Conocimiento en estudiantes de enfermería sobre sepsis quirúrgico / Knowledge in nursing students about surgical sepsis

Objetivo: Determinar el nivel de conocimiento de los estudiantes de enfermería de la Universidad Técnica de Ambato sobre sepsis quirúrgica. Material y método: La presente investigación tiene un diseño de desarrollo observacional, de tipo descriptivo, cohorte transversal, con un enfoque cuantitativo, ya que el nivel de cono-cimiento se verá representado mediante tablas y gráficos para des-cribir la problemática del periodo octubre 2023 febrero 2024. Re-sultados: Se evidencia un alto porcentaje de respuestas incorrectas por cada ítem por parte de los estudiantes. La categoría Nivel de Conocimiento sobre Definición de Sepsis, fue respondida de ma-nera incorrecta con un porcentaje del 83,9%, la categoría Nivel de Conocimiento sobre Diagnóstico de Sepsis obtuvo 51,7% y, por úl-timo, la Nivel de Conocimiento sobre Tratamiento de Sepsis con el 29,2%. Conclusiones: El nivel de conocimiento de los estudiantes sobre Sepsis Quirúrgica es malo, debido a que existe una subesti-mación de la gravedad de la sepsis como afección potencialmente mortal, lo que puede traer un impacto negativo en los pacientes[AU]

Objective: Determine the level of knowledge of nursing students at the Technical University of Ambato about surgical sepsis. Mate-rials and methods: This research has an observational, descriptive, transversal development design, with a quantitative approach since the level of knowledge will be represented through tables and gra-phs to describe the problems of the period October 2023-February 2024. Results: A high percentage of incorrect answers for each item by the students is evident. The category Level of Knowledge about Definition of Sepsis was answered incorrectly with a percentage of 83.9%, the category Level of Knowledge about Diagnosis of Sepsis obtained 51.7% and, finally, the category Level of Knowledge about Treatment of Sepsis. Sepsis with 29.2%. Conclusions: The level of knowledge of students about Surgical Sepsis is poor because there is an underestimation of the severity of sepsis as a potentially fatal condition, which can have a negative impact on patients[AU]

Objetivo: Determinar o nível de conhecimento dos estudantes de enfermagem da Universidade Técnica de Ambato sobre sepse ci-rúrgica. Material e método: Esta pesquisa possui desenho de coor-te observacional, descritivo, transversal, com abordagem quantita-tiva, uma vez que o nível de conhecimento será representado por meio de tabelas e gráficos para descrever o problema no período de outubro de 2023 a fevereiro de 2024. Resultados: Uma parada. É evidente o percentual de respostas incorretas para cada item por parte dos alunos. A categoria Nível de Conhecimento sobre Defi-nição de Sepse foi respondida incorretamente com percentual de 83,9%, a categoria Nível de Conhecimento sobre Diagnóstico de Sepse obteve 51,7% e por fim, a categoria Nível de Conhecimen-to sobre Tratamento de Sepse com 29,2%. Conclusões: O nível de conhecimento dos estudantes sobre a Sepse Cirúrgica é baixo, pois há uma subestimação da gravidade da sepse como uma condição potencialmente fatal, que pode ter um impacto negativo nos pa-cientes[AU]

Understanding the value of monocyte distribution width (MDW) in acutely ill medical patients presenting to the emergency department: a prospective single center evaluation.

The monocyte distribution width (MDW) has emerged as a promising biomarker for accurate and early identification of patients with potentially life-threatening infections. Here we tested the diagnostic performance of MDW in adult patients requiring hospital admission for community-acquired infections and sepsis, evaluated sources of heterogeneity in the estimates of diagnostic accuracy, and assessed the meaning of MDW in a patient population presenting to the emergency department (ED) for acute non-infectious conditions. 1925 consecutive patients were categorized into three groups: non-infection (n = 1507), infection (n = 316), and sepsis/septic shock (n = 102). Diagnostic performance for infection or sepsis of MDW alone or in combination with components of SOFA was tested using AUC of ROC curves, sensitivity, and specificity. The relationship between MDW and different pathogens as well as the impact of non-infectious conditions on MDW values were explored. For the prediction of infection, the AUC/ROC of MDW (0.84) was nearly overlapping that of procalcitonin (0.83), and C-reactive protein (0.89). Statistical optimal cut-off value for MDW was 21 for predicting infection (sensitivity 73%, specificity 82%) and 22 for predicting sepsis (sensitivity 79%, specificity 83%). The best threshold to rule out infection was MDW ≤ 17 (NPV 96.9, 95% CI 88.3-100.0), and ≤ 18 (NPV 99.5, 95% CI 98.3-100.0) to rule out sepsis. The combination of MDW with markers of organ dysfunction (creatinine, bilirubin, platelets) substantially improved the AUC (0.96 (95% CI 0.94-0.97); specificity and sensitivity of 88% and 94%, respectively). In conclusion, MDW has a good diagnostic performance in diagnosing infection and sepsis in patients presenting in ED. Its use as an infection marker even increases when combined with other markers of organ dysfunction. Understanding the impact of interactions of non-infectious conditions and comorbidities on MDW and its diagnostic accuracy requires further elucidation.

