Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.

Post-implantation syndrome (PIS) can lead to prolonged hospitalization and major adverse cardiovascular events (MACE). This systematic review and meta-analysis investigated the clinical profile of PIS after abdominal (EVAR) and thoracic endovascular aortic repair (TEVAR). A comprehensive literature search identified 1463 studies, of which 16 (14 retrospective and 2 prospective) met the inclusion criteria. Data from these studies were aggregated using a random effects model to calculate pooled risk ratios and mean differences. The analysis included 2890 patients (males 84.7%, mean age 63.3 years ± 18.8) with 882 experiencing PIS. No significant differences were found in demographics, anthropometric measurements, risk factors, medical history, or chronic medical treatments between the two groups. Fever (above 38°C) was the most frequent clinical manifestation, observed in 75-100% of PIS cases. As expected, higher levels of post-procedural white blood cells (WBC) and platelets (PLT) were shown in PIS patients compared to non-PIS patients. Interestingly, pre-procedural WBC and PLT counts were significantly higher in the PIS group (p<0.001 and p<0.002 respectively). Patients with PIS were more likely to receive polyester graft (p=0.003), while those with polytetrafluoroethylene prostheses were less likely to develop PIS (p=0.04). The PIS group exhibited longer hospital stays compared to the non-PIS group (p<0.001). While most studies reported no evident PIS impact on outcomes, two studies reported higher rate of MACE. Larger prospective studies are needed to determine the optimal management strategies of patients at risk of PIS.

This study compared eight scoring systems for predicting 28-day and 1-year all-cause mortality in critically ill patients with acute pancreatitis (AP).

Data from the Medical Information Mart for Intensive Care IV were used to conduct a comparative analysis of several predictive scoring systems. Predictive performance for 28-day and 1-year mortality was assessed using receiver operating characteristic (ROC) curves (area under the curve [AUC]), restricted cubic splines (RCS) for nonlinearity testing, and multivariable logistic regression for independent predictor analysis.

A total of 694 patients were included (28-day mortality: 15.56%; 1-year mortality: 24.78%). Acute Physiology Score III (APSIII) demonstrated the highest accuracy for 28-day mortality (AUC: 0.847, 95% confidence interval (CI): 0.808-0.886), followed by Bedside Index for Severity in Acute Pancreatitis (BISAP) (AUC: 0.835, 95% CI: 0.794-0.875). Linear relationships between scores and 28-day mortality were confirmed (all p for nonlinear > 0.05). Multivariable regression identified APSIII and BISAP as independent 28-day mortality predictors. For 1-year mortality, APSIII, BISAP, and Simplified Acute Physiology Score II (SAPS II) were independent predictors.

Both APSIII and BISAP were identified as independent predictors of 28-day mortality, while APSIII, BISAP, and SAPSII were associated with 1-year mortality. Among them, APSIII showed the best overall discriminative ability for both short- and long-term outcomes. However, BISAP remains an attractive alternative for its simplicity and comparable performance.

Frequent computed tomography (CT) scans for pulmonary nodule monitoring (every 3, 6, and 12 months) lead to increased radiation exposure and a potential risk of malignancy. Although lung magnetic resonance imaging (MRI) is gradually approaching CT in terms of performance, the effectiveness of zero-echo-time (ZTE) sequences remains to be fully optimized, particularly in terms of diagnostic accuracy under the Lung-RADS.

This study aimed to evaluate the feasibility of oxygen-enhanced (OE) ZTE-MRI at varying oxygen concentrations (21% and 100%) for both the subjective and objective assessment of pulmonary nodules. It further explored the potential of OE-ZTE-MRI in detecting nodules, its diagnostic utility in Lung-RADS classification, and its role in evaluating malignant potential.

A total of 68 participants who underwent CT, ZTE-MRI, or OE-ZTE-MRI, were enrolled in this study. Quantitative MRI parameters (signal-to-noise ratio [SNR] and contrast-to-noise ratio [CNR]) were used to evaluate image quality. Lung nodule detection and Lung-RADS classification were performed by two radiologists independently using observer-based scoring of structural features: nodule type (solid nodule [SN], part-solid nodule [PSN], and ground-glass nodule [GGN]), and nodule size (measured manually on CT, ZTE-MRI, and OE-ZTE-MRI). Statistical analyses included the Wilcoxon signed-rank test, percentage consistency, kappa values, intraclass correlation coefficient (ICC), Spearman`s correlation, and Bland-Altman analysis. Statistical significance was set at p < 0.05.

Among the 68 patients (80 nodules; 57.2 ± 10.7 years; 27 males), OE-ZTE-MRI demonstrated a significantly higher SNR (p < 0.05) and superior qualitative scoring compared to ZTE-MRI. The nodule detection rate for OE-ZTE-MRI was 87.5%, with a diagnostic performance comparable to that of CT for assessing nodule diameter (ICC: 0.997; r = 0.994). OE-ZTE-MRI showed a high agreement with CT in nodule characterization (kappa = 0.789) and Lung-RADS (kappa = 0.756). Additionally, OE-ZTE-MRI exhibited strong inter-observer consistency in nodule size measurements.

