A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults

Clinical decision support tools useful for identifying sepsis risk

PURPOSE

Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms.

METHODS

This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity.

RESULTS

Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups.

CONCLUSION

While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

BACKGROUND

This study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis.

METHODS

We enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model's output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality.

RESULTS

3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55-77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model.

CONCLUSIONS

This study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

Advances in sepsis biomarkers

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

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.

Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model

Introduction

The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis.

Methods

A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared.

Results

A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively.

Conclusion

The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

Decreased NK cell count is a high-risk factor for convulsion in children with COVID-19

BACKGROUND

The neurological symptoms caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of increasing concern. Convulsions are among the main neurological manifestations reported in children with coronavirus disease-2019 (COVID-19), and cause serious harm to physical and mental health. This study aimed to investigate the risk factors for convulsion in children with COVID-19.

METHODS

This prospective study was conducted at the Children's Hospital of Soochow University. In total, 102 COVID-19 patients with convulsion, 172 COVID-19 patients without convulsion, and 50 healthy controls were enrolled in the study. The children's clinical and laboratory data were analyzed to assess the risk factors for convulsion in COVID-19 patients.

RESULTS

Convulsions occurred in 37.2% of children, mostly those aged 1-3 years, who were hospitalized with the Omicron variant. The neutrophil count, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume-to-platelet ratio (MPR) were significantly higher in the convulsion group than those in the non-convulsion and control groups (P < 0.01). However, the counts of lymphocytes, eosinophils, platelets, lymphocyte subsets, CD3+ T cells, CD4+ T cells, CD8+ T cells, and NK cells were lower in the convulsion group than those in the non-convulsion and control groups (P < 0.01). Multivariate regression analysis indicated that NK cell count (OR = 0.081, 95% CI: 0.010-0.652) and a history of febrile seizure (OR = 10.359, 95% CI: 2.115-50.746) were independent risk factors for the appearance of convulsions in COVID-19.

CONCLUSIONS

History of febrile seizure and decreased NK cell count were high-risk factors for convulsions in COVID-19 patients.

Monocyte anisocytosis corresponds with increasing severity of COVID-19 in children

Introduction

Although SARS-CoV-2 infection can lead to severe COVID-19 in children, the role of biomarkers for assessing the risk of progression to severe disease is not well established in the pediatric population. Given the differences in monocyte signatures associated with worsening COVID-19 in adults, we aimed to determine whether monocyte anisocytosis early in the infectious course would correspond with increasing severity of COVID-19 in children.

Methods

We performed a multicenter retrospective study of 215 children with SARS-CoV-2 infection, Multisystem Inflammatory Syndrome in Children (MIS-C), convalescent COVID-19, and healthy age-matched controls to determine whether monocyte anisocytosis, quantified by monocyte distribution width (MDW) on complete blood count, was associated with increasing severity of COVID-19. We performed exploratory analyses to identify other hematologic parameters in the inflammatory signature of pediatric SARS-CoV-2 infection and determine the most effective combination of markers for assessing COVID-19 severity in children.

Results

Monocyte anisocytosis increases with COVID-19 severity and need for hospitalization. Although other inflammatory markers such as lymphocyte count, neutrophil/lymphocyte ratio, C-reactive protein, and cytokines correlate with disease severity, these parameters were not as sensitive as MDW for identifying severe disease in children. An MDW threshold of 23 offers a sensitive marker for severe pediatric COVID-19, with improved accuracy when assessed in combination with other hematologic parameters.

Conclusion

Monocyte anisocytosis corresponds with shifting hematologic profiles and inflammatory markers in children with COVID-19, and MDW serves as a clinically accessible biomarker for severe COVID-19 in children.

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

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

Hematological characteristics of COVID-19 patients with fever infected by the Omicron variant in Shanghai: A retrospective cohort study in China

BACKGROUND

A wave of the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly spread in Shanghai, China. Hematological abnormalities have been reported in coronavirus disease 2019 (COVID-19) patients; however, the difference in hematological parameters between COVID-19 patients with fever and patients who are febrile from other causes remains unexplored.

METHODS

This retrospective cohort study enrolled 663 SARS-CoV-2 positive patients identified by RT-PCR. Clinical parameters, including age, sex, and threshold cycle values of all COVID-19 patients, and hematological parameters of COVID-19 patients in the fever clinic were abstracted for analysis.

RESULTS

Overall, 60.8% of COVID-19 patients were male, and the median age was 45 years. Most of COVID-19 patients were asymptomatic, while 25.8% of patients showed fever and 10.9% of patients had other emergencies. COVID-19 patients with fever had significantly lower white blood cells (WBCs), neutrophils, lymphocytes, platelets and C-reactive protein (CRP), and significantly higher monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV), and mean platelet volume-to-platelet ratio (MPR) levels, compared with those in SARS-CoV-2 negative patients with fever from other causes (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), PLR, and systemic inflammatory index (SII) levels were significantly higher in COVID-19 patients with emergencies (p < 0.05). WBCs showed the best performance with an area under the curve (0.756), followed by neutrophils (0.730) and lymphocytes (0.694) in the diagnosis of COVID-19 in the fever clinic.

CONCLUSION

WBCs, neutrophils, lymphocytes, platelets, CRP and MLR, PLR, and MPR may be useful in early diagnosis of COVID-19 in the fever clinic.