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis.

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan-Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Transcriptional markers classifying Escherichia coli and Staphylococcus aureus induced sepsis in adults: A data-driven approach.

Sepsis is a life-threatening condition mainly caused by gram-negative and gram-positive bacteria. Understanding the type of causative agent in the early stages is essential for precise antibiotic therapy. This study sought to identify a host gene set capable of distinguishing between sepsis induced by gram-negative bacteria; Escherichia coli and gram-positive bacteria; Staphylococcus aureus in community-onset adult patients. In the present study, microarray expression information was used to apply the Least Absolute Shrinkage and Selection Operator (Lasso) technique to select the predictive gene set for classifying sepsis induced by E. coli or S. aureus pathogens. We identified 25 predictive genes, including LILRA5 and TNFAIP6, which had previously been associated with sepsis in other research. Using these genes, we trained a logistic regression classifier to distinguish whether a sample contains an E. coli or S. aureus infection or belongs to a healthy control group, and subsequently assessed its performance. The classifier achieved an Area Under the Curve (AUC) of 0.96 for E. coli and 0.98 for S. aureus-induced sepsis, and perfect discrimination (AUC of 1) for healthy controls from the other conditions in a 10-fold cross-validation. The genes demonstrated an AUC of 0.75 in distinguishing between sepsis patients with E. coli and S. aureus pathogens. These findings were further confirmed in two distinct independent validation datasets which gave high prediction AUC ranging from 0.72-0.87 and 0.62 in distinguishing three groups of participants and two groups of patients respectively. These genes were significantly enriched in the immune system, cytokine signaling in immune system, innate immune system, and interferon signaling. Transcriptional patterns in blood can differentiate patients with E. coli-induced sepsis from those with S. aureus-induced sepsis. These diagnostic markers, upon validation in larger trials, may serve as a foundation for a reliable differential diagnostics assay.

[Basic research in sepsis: advances and challenges].

Although the understanding of sepsis has evolved from "sepsis 1.0" to "sepsis 3.0", and the consensus on clinical management of sepsis has been continuously updated, the incidence rate and mortality of sepsis remain high. Therefore, in-depth investigation of the pathogenesis and related influencing factors of sepsis is of great significance for revealing the nature of sepsis and improving the clinical outcome of sepsis patients. This review will focus on the key issues in the basic research of sepsis, and summarize the recent advances and challenges in this field, mainly including genetic polymorphism, microorganisms, high-mobility group box 1 (HMGB1), endothelial dysfunction, immunotherapy, and biomarkers, aiming to provide new insights for the diagnosis and treatment of sepsis.

[Clinical characteristics and prognosis of acute gastrointestinal injury in patients with sepsis-associated acute respiratory distress syndrome].