OE-ZTE-MRI, which incorporates oxygen concentration adjustments, outperformed conventional ZTE-MRI in both subjective and objective evaluations. It achieves diagnostic performance comparable to that of CT in terms of nodule size. According to the Lung-RADS classification, OE-ZTE-MRI is gradually approaching the same diagnostic accuracy as CT.

This systematic review outlines a comprehensive approach to identify and analyze prognostic factors associated with mortality in adult cancer patients with sepsis in the intensive care unit. The review will focus on all-cause 28-day mortality, and where not available, we will use 30-day, intensive care unit, or in-hospital mortality.

We present a protocol for the systematic review of prognostic factors for mortality in adult cancer patients with sepsis managed in the intensive care unit. Our primary outcome is 28-day mortality, and where not available, we will use 30-day, intensive care unit, or in-hospital mortality. The secondary outcome is the global mortality incidence. Studies on the basis of the population (sepsis and neoplasms), prognostic study methods and outcome of interest (mortality) will be included. We will search the following databases: Medline, PubMed, EMBASE, SCOPUS, Web of Science, and Bireme-BVS, until April 5, 2024. The risk of bias will be assessed using the QUIPS tool. A meta-analysis will be conducted where possible to generate pooled estimates for identified prognostic factors. Two authors will independently assess the risk of bias in each study using the Quality in Prognostic Studies tool. The GRADE approach will be employed to evaluate the overall quality of evidence and the strength of the recommendations. Findings will be disseminated through publication in a peer-reviewed journal. This review aims to provide clinicians with valuable insights into factors influencing mortality risk in this high-risk population, ultimately informing clinical decision-making and improving patient outcomes.

The results of this review will be published in a peer-reviewed scientific journal. Does not require ethical approval.

Central nervous system (CNS) histoplasmosis occurs in 5%-10% of patients with disseminated histoplasmosis, is sparsely described in the literature and is highly morbid.

To evaluate clinical characteristics and outcomes of patients with CNS and non-CNS disseminated histoplasmosis.

In this retrospective case-control study, we matched 45 cases of CNS histoplasmosis with 45 controls with disseminated histoplasmosis without CNS involvement by hospital and date of encounter. Data were collected from three hospitals from January 2000 to December 2022 using histoplasmosis-related ICD-9/10 codes. Patients were classified as confirmed (Histoplasma growth on cerebrospinal fluid [CSF] or CNS culture), probable (positive CSF Histoplasma antigen or antibody), or possible (positive urine or serum Histoplasma antigen plus either CSF WBC ≥ 5 cells/μL or abnormalities on CNS imaging, with no other evident cause) CNS histoplasmosis.

CNS (n = 45) and non-CNS disseminated histoplasmosis (n = 45) patients had similar demographic and clinical characteristics, although persons living with HIV (PLWH) were more prevalent in the CNS histoplasmosis group (93.3% and 80.0%, respectively, p = 0.019). CSF profiles (CSF WBC, glucose and total protein) and MRI brain imaging were normal in 28.2% and 21.9% of CNS histoplasmosis patients, respectively. CNS histoplasmosis patients were severely ill, with 34.1% requiring ICU care and Glasgow Outcome Scores of 1-4 in 51.1% of patients at discharge. In-hospital mortality was 6.7% for CNS vs. 13.3% for disseminated histoplasmosis (p = 0.215).

In this large CNS histoplasmosis cohort, we found an increased prevalence of PLWH in CNS vs. non-CNS disseminated histoplasmosis. Similar to prior CNS histoplasmosis cohorts, we report relatively high rates of normal CSF profiles (28.2%) and MRI brain imaging (21.9%). We also found significant morbidity in patients with CNS histoplasmosis, data which were not reported in prior cohorts.

Bacterial infections in the blood (sepsis) have been recognized as a leading cause of mortality in the clinical field due to limitations in the detection of bacteria at low concentration and their resistance to antibiotics by excessive misuse. Some of the common symptoms are fever, chills, rapid heartbeat, difficulty breathing, confusion, and changes in mental status with occasionally pale, clammy, and mottled skin. Early diagnosis and identification are the keys to a successful treatment for sepsis patients. Researchers have developed nanoparticles to enrich bacterial populations followed by detection and applied them to conventional methods such as phenotypic and molecular diagnostics to enhance different detectors' responses toward pathogens. This short review systematically overviews steps that are followed in clinical labs for bacterial detection, identification, and their drawbacks. In this context, we discuss the role that nanoparticles can play in overcoming the limits of traditional microbiology methods in terms of turnaround times (TATs) and accuracy. We believe that this short review will provide up-to-date information about the applications of nanoparticles in the enrichment, separation, and identification of bacterial infection in the clinical field and, therefore, a way of rapid treatment.

This study investigates the global incidence of maternal sepsis, a life-threatening condition and major cause of maternal mortality. Through a systematic review and meta-analysis, we aim to provide a more precise estimation of its incidence, identify regional variations, and examine associated risk factors to inform improved prevention and management strategies.

