organ dysfunction, most notably procalcitonin, substantially improve early prediction of sepsis with added value to qSOFA alone as a simple screening tool on emergency department admission.

Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.

BACKGROUND: Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. METHODS: We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. RESULTS: After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857-0.887). External validations show AUROCs of 0.805 (95% CI, 0.787-0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837-0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836-0.877) than PCT alone (0.790; 95% CI, 0.759-0.821; P < 0.001). CONCLUSIONS: Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis.

OBJECTIVE: To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU). STUDY DESIGN: Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation). RESULTS: The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set. CONCLUSIONS: Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Gut microbiota composition and changes in patients with sepsis: potential markers for predicting survival.

BACKGROUND: Sepsis can cause immune dysregulation and multiple organ failure in patients and eventually lead to death. The gut microbiota has demonstrated its precise therapeutic potential in the treatment of various diseases. This study aimed to discuss the structural changes of the gut microbiota in patients with sepsis and to analyze the differences in the gut microbiota of patients with different prognoses. METHODS: We conducted a multicenter study in which rectal swab specimens were collected on the first and third days of sepsis diagnosis. A total of 70 specimens were collected, and gut microbiota information was obtained by 16S rRNA analysis. RESULTS: The relative abundance of Enterococcus decreased in rectal swab specimens during the first three days of diagnosis in patients with sepsis, while the relative abundance of inflammation-associated Bacillus species such as Escherichia coli, Enterobacteriaceae, and Bacteroidetes increased. By comparing the differences in the flora of the survival group and the death group, we found that the abundance of Veillonella and Ruminococcus in the death group showed an increasing trend (p < 0.05), while the abundance of Prevotella_6 and Prevotella_sp_S4_BM14 was increased in surviving patients (p < 0.05). CONCLUSIONS: The Firmicutes/Bacteroidetes ratio, reflecting overall gut microbial composition, was significantly lower on day three of sepsis diagnosis. Changes in the abundance of specific gut microbiota may serve as prognostic markers in patients with sepsis.

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. METHODS: Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms. RESULTS: Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). CONCLUSION: The SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.