OBJECTIVE: To observe the clinical characteristics and prognosis of patients with acute respiratory distress syndrome (ARDS) in sepsis combined with acute gastrointestinal injury (AGI) of different grades, and to further explore the risk factors associated with the poor prognosis of patients. METHODS: The clinical data of patients with septic ARDS admitted to the intensive care unit (ICU) of Tianjin First Central Hospital from March to October 2023 were collected. According to the 2012 European Association of Critical Care Medicine AGI definition and grading criteria, the patients were categorized into AGI grade 0- IV groups. The clinical characteristics and 28-day clinical outcomes of the patients were observed; the risk factors related to the prognosis of patients with septic ARDS combined with AGI were analyzed by using univariate and multivariate Logistic regression; and the receiver operator characteristic curve (ROC curve) and calibration curves were plotted to evaluate the predictive value of each risk factor on the prognosis of patients with septic ARDS combined with AGI. RESULTS: A total of 92 patients with septic ARDS were enrolled, including 7 patients in the AGI 0 group, 20 patients in the AGI I group, 38 patients in the AGI II group, 23 patients in the AGI III group, and 4 patients in the AGI IV group. The incidence of AGI was 92.39%. With the increase of AGI grade, the ARDS grade increased, and acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), intra-abdominal pressure (IAP), white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), lymphocyte percentage (LYM%), and 28-day mortality all showed a significant increasing trend, while the oxygenation index (PaO2/FiO2) showed a significant decreasing trend (all P < 0.05). Pearson correlation analysis showed that APACHE II score, SOFA score, and ARDS classification were positively correlated with patients' AGI grade (Pearson correlation index was 0.386, 0.473, and 0.372, respectively, all P < 0.001), and PaO2/FiO2 was negatively correlated with patients' AGI grade (Pearson correlation index was -0.425, P < 0.001). Among the patients with septic ARDS combined with AGI, there were 68 survivors and 17 deaths at 28 days. The differences in APACHE II score, SOFA score, ARDS grade, AGI grade, PaO2/FiO2, IAP, AGI 7-day worst value, length of ICU stay, and total length of hospital stay between the survival and death groups were statistically significant. Univariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.350, 95% confidence interval (95%CI) was 1.071-1.702, P = 0.011], PaO2/FiO2 (OR = 0.964, 95%CI was 0.933-0.996, P = 0.027) and AGI 7-day worst value (OR = 2.103, 95%CI was 1.194-3.702, P = 0.010) were the risk factors for 28-day mortality in patients with septic ARDS combined with AGI. Multivariate Logistic regression analysis showed that SOFA score (OR = 1.384, 95%CI was 1.153-1.661, P < 0.001), PaO2/FiO2 (OR = 0.983, 95%CI was 0.968-0.999, P = 0.035) and AGI 7-day worst value (OR = 1.992, 95%CI was 1.141-3.478, P = 0.015) were the independent risk factors for 28-day mortality in patients with septic ARDS combined with AGI. ROC curve analysis showed that SOFA score, PaO2/FiO2 and AGI 7-day worst value had predictive value for the 28-day prognosis of patients with septic ARDS combined with AGI. The area under the ROC curve (AUC) was 0.824 (95%CI was 0.697-0.950), 0.760 (95%CI was 0.642-0.877) and 0.721 (95%CI was 0.586-0.857), respectively, all P < 0.01; when the best cut-off values of the above metrics were 5.50 points, 163.45 mmHg (1 mmHg≈0.133 kPa), and 2.50 grade, the sensitivities were 94.1%, 94.1%, 31.9%, respectively, and the specificities were 80.9%, 67.6%, 88.2%, respectively. CONCLUSIONS: The incidence of AGI in patients with septic ARDS is about 90%, and the higher the AGI grade, the worse the prognosis of the patients. SOFA score, PaO2/FiO2 and AGI 7-day worst value have a certain predictive value for the prognosis of patients with septic ARDS combined with AGI, among which, the larger the SOFA score and AGI 7-day worst value, and the smaller the PaO2/FiO2, the higher the patients' mortality.

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation.

OBJECTIVE: Cellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratification models have been developed, but their performance in external validation has been unsatisfactory. This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation. DESIGN: This is a retrospective cohort study. SETTING: This study included both the development and the external-validation phases from two independent large cohorts internationally. PARTICIPANTS AND METHODS: A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. The best models were selected and then externally validated in 2506 patients with cellulitis from the YiduCloud database of our university. The performances and robustness of selected models were further compared in the external-validation group by area under the curve (AUC), diagnostic accuracy, sensitivity, specificity and diagnostic OR. PRIMARY OUTCOME MEASURES: The primary outcome of interest in this study was the development based on the Sepsis-3.0 criteria during hospitalisation. RESULTS: Patient characteristics were significantly different between the two groups. In internal validation, XGBoost was the best model, with an AUC of 0.780, and AdaBoost was the worst model, with an AUC of 0.585. In external validation, the AUC of the artificial neural network (ANN) model was the highest, 0.830, while the AUC of the logistic regression (LR) model was the lowest, 0.792. The AUC values changed less in the boosting and ANN models than in the LR model when variables were deleted. CONCLUSIONS: Boosting and neural network models performed slightly better than the LR model and were more robust in complex clinical situations. The results could provide a tool for clinicians to detect hospitalised patients with cellulitis developing sepsis early.

Early detection of low QRS voltage and its association with mortality in patients with sepsis.