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search of PubMed/MEDLINE, Scopus, Google Scholar, EMBASE, and the Web of Science was performed for studies published from inception to January 10, 2025. The methodological quality of the included studies was rigorously assessed using the Joanna Briggs Institute (JBI) critical appraisal tools. The pooled incidence rate per 10,000 pregnancies was estimated using a random-effects meta-analysis model to account for study heterogeneity. Furthermore, the analysis also explored the risk factors that contribute to the development of maternal sepsis.

A total of 44 studies, encompassing 141,200,302 pregnant women from 24 countries, were included in the analysis. The global cumulative incidence of maternal sepsis was found to be 13.16 per 10,000 pregnant women (95 % CI: 9.91-17.47). Regional variations were significant, with the highest crude incidence observed in the African region (129.17 per 10,000; 95 % CI: 67.05-248.85), while the lowest was recorded in the Region of the Americas (6.31 per 10,000; 95 % CI: 4.36-9.12). These findings were based on six studies from the African region and 17 from the Americas. Additionally, the study identified several factors, such as age ≥ 35, multiple pregnancies, gestational diabetes, preeclampsia/eclampsia, hypertension, diabetes mellitus, obesity, and cesarean delivery, that were linked to an increased risk of maternal sepsis.

This study provides global and regional estimates of maternal sepsis, with a cumulative incidence of 13.16 per 10,000 pregnancies, highlighting regional disparities. Key risk factors include multiple pregnancies, preeclampsia, hypertension, obesity, and cesarean delivery. The findings emphasize the need for improved healthcare access, better data collection, and early intervention to reduce maternal sepsis worldwide.

This review will explore the current knowledge, beginning with the physiologic underpinnings and delve into the evolving scientific literature, encompassing the inextricably intertwined diagnosis and management of sepsis and septic shock in human and small animal patients. Sepsis is a significant cause of morbidity and mortality in patients, mostly for failure to recognize or treat promptly and adequately. Diagnosis is based on the individual patient, clinical context, and clinical acumen. High quality supportive care in the intensive care unit setting is patient-centered with intensive nursing, focused on physiologic systems, goal-oriented, and multi-disciplinary with a team-based approach to patient care.

Sepsis remains a major health concern with high associated mortality. Adequate treatment involves the use of antibiotic therapy although the timing of antibiotics is controversial. A decision analysis model of antibiotic initiation was created to determine optimal management of patients with suspected sepsis.

Two decision trees were created using data from the published literature. A limited model used mortality as the primary outcome using the impact of antibiotic timing on rates of progression to shock and in-hospital mortality. The primary model included mortality and stewardship-related factors such as antibiotic avoidance and antibiotic-associated adverse events. Rapid initiation of antibiotics was defined as universal antibiotic administration within 3 h of presentation whereas deferred initiation included administration up to 6 h. Sensitivity analyses were performed to evaluate the effectiveness of each option.

When considering only mortality, rapid initiation was the optimal strategy. When considering stewardship-related factors, rapid initiation of antibiotics maximized utility in only 40.6% of model iterations. One-way sensitivity analysis demonstrated rapid initiation of antibiotics was optimal when initiation times were above 1.33 h and the prevalence of infection was above 89.5%. Two-way sensitivity analysis demonstrated that as time to antibiotics increased, rate of true infection above which rapid antibiotics is optimal drops from just under 91% to approximately 88.5%.

We constructed decision analysis models to characterize optimal conditions for antibiotic initiation in suspected sepsis. Our model suggests that the prevalence of infection needs to be approximately 90% for rapid initiation of antibiotics to be the optimal strategy.

Background/Objectives: Acute obstructive pyelonephritis caused by ureteral calculi is a severe urological emergency that can rapidly progress to life-threatening complications, including urosepsis. Early risk stratification is critical for timely intervention and improved patient outcomes. Although Hu's nomogram has been proposed as a predictive tool for urosepsis, its external validation remains limited. This study aims to validate Hu's nomogram in an independent cohort and identify novel clinical and imaging predictors of urosepsis. Methods: This retrospective cohort study included 341 patients diagnosed with ureteral calculi who underwent surgical intervention at a single institution between January 2019 and October 2023. Clinical, laboratory, and imaging data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of urosepsis. The predictive accuracy of Hu's nomogram was evaluated using receiver operating characteristic curve analysis. Results: Among 341 patients, 66 (19.4%) developed urosepsis. Multivariate analysis identified female gender, corticosteroid use, lower platelet count, elevated C-reactive protein levels, positive urine white blood cell count, lower computed tomography attenuation values of calculi, and higher computed tomography attenuation values of hydronephrosis as independent predictors of urosepsis. Hu's nomogram demonstrated a strong predictive performance (area under the curve: 0.761; 95% CI: 0.701-0.821), reaffirming its clinical utility for risk stratification. Conclusions: This study provides an external validation of Hu's nomogram and identifies novel risk factors for urosepsis prediction, including corticosteroid use and imaging-based parameters. Incorporating these findings into clinical practice may enhance early risk stratification, facilitate timely interventions, and ultimately improve patient outcomes.

