Clinical characteristics and peripheral immunocyte subsets alteration of 85 COVID-19 deaths

A deep learning model for predicting COVID-19 ARDS in critically ill patients

Background

The coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection previously unknown to humans. However, predictive studies of acute respiratory distress syndrome (ARDS) in patients with COVID-19 are limited. In this study, we attempted to establish predictive models to predict ARDS caused by COVID-19 via a thorough analysis of patients' clinical data and CT images.

Method

The data of included patients were retrospectively collected from the intensive care unit in our hospital from April 2022 to June 2022. The primary outcome was the development of ARDS after ICU admission. We first established two individual predictive models based on extreme gradient boosting (XGBoost) and convolutional neural network (CNN), respectively; then, an integrated model was developed by combining the two individual models. The performance of all the predictive models was evaluated using the area under receiver operating characteristic curve (AUC), confusion matrix, and calibration plot.

Results

A total of 103 critically ill COVID-19 patients were included in this research, of which 23 patients (22.3%) developed ARDS after admission; five predictive variables were selected and further used to establish the machine learning models, and the XGBoost model yielded the most accurate predictions with the highest AUC (0.94, 95% CI: 0.91-0.96). The AUC of the CT-based convolutional neural network predictive model and the integrated model was 0.96 (95% CI: 0.93-0.98) and 0.97 (95% CI: 0.95-0.99), respectively.

Conclusion

An integrated deep learning model could be used to predict COVID-19 ARDS in critically ill patients.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4⁺ T/CD8⁺ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3⁺ T,CD4⁺T and CD8⁺T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4⁺ and CD8⁺ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

Validation of the T-Lymphocyte Subset Index (TLSI) as a Score to Predict Mortality in Unvaccinated Hospitalized COVID-19 Patients

Lymphopenia has been consistently reported as associated with severe coronavirus disease 2019 (COVID-19). Several studies have described a profound decline in all T-cell subtypes in hospitalized patients with severe and critical COVID-19. The aim of this study was to assess the role of T-lymphocyte subset absolute counts measured at ward admission in predicting 30-day mortality in COVID-19 hospitalized patients, validating a new prognostic score, the T-Lymphocyte Subset Index (TLSI, range 0−2), based on the number of T-cell subset (CD4+ and CD8+) absolute counts that are below prespecified cutoffs. These cutoff values derive from a previously published work of our research group at Policlinico Tor Vergata, Rome, Italy: CD3+CD4+ < 369 cells/μL, CD3+CD8+ < 194 cells/μL. In the present single-center retrospective study, T-cell subsets were assessed on admission to the infectious diseases ward. Statistical analysis was performed using JASP (Version 0.16.2. JASP Team, 2022, Amsterdam, The Netherlands) and Prism8 (version 8.2.1. GraphPad Software, San Diego, CA, USA). Clinical and laboratory parameters of 296 adult patients hospitalized because of COVID-19 were analyzed. The overall mortality rate was 22.3% (66/296). Survivors (S) had a statistically significant lower TLSI score compared to non-survivors (NS) (p < 0.001). Patients with increasing TLSI scores had proportionally higher rates of 30-day mortality (p < 0.0001). In the multivariable logistic analysis, the TLSI was an independent predictor of in-hospital 30-day mortality (OR: 1.893, p = 0.003). Survival analysis showed that patients with a TLSI > 0 had an increased risk of death compared to patients with a TLSI = 0 (hazard ratio: 2.83, p < 0.0001). The TLSI was confirmed as an early and independent predictor of COVID-19 in-hospital 30-day mortality.

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

BACKGROUND AND AIMS

Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022.

RESULTS

In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis.

CONCLUSIONS

NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).

The Value of Early Positive Nucleic Acid Test and Negative Conversion Time of SARS-CoV-2 RNA in the Clinical Outcome of COVID-19 Patients

Background

The outbreak of coronavirus disease (COVID-19) poses a great threat to global public health. At present, the number of newly confirmed COVID-19 cases and deaths is increasing worldwide. The strategy of comprehensive and scientific detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through quantitative real-time polymerase chain reaction (qRT-PCR) for special populations and environments provides great support for the prevention and control of this pandemic in China. Our study focused on determining the factors associated with the length of time from symptom onset to the first positive nucleic acid test of throat swabs in COVID-19 patients, evaluating the effect of early positive nucleic acid detection on the disease severity and its significance in prognosis, and predicting the factors associated with the time from positive SARS-CoV-2 RNA test to negative conversion (negative conversion of SARS-CoV-2 virus) in COVID-19 patients.

Methods

This study included 116 hospitalized patients with COVID-19 from January 30, 2020 to March 4, 2020 in Wuhan, China. Throat swab samples were collected for qRT-PCR testing of SARS-CoV-2 RNA, and all patients included in this study were positive for this test.

Results

The multivariate Cox proportional hazards model showed that disease severity (HR = 0.572; 95% CI 0.348–0.942; p = 0.028) was a protective factor for the time from symptom onset to positive nucleic acid detection. Meanwhile, the time from symptom onset to positive nucleic acid detection (HR = 1.010; 95% CI 1.005–1.020; p = 0.0282) was an independent risk factor for the delay in negative conversion time of SARS-CoV-2 virus. However, the severity of the disease (HR=1.120; 95% CI 0.771–1.640; p = 0.544) had no correlation with the negative conversion time of SARS-CoV-2 virus.

Conclusions

Patients with more severe disease had a shorter time from symptom onset to a positive nucleic acid test. Prolonged time from symptom onset to positive nucleic acid test was an independent risk factor for the delay in negative conversion time of SARS-CoV-2 virus, and the severity of the disease had no correlation with negative conversion time of SARS-CoV-2 virus.

Immunopathology and Immunosenescence, the Immunological Key Words of Severe COVID-19. Is There a Role for Stem Cell Transplantation?

The outcomes of Coronavirus disease-2019 (COVID-19) vary depending on the age, health status and sex of an individual, ranging from asymptomatic to lethal. From an immunologic viewpoint, the final severe lung damage observed in COVID-19 should be caused by cytokine storm, driven mainly by interleukin-6 and other pro-inflammatory cytokines. However, which immunopathogenic status precedes this "cytokine storm" and why the male older population is more severely affected, are currently unanswered questions. The aging of the immune system, i.e., immunosenescence, closely associated with a low-grade inflammatory status called "inflammageing," should play a key role. The remodeling of both innate and adaptive immune response observed with aging can partly explain the age gradient in severity and mortality of COVID-19. This review discusses how aging impacts the immune response to the virus, focusing on possible strategies to rejuvenate the immune system with stem cell-based therapies. Indeed, due to immunomodulatory and anti-inflammatory properties, multipotent mesenchymal stem cells (MSCs) are a worth-considering option against COVID-19 adverse outcomes.