Prediction of Sepsis in COVID-19 Using Laboratory Indicators

Tumor Cell-Derived Exosomal Hybrid Nanosystems Loaded with Rhubarbic Acid and Tanshinone IIA for Sepsis Treatment

Background

Sepsis continues to exert a significant impact on morbidity and mortality in clinical settings, with immunosuppression, multi-organ failure, and disruptions in gut microbiota being key features. Although rheinic acid and tanshinone IIA show promise in mitigating macrophage apoptosis in sepsis treatment, their precise targeting of macrophages remains limited. Additionally, the evaluation of intestinal flora changes following treatment, which plays a significant role in subsequent cytokine storms, has been overlooked. Leveraging the innate inflammation chemotaxis of tumor cell-derived exosomes allows for their rapid recognition and uptake by activated macrophages, facilitating phenotypic changes and harnessing anti-inflammatory effects.

Methods

We extracted exosomes from H1299 cells using a precipitation method. Then we developed a tumor cell-derived exosomal hybrid nanosystem loaded with rhubarbic acid and tanshinone IIA (R+T/Lipo/EXO) for sepsis treatment. In vitro studies, we verify the anti-inflammatory effect and the mechanism of inhibiting cell apoptosis of nano drug delivery system. The anti-inflammatory effects, safety, and modulation of intestinal microbiota by the nanoformulations were further validated in the in vivo study.

Results

Nanoformulation demonstrated enhanced macrophage internalization, reduced TNF-α expression, inhibited apoptosis, modulated intestinal flora, and alleviated immunosuppression.

Conclusion

R+T/Lipo/EXO presents a promising approach using exosomal hybrid nanosystems for treating sepsis.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

Machine learning algorithms in sepsis

Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.

The unfolded protein response pathway as a possible link in the pathogenesis of COVID-19 and sepsis

The global impact of the COVID-19 pandemic has been substantial. Emerging evidence underscores a strong clinical connection between COVID-19 and sepsis. Numerous studies have identified the unfolded protein response (UPR) pathway as a crucial pathogenic pathway for both COVID-19 and sepsis, but it remains to be investigated whether this signaling pathway operates as a common pathogenic mechanism for both COVID-19 and sepsis. In this study, single-cell RNA-seq data and transcriptome data for COVID-19 and sepsis cases were downloaded from GEO (Gene Expression Omnibus). By analyzing the single-cell transcriptome data, we identified B cells as the critical cell subset and the UPR pathway as the critical signaling pathway. Based on the transcriptome data, a machine learning diagnostic model was then constructed using the interleaved genes of B-cell-related and UPR-pathway-related genes. We validated the diagnostic model using both internal and external datasets and found the accuracy and stability of this model to be extremely strong. Even after integrating our algorithmic model with the patient's clinical status, it continued to yield identical results, further emphasizing the reliability of this model. This study provides a novel molecular perspective on the pathogenesis of sepsis and COVID-19 at the single-cell level and suggests that these two diseases may share a common mechanism.

Risk Factors for Radiological Progression Within Admissive One Week in the Hospitalized COVID-19 Omicron Variant-Infected Patients

Purpose

Recently, the SARS-CoV-2 Omicron variant was identified as responsible for a novel wave of COVID-19 worldwide. We perform a retrospective study to identify potential risk factors contributing to radiological progression in the COVID-19 patients due to the Omicron variant infection. These findings would provide guiding information for making clinical decisions that could improve the Omicron infection prognosis and reduce disease-related death.

Methods

This is a retrospective cohort study from a single center in China. According to the radiological change within admissive one week, enrolled cases were divided into two groups: the progressive (1w-PD) and the stable or improved disease (1w-non-PD). Separate analyses were performed on patients stratified into subgroups using the Mann-Whitney U-test, the Fisher exact test, or the Chi-squared test and a multivariable logistic regression analysis.

Results

Both the 1w-non-PD and 1w-PD cohorts displayed comparable asymptomatic infection, have similar underlying disease, impairment in respiratory function, coagulation dysfunction, tissue injury, SARS-CoV-2 viral load, and disease severity. However, the 1w-PD cohort was more inclined to cluster in populations presented with age between 41 and 65, higher CURB-65 scores, undetectable SARS-CoV-2 IgG, and lung affection. Based on the multiple logistic regression analysis, complicated bilateral and ground-glass opacities (GGOs) like pneumonia at admission were independent risk factors to radiological progression within admissive one week.

Conclusion

This study provided preliminary data regarding disease progression in Omicron-infected patients that indicated the development of pneumonia in the context of Omicron infection was worthy of potential risk factors.

Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission

INTRODUCTION

Septic patients requiring intensive care unit (ICU) readmission are at high risk of mortality, but research focusing on the association of ICU readmission due to sepsis and mortality is limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting in-hospital mortality in septic patients readmitted to the ICU using routinely available clinical data.

METHODS

The data used in this study were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV, v1.0) database, between 2008 and 2019. The study cohort included patients with sepsis requiring ICU readmission. The data were randomly split into a training (75%) data set and a validation (25%) data set. Nine popular ML models were developed to predict mortality in septic patients readmitted to the ICU. The model with the best accuracy and area under the curve (A.C.) in the validation cohort was defined as the optimal model and was selected for further prediction studies. The SHAPELY Additive explanations (SHAP) values and Local Interpretable Model-Agnostic Explanation (LIME) methods were used to improve the interpretability of the optimal model.

RESULTS

A total of 1117 septic patients who had required ICU readmission during the study period were enrolled in the study. Of these participants, 434 (38.9%) were female, and the median (interquartile range [IQR]) age was 68.6 (58.4-79.2) years. The median (IQR) ICU interval duration was 2.60 (0.64-5.78) days. After feature selection, 31 of 47 clinical factors were ultimately chosen for use in model construction. Of the nine ML models tested, the best performance was achieved with the random forest (RF) model, with an A.C. of 0.81, an accuracy of 85% and a precision of 62% in the validation cohort. The SHAP summary analysis revealed that Glasgow Coma Scale score, urine output, blood urea nitrogen, lactate, platelet count and systolic blood pressure were the top six most important factors contributing to the RF model. Additionally, the LIME method demonstrated how the RF model works in terms of explaining risk of death prediction in septic patients requiring ICU readmission.

CONCLUSION

The ML models reported here showed a good prognostic prediction ability in septic patients requiring ICU readmission. Of the features selected, the parameters related to organ perfusion made the largest contribution to outcome prediction during ICU readmission in septic patients.