Gastric cancer is a common malignant tumor, and radical gastrectomy can markedly improve the prognosis of gastric cancer patients. However, some patients are diagnosed with advanced gastric cancer before receiving any antitumor therapy and need to receive neoadjuvant chemotherapy (NACT). Previous studies have shown that NACT may cause gut barrier dysfunction and intestinal dysbacteriosis which may further lead to infections. Probiotics have the potential to reduce postoperative infections and improve short-term outcomes after abdominal surgery; however, no large-sample, multicenter, randomized clinical trials have been conducted to explore the effectiveness of probiotics in gastric cancer patients receiving NACT. So we proposed a hypothesis that probiotics can improve short-term outcomes after minimally invasive radical gastrectomy in gastric cancer patients receiving NACT and designed this multicenter randomized controlled trial with the objective to verify this hypothesis.

The GISSG 2023-01 study will be a prospective, open-label, multicenter RCT to verify whether perioperatively probiotic supplementation (begin from the end of the last cycle of NACT to postoperative day 7 or the discharge day) can reduce postoperative infections and improve recovery of gastrointestinal function and other short-term outcomes after minimally invasive radical gastrectomy in gastric cancer patients receiving NACT. A total of 318 patients who meet the inclusion criteria will be enrolled in this study and randomly divided into two groups in a 1:1 ratio: the probiotic group (n = 159) and the control group (n = 159). The participants in the probiotic group will receive perioperative probiotic supplementation, and those in the control group will receive blank control management. The other perioperative management protocols will be the same between the two groups. The primary outcome is postoperative infection compared between the two groups, and the secondary outcomes are postoperative recovery of gastrointestinal function, quality of life, laboratory parameters of systemic inflammation and other short-term outcomes.

The results of this RCT should clarify whether perioperative probiotic supplementation would reduce postoperative infection, promote recovery of gastrointestinal function, reduce laboratory parameters of systemic inflammation and improve symptoms and quality of life after minimally invasive radical gastrectomy in gastric cancer patients receiving NACT. It is hoped that our data will provide evidence that probiotic supplementation improves short-term outcomes in gastric cancer patients receiving NACT.

This trial has been registered on https://clinicaltrials.gov/(NCT05901779 ).

Purpose: Elevated glycated hemoglobin A1c (HbA1c) has not been specifically evaluated as a risk factor for urosepsis after kidney stone procedures. Moreover, there are no current guidelines for perioperative HbA1c optimization, nor recommendations for the optimal timing to treat non-urgent kidney stones in the setting of poor glycemic control. We evaluated the association between perioperative HbA1c levels and sepsis risk after ureteroscopy with lithotripsy. Methods: Patients undergoing ureteroscopy with lithotripsy from January 2020 to June 2023 at a tertiary center were retrospectively reviewed. Postoperative sepsis was defined as Systemic Inflammatory Response Syndrome scores ≥2 within 30 days after ureteroscopy. The risk of sepsis at various HbA1c thresholds was evaluated via multivariate logistic regression. Results: A total of 1454 patients underwent ureteroscopy with lithotripsy, and 319 patients had HbA1c collected within 90 days of their procedures. The mean preoperative Charlson Comorbidity Index (CCI) score was 3.22 (±2.77). An increased risk of sepsis was observed among patients with HbA1c levels between 8.0% and 9.9% (odds ratio [OR] 4.42, p = 0.025) and ≥10% (OR 8.17, p = 0.003). Positive preoperative urine culture despite treatment (OR 4.53, p < 0.001) and higher CCI (OR 1.17, p = 0.045) were also associated with increased odds of sepsis. Conclusion: The odds of sepsis after ureteroscopy with lithotripsy follow a dose-response relationship with elevated perioperative HbA1c. These data underscore the clinical utility of incorporating HbA1c into preprocedural optimization and may justify certain patients to delay elective ureteroscopy to improve glycemic control before endourologic intervention.

To develop an electronic medical record (EMR) data processing tool that confers clinical context to machine learning (ML) algorithms for error handling, bias mitigation, and interpretability.

We present Trust-MAPS, an algorithm that translates clinical domain knowledge into high-dimensional, mixed-integer programming models that capture physiological and biological constraints on clinical measurements. EMR data are projected onto this constrained space, effectively bringing outliers to fall within a physiologically feasible range. We then compute the distance of each data point from the constrained space modeling healthy physiology to quantify deviation from the norm. These distances, termed "trust-scores," are integrated into the feature space for downstream ML applications. We demonstrate the utility of Trust-MAPS by training a binary classifier for early sepsis prediction on data from the 2019 PhysioNet Computing in Cardiology Challenge, using the XGBoost algorithm and applying SMOTE for overcoming class-imbalance.

The Trust-MAPS framework shows desirable behavior in handling potential errors and boosting predictive performance. We achieve an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.89-0.92) for predicting sepsis 6 hours before onset-a marked 15% improvement over a baseline model trained without Trust-MAPS.