Various electrocardiographic changes occur during sepsis, but data on the clinical importance of a low QRS voltage in sepsis are still limited. We aimed to evaluate the association between low QRS voltage identified early in sepsis and mortality in patients with sepsis. Between September 2019 and December 2020, all consecutive adult patients diagnosed with sepsis in the emergency room or general ward at Samsung Medical Center were enrolled. Patients without a 12-lead electrocardiogram recorded within 48 h of recognition of sepsis were excluded. In 432 eligible patients, 12-lead electrocardiogram was recorded within the median of 24 min from the first recognition of sepsis, and low QRS voltage was identified in 115 (26.6%) patients. The low QRS group showed more severe organ dysfunction and had higher levels of N-terminal pro-brain natriuretic peptide. The hospital mortality was significantly higher in the low QRS voltage group than in the normal QRS voltage group (49.6% vs. 28.1%, p < 0.001). Similarly, among the 160 patients who required intensive care unit admission, significantly more patients in the low QRS group died in the intensive care unit (35.9% vs. 18.2%, p = 0.021). Low QRS voltage was associated with increased hospital mortality in patients with sepsis.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment.

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

Assessment of serum proprotein convertase subtilisin/kexin type 9 in pediatric sepsis syndrome.

Sepsis is a life-threatening condition that arises when the body's response to infection causes injury to its tissues and organs. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an enzyme released in response to the drop in cholesterol level occurring in sepsis. Our study aimed to evaluate the prognostic role of serum Proprotein convertase subtilisin/kexin type 9 (PCSK9) level in children with sepsis and severe sepsis. Sixty children were included in this study. They were divided into two groups: 30 children in the sepsis group and 30 in the severe sepsis group. Another 30 apparently healthy children were included as a control group. Blood samples were withdrawn from all included children for complete blood count (CBC), renal function tests (RFT), liver function tests (LFT), LDL-cholesterol (LDL-C), blood culture, and serum PCSK9. In this study, PCSK9 and LDL-C were higher in the two sepsis groups than in the control group (p < 0.05). They were also higher in the severe sepsis group than the sepsis group and in the non-survivors than in the survivors (p < 0.05). PCSK9 was positively correlated with length of hospital stay in surviving children (r = 0.67, p = 0.001) and had predicted significant hematological dysfunction (adjusted B = - 96.95, p = 0.03). In conclusion, the PCSK9 assay can be used as a biomarker for bad prognosis in children suffering from clinical sepsis.

Examining the relationship between alterations in plasma cholesterol, vascular endothelin-1 levels, and the severity of sepsis in children: An observational study.

Considering the significant impact of total cholesterol (TC) and vascular endothelin-1 (ET-1) on children sepsis outcomes, this research aimed to explore the association between the levels of plasma cholesterol and vascular endothelin-1 and the severity of sepsis and evaluated its clinical implications. In this study, we examined 250 pediatric patients diagnosed with sepsis between February 2019 and April 2021, collecting data on their plasma levels of TC and ET-1. Depending on the observed outcomes, the participants were divided into 2 categories: a group with a positive prognosis (control group, n = 100) and a group with a negative prognosis (n = 50). We assessed the significance of plasma TC and ET-1 levels in forecasting the outcomes for these pediatric patients. Patients in the group with a poor prognosis experienced notably longer hospital stays and higher treatment expenses than those in the control group (P < .05). Within the first 24 hours of admission and again on days 3 and 7, the levels of ET-1 were significantly higher in the poor prognosis group, whereas plasma TC levels were notably lower in comparison to the control group (P < .05). A Spearman correlation analysis identified a significant correlation between the levels of plasma TC and ET-1 and the severity of sepsis among the children (P < .05). The diagnostic performance for the severity of sepsis in children, as measured by the area under the curve (AUC), was 0.805 for plasma TC, 0.777 for ET-1 levels, and 0.938 when both were combined. This investigation underscores a meaningful relationship between the levels of plasma TC and ET-1 in pediatric sepsis patients, suggesting these biomarkers are highly valuable in predicting patient outcomes. High levels of ET-1 and low levels of TC in these patients signify a grave condition and a poor prognosis.

Myeloperoxidase and its derivative hypochlorous acid combined clinical indicators predict new-onset atrial fibrillation in sepsis: a case-control study.

