Septic shock 3.0 criteria application in severe COVID-19 patients: An unattended sepsis population with high mortality risk

Syndecan-1 levels predict septic shock in critically ill patients with COVID-19

BACKGROUND

The clinical picture of coronavirus disease 2019 (COVID-19)-associated sepsis is similar to that of sepsis of other aetiologies. The present study aims to analyse the role of syndecan-1 (SDC-1) as a potential predictor of septic shock in critically ill patients with COVID-19.

METHODS

This is a prospective study of 86 critically ill patients due to COVID-19 infection. Patients were followed until day 28 of hospitalization. Vascular biomarkers, such as vascular cell adhesion protein-1, SDC-1, angiopoietin-1 and angiopoietin-2, were quantified upon admission and associated with the need for vasopressors in the first 7 d of hospitalization.

RESULTS

A total of 86 patients with COVID-19 (mean age 60±16 y; 51 men [59%]) were evaluated. Thirty-six (42%) patients died during hospitalization and 50 (58%) survived. The group receiving vasopressors had higher levels of D-dimer (2.46 ng/ml [interquartile range {IQR} 0.6-6.1] vs 1.01 ng/ml [IQR 0.62-2.6], p=0.019) and lactate dehydrogenase (929±382 U/l vs 766±312 U/l, p=0.048). The frequency of deaths during hospitalization was higher in the group that received vasoactive amines in the first 24 h in the intensive care unit (70% vs 30%, p=0.002). SDC-1 levels were independently associated with the need for vasoactive amines, and admission values >269 ng/ml (95% CI 0.524 to 0.758, p=0.024) were able to predict the need for vasopressors during the 7 d following admission.

CONCLUSIONS

Syndecan-1 levels predict septic shock in critically ill patients with COVID-19.

More attention should be paid to Omicron-associated sepsis: a multicenter retrospective study in south China

Background

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible but causes less severe disease compared to other variants. However, its association with sepsis incidence and outcomes is unclear. This study aimed to investigate the incidence of Omicron-associated sepsis, as per the Sepsis 3.0 definition, in hospitalized patients, and to explore its relationship with clinical characteristics and prognosis.

Methods

This multicenter retrospective study included adults hospitalized with confirmed SARS-CoV-2 infection across six tertiary hospitals in Guangzhou, China from November 2022 to January 2023. The Sequential Organ Failure Assessment (SOFA) score and its components were calculated at hospital admission to identify sepsis. Outcomes assessed were need for intensive care unit (ICU) transfer and mortality. Receiver operating characteristic curves evaluated the predictive value of sepsis versus other biomarkers for outcomes.

Results

A total of 299 patients (mean age: 70.1±14.4 years, 42.14% female) with SOFA score were enrolled. Among them, 152 were categorized as non-serious cases while the others were assigned as the serious group. The proportion of male patients, unvaccinated patients, patients with comorbidity such as diabetes, chronic cardiovascular disease, and chronic lung disease was significantly higher in the serious than non-serious group. The median SOFA score of all enrolled patients was 1 (interquartile range, 0-18). In our study, 147 patients (64.19%) were identified as having sepsis upon hospital admission, with the majority of these septic patients (113, representing 76.87%) being in the serious group, the respiratory, coagulation, cardiovascular, central nervous, and renal organ SOFA scores were all significantly higher in the serious compared to the non-serious group. Among septic patients, 20 out of 49 (40.81%) had septic shock as indicated by lactate measurement within 24 hours of admission, and the majority of septic patients were in the serious group (17/20, 76.87%). Sepsis was present in 118 out of 269 (43.9%) patients in the general ward, and among those with sepsis, 34 out of 118 (28.8%) later required ICU care during hospitalization. By contrast, none of the patients without sepsis required ICU care. Moreover, the mortality rate was significantly higher in patients with than without sepsis.

Conclusions

A considerable proportion of patients infected with Omicron present with sepsis upon hospital admission, which is associated with a poorer prognosis. Therefore, early recognition of viral sepsis by evaluation of the SOFA score in hospitalized coronavirus disease 2019 patients is crucial.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

BACKGROUND

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes.

OBJECTIVE

The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management.

METHODS

This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission.

RESULTS

A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83.

CONCLUSIONS

Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Extracellular Vesicles: New Players in the Mechanisms of Sepsis- and COVID-19-Related Thromboinflammation

Sepsis and COVID-19 patients often manifest an imbalance in inflammation and coagulation, a complex pathological mechanism also named thromboinflammation, which strongly affects patient prognosis. Extracellular vesicles (EVs) are nanoparticles released by cells into extracellular space that have a relevant role in cell-to-cell communication. Recently, EVs have been shown to act as important players in a variety of pathologies, including cancer and cardiovascular disease. The biological properties of EVs in the mechanisms of thromboinflammation during sepsis and COVID-19 are still only partially known. Herein, we summarize the current experimental evidence on the role of EVs in thromboinflammation, both in bacterial sepsis and in COVID-19. A better understanding of EV involvement in these processes could be useful in describing novel diagnostic and therapeutic applications of EVs in these diseases.