Downstream classification performance improves after Trust-MAPS preprocessing, highlighting the bias reducing capabilities of the error-handling projections. Trust-scores emerge as clinically meaningful features that not only boost predictive performance for clinical decision support tasks but also lend interpretability to ML models.

This work is the first to translate clinical domain knowledge into mathematical constraints, model cross-vital dependencies, and identify aberrations in high-dimensional medical data. Our method allows for error handling in EMR and confers interpretability and superior predictive power to models trained for clinical decision support.

Currently, understanding the immune response, its abnormalities, and its diagnostic possibilities is a key point in the management of patients with various diseases, from infectious to oncological ones. The aim of this review was to analyze the data presented in the current literature on immune disorders and the possibility of their laboratory diagnostics in combination with clinical manifestations. We have performed a systematic analysis of the literature presented in international databases over the last ten years. We have presented data on the possibility of diagnosing immunopathological processes due to changes in immune cells and soluble molecules involved in the pathogenesis of a wide range of diseases, as well as the determination of antibodies to detect autoimmune processes. By applying laboratory techniques such as hematology, flow cytometry, ELISA, etc., available to most clinical laboratories worldwide, clinical data on immune system dysfunction in a wide range of diseases are being collected. This process is unfortunately still very far from being completed. However, with all the diversity of accumulated knowledge, we can currently state that the pathogenesis of the vast majority of immune-mediated diseases is not yet known. At the same time, the current success in dividing immune-mediated diseases into distinct clusters based on different types of inflammatory responses that are based on the involvement of different populations of T helper cells and cytokine molecules represents significant progress. Further research in this direction seems very promising, as it allows the identification of new target cells and target molecules for both improved diagnostics and targeted therapies.

Sepsis affects approximately 50 million people worldwide, resulting in 11 million deaths annually. Conflicting results and insufficient evidence comparing performance biomarkers exist. The study aimed to comprehensively compare available biomarkers and clinical scores for detecting sepsis since its redefinition in 2016 with this systematic review and Bayesian diagnostic test accuracy network meta-analysis.

We conducted searches in the PubMed, EMBASE, and Scopus databases between January 2016 and December 2023. Eligible studies assessed the diagnostic accuracies of biomarkers, the quick Sequential Organ Failure Assessment (qSOFA) score, or Systemic Inflammatory Response Syndrome (SIRS) criteria in detecting sepsis. Bivariate hierarchical random effects arm-based beta-binomial models were used for quantitative synthesis (PROSPERO Registration Number: CRD42018086545).

We included 78 studies representing 34,234 patients and compared qSOFA score, SIRS criteria alongside seven of the most studied biomarkers: procalcitonin, C-reactive protein (CRP), interleukin-6 (IL-6), presepsin (cluster of differentiation 14 subtypes), CD64, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and lipopolysaccharide-binding protein (LBP). CD64 demonstrated the highest superiority index, followed by sTREM-1 and presepsin (diagnostic odds ratio: 20.17 vs 18.73 and 10.04, 95 % credible interval [CrI]: 8.39-38.61 vs 1.31-83.98 and 6.71-14.24; quality of evidence: moderate vs low and low). Multivariable meta-regression analysis identified significant sources of heterogeneity, including study design, proportion of sepsis, sample size, and the risk of bias (patient selection).

The best diagnostic accuracy for sepsis was shown by CD64, with a moderate quality of evidence. Compared to CD64, sTREM-1 and presepsin provided suboptimal and low evidence. These biomarkers were more effective at identifying updated sepsis than clinical scores. We recommend re-considering the addition of biomarkers in screening for sepsis or sepsis-related conditions, as this could lead to more accurate and timely decisions for future clinical interventions.

The aim of this study was to compare the prognostic value of PSP in different types of sepsis, with common biomarkers. PSP levels were higher in bacteremia and Gram-negative sepsis and correlated with worse outcome (p = 0,006) earlier than WBC, CRP and PCT. It may aid for risk stratification in sepsis.

Corneal transplantation is an exceedingly common and pivotal procedure in the management of corneal disease. The increasing demand for corneal transplantation underscores the importance of current guidelines and methodologies for donor tissue screening and collection. A recent FDA notice of inspectional focus on United States eye banks brought concerns about donor sepsis being a source for recipient infections and improper eye bank screening practices. However, there is evidence to suggest that septic donors do not increase the risk of transmission of infections associated with corneal grafts. This is additionally important given the growing need for more corneal graft tissue to maximize the use of suitable tissue. Eye banks also have a number of protocols to consider when screening donors for the presence of sepsis, providing a unique challenge given the broadness of the term. This paper provides an overview of trends in clarification of the term "sepsis" as well as the screening protocol in eye banks.

Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.

We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them. For evaluation, we leveraged the largest existing public ICU monitoring datasets (MIMIC III and eICU).

Models produced by GroupFasterRisk outperformed OASIS and SAPS II scores and performed similarly to APACHE IV/IVa while using at most a third of the parameters. For patients with sepsis/septicemia, acute myocardial infarction, heart failure, and acute kidney failure, GroupFasterRisk models outperformed OASIS and SOFA. Finally, different mortality prediction ML approaches performed better based on variables selected by GroupFasterRisk as compared to OASIS variables.