BACKGROUD: New-onset atrial fibrillation (NOAF) is a common complication of sepsis and linked to higher death rates in affected patients. The lack of effective predictive tools hampers early risk assessment for the development of NOAF. This study aims to develop practical and effective predictive tools for identifying the risk of NOAF. METHODS: This case-control study retrospectively analyzed patients with sepsis admitted to the emergency department of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from September 2017 to January 2023. Based on electrocardiographic reports and electrocardiogram monitoring records, patients were categorized into NOAF and non-NOAF groups. Laboratory tests, including myeloperoxidase (MPO) and hypochlorous acid (HOCl), were collected, along with demographic data and comorbidities. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were employed to identify predictors. The area under the curve (AUC) was used to evaluate the predictive model's performance in identifying NOAF. RESULTS: A total of 389 patients with sepsis were included in the study, of which 63 developed NOAF. MPO and HOCl levels were significantly higher in the NOAF group compared to the non-NOAF group. Multivariate logistic regression analysis identified MPO, HOCl, tumor necrosis factor-α (TNF-α), white blood cells (WBC), and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score as independent risk factors for NOAF in sepsis. Additionally, a nomogram model developed using these independent risk factors achieved an AUC of 0.897. CONCLUSION: The combination of MPO and its derivative HOCl with clinical indicators improves the prediction of NOAF in sepsis. The nomogram model can serve as a practical predictive tool for the early identification of NOAF in patients with sepsis.

Performance of a Large Language Model in Screening Citations.

Importance: Large language models (LLMs) are promising as tools for citation screening in systematic reviews. However, their applicability has not yet been determined. Objective: To evaluate the accuracy and efficiency of an LLM in title and abstract literature screening. Design, Setting, and Participants: This prospective diagnostic study used the data from the title and abstract screening process for 5 clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock. The LLM decided to include or exclude citations based on the inclusion and exclusion criteria in terms of patient, population, problem; intervention; comparison; and study design of the selected CQ and was compared with the conventional method for title and abstract screening. This study was conducted from January 7 to 15, 2024. Exposures: LLM (GPT-4 Turbo)-assisted citation screening or the conventional method. Main Outcomes and Measures: The sensitivity and specificity of the LLM-assisted screening process was calculated, and the full-text screening result using the conventional method was set as the reference standard in the primary analysis. Pooled sensitivity and specificity were also estimated, and screening times of the 2 methods were compared. Results: In the conventional citation screening process, 8 of 5634 publications in CQ 1, 4 of 3418 in CQ 2, 4 of 1038 in CQ 3, 17 of 4326 in CQ 4, and 8 of 2253 in CQ 5 were selected. In the primary analysis of 5 CQs, LLM-assisted citation screening demonstrated an integrated sensitivity of 0.75 (95% CI, 0.43 to 0.92) and specificity of 0.99 (95% CI, 0.99 to 0.99). Post hoc modifications to the command prompt improved the integrated sensitivity to 0.91 (95% CI, 0.77 to 0.97) without substantially compromising specificity (0.98 [95% CI, 0.96 to 0.99]). Additionally, LLM-assisted screening was associated with reduced time for processing 100 studies (1.3 minutes vs 17.2 minutes for conventional screening methods; mean difference, -15.25 minutes [95% CI, -17.70 to -12.79 minutes]). Conclusions and Relevance: In this prospective diagnostic study investigating the performance of LLM-assisted citation screening, the model demonstrated acceptable sensitivity and reasonably high specificity with reduced processing time. This novel method could potentially enhance efficiency and reduce workload in systematic reviews.

A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.

BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technology for visualizing physicochemical tissue characteristics. Machine learning (ML) for skin HSI might offer an automated approach for bedside microcirculation assessment, providing an individualized tissue fingerprint of critically ill patients in intensive care. The study aimed to determine if machine learning could be utilized to automatically identify regions of interest (ROIs) in the hand, thereby distinguishing between healthy individuals and critically ill patients with sepsis using HSI. METHODS: HSI raw data from 75 critically ill sepsis patients and from 30 healthy controls were recorded using TIVITA® Tissue System and analyzed using an automated ML approach. Additionally, patients were divided into two groups based on their SOFA scores for further subanalysis: less severely ill (SOFA ≤ 5) and severely ill (SOFA > 5). The analysis of the HSI raw data was fully-automated using MediaPipe for ROI detection (palm and fingertips) and feature extraction. HSI Features were statistically analyzed to highlight relevant wavelength combinations using Mann-Whitney-U test and Benjamini, Krieger, and Yekutieli (BKY) correction. In addition, Random Forest models were trained using bootstrapping, and feature importances were determined to gain insights regarding the wavelength importance for a model decision. RESULTS: An automated pipeline for generating ROIs and HSI feature extraction was successfully established. HSI raw data analysis accurately distinguished healthy controls from sepsis patients. Wavelengths at the fingertips differed in the ranges of 575-695 nm and 840-1000 nm. For the palm, significant differences were observed in the range of 925-1000 nm. Feature importance plots indicated relevant information in the same wavelength ranges. Combining palm and fingertip analysis provided the highest reliability, with an AUC of 0.92 to distinguish between sepsis patients and healthy controls. CONCLUSION: Based on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.