GroupFasterRisk's models performed better than risk scores currently used in hospitals, and on par with black box ML models, while being orders of magnitude sparser. Because GroupFasterRisk produces a variety of risk scores, it allows design flexibility-the key enabler of practical model creation.

GroupFasterRisk is a fast, accessible, and flexible procedure that allows learning a diverse set of sparse risk scores for mortality prediction.

Sepsis and septic shock are common conditions evaluated and managed in the emergency department (ED), and these conditions are associated with significant morbidity and mortality. There have been several recent updates in the literature, including guidelines, on the evaluation and diagnosis of sepsis and septic shock.

This is the first paper in a two-part series that provides emergency clinicians with evidence-based updates concerning sepsis and septic shock. This first paper focuses on evaluation and diagnosis of sepsis and septic shock.

The evaluation, diagnosis, and management of sepsis have evolved since the first definition in 1991. Current guidelines emphasize rapid diagnosis to improve patient outcomes. However, scoring systems have conflicting data for diagnosis, and sepsis should be considered in any patient with infection and abnormal vital signs, evidence of systemic inflammation (e.g., elevated white blood cell count or C-reactive protein), or evidence of end-organ dysfunction. The clinician should consider septic shock in any patient with infection and hypotension despite volume resuscitation or who require vasopressors to maintain a mean arterial pressure ≥ 65 mmHg. There are a variety of sources of sepsis but the most common include pulmonary, urinary tract, abdomen, and skin/soft tissue. Examples of other less common etiologies include the central nervous system (e.g., meningitis, encephalitis), spine (e.g., spinal epidural abscess, osteomyelitis), cardiac (e.g., endocarditis), and joints (e.g., septic arthritis). Evaluation may include biomarkers such as procalcitonin, C-reactive protein, and lactate, but these should not be used in isolation to exclude sepsis. Imaging is a key component of evaluation and should be based on the suspected source.

There have been several recent updates in the literature including guidelines concerning sepsis and septic shock; an understanding of these updates can assist emergency clinicians and improve the care of these patients.

Acute pancreatitis (AP) is an inflammatory condition with rising incidence, often resulting in severe complications and increased mortality, particularly when accompanied by organ failure. Early identification of patients at risk for severe AP is essential for timely intervention. Current scoring systems like Ranson's, BISAP, and APACHE-II, though useful, have limitations in terms of time and specificity. We aimed to identify a simple and early scoring system to predict severe AP.

In this single-center study conducted over two years, patients diagnosed with AP within 72 h of symptom onset were enrolled. Initial clinical and laboratory data were prospectively collected according to established criteria, including BISAP, APACHE-II, and Ranson's. Multivariate logistic regression analyses were performed to identify independent risk factors for severe AP, which were then used to develop a new scoring system.

In our population of 424 patients (8.5% severe), we identified key clinical and laboratory markers-blood urea nitrogen (BUN), neutrophil-to-lymphocyte ratio (NLR), and heart rate-as independent predictors of severe AP. Based on these factors, we developed the BHN scoring system, which demonstrated non-inferior sensitivity (91.7%) and specificity (83.3%) for predicting severe disease, compared to more complex systems BISAP, Ranson's, and APACHE-II.

The BHN score offers a simple, accessible tool in a variety of clinical settings, improving early risk stratification. External validation and further exploration of its use in mortality prediction are needed, but BHN presents a promising alternative for guiding early treatment decisions in acute pancreatitis.

Sepsis is a major global health problem. However, it lacks a true reference standard for case identification, complicating epidemiologic surveillance. Consensus definitions have changed multiple times, clinicians struggle to identify sepsis at the bedside, and differing identification algorithms generate wide variation in incidence rates. The two current identification approaches use codes from administrative data, or electronic health record (EHR)-based algorithms such as the Center for Disease Control Adult Sepsis Event (ASE); both have limitations. Here our primary purpose is to report initial steps in developing a novel approach to identifying sepsis using unsupervised clustering methods. Secondarily, we report preliminary analysis of resulting clusters, using identification by ASE criteria as a familiar comparator.

This retrospective cohort study used hospital administrative and EHR data on adults admitted to intensive care units (ICUs) at five Canadian medical centres (2015-2017), with split development and validation cohorts. After preprocessing 592 variables (demographics, encounter characteristics, diagnoses, medications, laboratory tests, and clinical management) and applying data reduction, we presented 55 principal components to eight different clustering algorithms. An automated elbow method determined the optimal number of clusters, and the optimal algorithm was selected based on clustering metrics for consistency, separation, distribution and stability. Cluster membership in the validation cohort was assigned using an XGBoost model trained to predict cluster membership in the development cohort. For cluster analysis, we prospectively subdivided clusters by their fractions meeting ASE criteria (≥ 50% ASE-majority clusters vs. ASE-minority clusters), and compared their characteristics.

There were 3660 patients in the development cohort and 3012 in the validation cohort, of which 21.5% (development) and 19.1% (validation) were ASE (+). The Robust and Sparse K-means Clustering (RSKC) method performed best. In the development cohort, it identified 48 clusters of hospitalizations; 11 ASE-majority clusters contained 22.4% of all patients but 77.8% of all ASE (+) patients. 34.9% of the 209 ASE (-) patients in the ASE-majority clusters met more liberal ASE criteria for sepsis. Findings were consistent in the validation cohort.

Unsupervised clustering applied to diverse, large-scale medical data offers a promising approach to the identification of sepsis phenotypes for epidemiological surveillance.

Background: Provider-to-provider emergency department telehealth (tele-ED) has been proposed to improve rural sepsis care. The objective of this study was to measure the association between sepsis documentation and tele-ED use, treatment guideline adherence, and mortality. Methods: This analysis was a multicenter (n = 23) cohort study of sepsis patients treated in rural emergency departments (EDs) that participated in a tele-ED network between August 2016 and June 2019. The primary outcome was whether sepsis was documented explicitly in the clinical note impression in the local ED, and the primary exposure was rural tele-ED use, with secondary outcomes of time to tele-ED use, 3-h guideline adherence, and in-hospital mortality. Results: Data from 1,146 rural sepsis patients were included, 315 (27%) had tele-ED used and 415 (36%) had sepsis recognized in the rural ED. Tele-ED use was not independently associated with sepsis recognition (adjusted odds ratio [aOR]: 1.23, 95% confidence interval [CI]: 0.90-1.67). Sepsis recognition was associated with earlier tele-ED activation (adjusted hazard ratio 1.66, 95% CI: 1.28-2.15) and greater 3-h guideline adherence (aOR 1.37, 95% CI 1.03-1.83) Sepsis recognition was not independently associated with mortality (aOR 1.32, 95% CI 0.97-1.80). Conclusions: Although tele-ED care is a promising strategy to improve sepsis outcomes, its use was limited by under-recognition of sepsis in rural EDs.

Research is limited in describing the association between admission source and mortality in critically ill patients. Therefore, this study investigated how intensive care units (ICUs) admission source (emergency department (ED) or ward) correlates with mortality rates.

This retrospective observational cross-sectional study was conducted in a tertiary pulmonology teaching hospital's ICU from January 1, 2018, to December 31, 2019. Patients were ICU patients admitted for acute respiratory failure. Demographic, comorbidities, diagnoses, APACHE II score, ICU admission (ED or ward), mechanical breathing support (invasive or noninvasive), length of stay, and mortality were recorded. Comparisons of ICU admission sources and mortality factors were established.

A total of 2,173 ICU patients were studied; 1,011 (46%) were admitted from the ED and 1,162 (54%) from the ward. Their mean age was 70 years, and 66% of them were men. Pneumonia was the leading cause of ICU admission at 60% and Chronic Obstructive Pulmonary Disease (COPD) was the most common comorbidity at 54%. When both groups were evaluated in terms of respiratory support, non-invasive mechanical ventilation use was higher in patients admitted from the emergency room (ED: 50% vs. Ward: 35%), invasive mechanical ventilation was more frequently required in patients admitted from the ward compared to those admitted from the emergency department (ED: 17% vs. Ward: 25%). Length of ICU stay (2 vs. 3 days P < 0.001) and ICU mortality (odds ratio: 1.66, 95% confidence interval 1.297-2.124, P < 0.001) were higher in patients admitted from the ward than in patients admitted from the emergency department. In addition, pneumonia patients and those with malignancies, interstitial lung disease, or noninvasive mechanical ventilation (NIV) failure were associated with higher mortality.

Our study suggests that ward-to-ICU patients had higher mortality rates compared to ED-to-ICU patients. Triage protocols to better identify potentially critically ill patients in the ED may improve outcomes by avoiding delays in care and better assignment of admission location.

Blood biomarkers of neurological injury could provide a rapid diagnosis of central nervous system injury caused by infections. A Food and Drug Administration (FDA)-approved assay for mild traumatic brain injury (TBI) measures glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), which signal astrocyte and neuronal injury, respectively. Here, we assessed the applicability of this biomarker assay for determining infection-induced brain injury.

We measured serum levels of GFAP and UCH-L1 retrospectively in serum samples from 3 study populations: (1) human cases infected with Venezuelan equine encephalitis virus (VEEV) and Madariaga virus (MADV) (n = 73), (2) human sepsis patients who were severely ill or diagnosed with encephalitis (n = 66), and (3) sepsis cases that were subsequently evaluated for cognitive impairment (n = 64).

In the virus infection group, we found elevated GFAP for VEEV (P = .014) and MADV (P = .011) infections, which correlated with seizures (P = .006). In the bacterial sepsis group, GFAP was elevated in cases diagnosed with encephalitis (P = .0007) and correlated with headaches (P = .0002). In the bacterial sepsis cases with a later cognitive assessment, elevated GFAP (P = .0057) at study enrollment was associated with cognitive impairment 6 months later with a positive prognostic capacity of 79% (95% confidence interval, 66%-95%; P = .0068).