Enhancing sepsis biomarker development: key considerations from public and private perspectives.

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

Improving prevention and early detection of sepsis among patient groups at risk: Introducing a model for a multimodal information campaign-The SepWiss study protocol.

BACKGROUND: Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection. Annually, sepsis leads to approx. 90.000 deaths in Germany. Risk factors include amongst others older age (>60), innate or acquired dysfunction of the immune system, and underlying chronic diseases of the lung, heart, liver, or kidneys. The manifestation of sepsis is a medical emergency, and patient outcomes depend on timely diagnosis and immediate treatment. In addition, vaccinations e.g., against pneumococci or influenza virus, are a highly effective public health tool to prevent the most common underlying infections that may lead to sepsis. However, a lack of public awareness for the relevance of vaccination and detecting sepsis as an emergency underlines the need for public health interventions that address these issues. SepWiss aims to evaluate the effects of a multimodal information campaign designed to address this lack of awareness among the risk population in Germany. METHODS: SepWiss is an intervention at state level, consisting of a multimodal information campaign targeting risk groups in the German federal states of Berlin and Brandenburg (intervention region). Based on available evidence, various information formats were developed and implemented by outdoor advertising, social media, educational formats and through stakeholders' platforms, starting in August 2021. The control region comprises of the remaining 14 German federal states. We will analyze vaccination coverage (primary outcome), and sepsis knowledge, the ability to detect sepsis as an emergency, and attitude towards vaccination (secondary outcomes) amongst the risk population in a controlled before-after comparison. The implementation is accompanied by a mixed-method process evaluation. DISCUSSION: SepWiss is the first project of its kind to evaluate a complex multi-faceted evidence-based information campaign with regards to the topics of vaccination coverage, and the importance of sepsis detection and prevention for the most vulnerable populations in Germany. Results will be valuable for informing further nationwide campaigns. TRIAL REGISTRATION: German Registry for Clinical Trials: DRKS00024475. Registered February 24th, 2021.

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression network analysis (WGCNA) for identifying sepsis-related modules and hub genes, and functional enrichment analyses to explore the roles of hub genes. Machine learning algorithms identify 3 diagnostic genes - CD177, LDHA, and MCEMP1 - consistently highly expressed in sepsis patients. The nomogram model effectively predicts sepsis risk, supported by receiver operator characteristic (ROC) curves. Correlations between diagnostic genes and immune cell infiltration are observed. MR analysis reveals a positive causal relationship between MCEMP1 and sepsis risk. In conclusion, this study presents potential sepsis diagnostic biomarkers, highlighting the genetic association of MCEMP1 with sepsis for insights into early diagnosis.

Validating the performance of organ dysfunction scores in children with infection: A cohort study.

PURPOSE: We aimed to validate the performance of six available scoring models for predicting hospital mortality in children with suspected or confirmed infections. METHODS: This single-center retrospective cohort study included pediatric patients admitted to the PICU for infection. The primary outcome was hospital mortality. The six scores included the age-adapted pSOFA score, SIRS score, PELOD2 score, Sepsis-2 score, qSOFA score, and PMODS. RESULTS: Of the 5,356 children admitted to the PICU, 9.1% (488) died, and 25.1% (1,342) had basic disease with a mortality rate of 12.7% (171); 65.3% (3,499) of the patients were younger than 2 years, and 59.4% (3,183) were male. The discrimination abilities of the pSOFA and PELOD2 scores were superior to those of the other models. The calibration curves of the pSOFA and PELOD2 scores were consistent between the predictions and observations. Elevated lactate levels were a risk factor for mortality. CONCLUSION: The pSOFA and PELOD2 scores had superior predictive performance for mortality. Given the relative unavailability of items and clinical operability, the pSOFA score should be recommended as an optimal tool for acute organ dysfunction in pediatric sepsis patients. Elevated lactate levels are related to a greater risk of death from infection in children in the PICU.