GFAP and UCH-L1 levels measured using an FDA-approved assay for TBI may indicate brain injury resulting from viral or bacterial infections and could predict the development of neurological sequelae.

We sought to delineate the mortality outcome time frames reported in septic shock randomized control trials (RCTs).

Systematic review of PubMed, EMBASE, and the Cochrane Database of Systematic Reviews.

Intensive care units.

Studies that included adult patients with septic shock.

Any type of intervention.

Information about the study, specific patient population, type of study intervention, specific intervention, and number of patients. Mortality time frames were analyzed for geographical differences and changes over time.

The search yielded 2660 unique citations. After screening, 132 eligible studies were identified. A total of 234 mortality time frames were collected from the included studies, of which 15 timeframes were unique. The most frequently reported time frame was 28-day mortality (n = 98, 74% of trials), followed by hospital mortality (n = 35, 27%), ICU mortality (n = 30, 23%), and 90-day mortality (n = 29, 22%). The most reported mortality time frame was 28 days in studies from every continent except Africa. The studies published between 2008 and 2013 (25%) more frequently reported hospital and ICU mortality combination than studies published between 2014 and 2019 (11.4%) (P = 0.043).

There was considerable variability in the mortality time frames reported in ICU-based septic shock trials. This variability may lead to under or overestimation of the problem, overlooking the effectiveness of the interventions studied, and further limiting the application of trials and their pooling in meta-analyses. A consensus regarding time frame reporting in septic shock trials is long overdue.

The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine. This is the fourth revision since the first edition was published in 2012. The purpose of the guidelines is to assist healthcare providers in making appropriate decisions in the treatment of sepsis and septic shock, leading to improved patient outcomes. We aimed to create guidelines that are easy to understand and use for physicians who recognize sepsis and provide initial management, specialized physicians who take over the treatment, and multidisciplinary healthcare providers, including nurses, physical therapists, clinical engineers, and pharmacists. The J-SSCG 2024 covers the following nine areas: diagnosis of sepsis and source control, antimicrobial therapy, initial resuscitation, blood purification, disseminated intravascular coagulation, adjunctive therapy, post-intensive care syndrome, patient and family care, and pediatrics. In these areas, we extracted 78 important clinical issues. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members. As a result, 42 GRADE-based recommendations, 7 good practice statements, and 22 information-to-background questions were created as responses to clinical questions. We also described 12 future research questions.

Cardiac surgery and the associated ischemia-reperfusion injury trigger an inflammatory response, which, in turn, can contribute to organ damage, prolonged hospitalization, and mortality. Therefore, the present study performed comprehensive monitoring of neutrophil-related inflammation in patients who underwent aortic valve surgery, including extracorporeal circulation. Neutrophil-related inflammation, as well as alterations in cellular physiology, phenotype, and function, were analyzed by flow cytometry, ELISA, and microscopy. Neutrophil activation occurred intraoperatively and preceded the upregulation of conventional inflammatory markers such as C-reactive protein and interleukin-6. Perioperatively, neutrophils maintained a stable response to platelet-activating factor (PAF) with regard to CD11b and CD66b expression but showed a decreased response in CD10. Postoperatively, neutrophils exhibited marked alterations in PAF-induced depolarization, while reactive oxygen species generation and phagocytic activity remained largely stable. Surprisingly, platelet-neutrophil complex formation was severely impaired intraoperatively but returned to normal levels postoperatively. Further studies are needed to elucidate the implications of these intraoperative and postoperative changes in neutrophil and platelet activity with respect to a potential immune dysfunction that temporarily increases susceptibility to infectious or hemostatic complications.

Sepsis is a critical condition associated with high morbidity and mortality, emphasizing the need for reliable biomarkers for its diagnosis and prognosis. This study uses advanced immunological techniques to evaluate monocytic CD39 (mCD39) expression as a potential marker in sepsis.

This prospective observational cohort study included 206 participants from the First Affiliated Hospital, Zhejiang University School of Medicine between April 2022 and September 2023. Participants were categorized into four groups: healthy donors, patients with mild infections, post-cardiac surgery patients (non-infectious inflammation), and sepsis patients. Peripheral Blood Mononuclear Cells were analyzed using mass cytometry time-of-flight (CyTOF) with a 42-marker immune panel and flow cytometry targeting monocytes. Statistical analyses included ROC curves for diagnostic and prognostic performance and Kaplan-Meier survival analysis for prognostic evaluation.

Sepsis patients exhibited significantly lower monocytic CD39 expression than mild infection and post-surgery groups (p < 0.05). The diagnostic performance analysis revealed that mCD39 effectively distinguished sepsis from mild infection (AUC = 0.877) and non-infectious inflammation (AUC = 0.935). Prognostic analysis identified low mCD39 expression as a strong predictor of short-term survival, with a 7-day survival AUC of 0.85 (p = 0.037). Kaplan-Meier analysis showed that sepsis patients with low mCD39 expression had significantly lower 28-day survival rates (56.7% vs. 80.6%, p = 0.016).

Low CD39 expression on monocytes might serve as a potential diagnostic biomarker and a strong predictor of poor prognosis in sepsis patients.