Two novel nomograms based on inflammatory cytokines or lymphocyte subsets to differentially diagnose severe or critical and Non-Severe COVID-19

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.

Establishment of prediction models for COVID-19 patients in different age groups based on Random Forest algorithm

BACKGROUND

Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Age is an independent factor in death from the disease, and predictive models to stratify patients according to their mortality risk are needed.

AIM

To compare the laboratory parameters of the younger (≤70) and the elderly (>70) groups, and develop death prediction models for the two groups according to age stratification.

DESIGN

A retrospective, single-center observational study.

METHODS

This study included 437 hospitalized patients with laboratory-confirmed COVID-19 from Tongji Hospital in Wuhan, China, 2020. Epidemiological information, laboratory data and outcomes were extracted from electronic medical records and compared between elderly patients and younger patients. First, recursive feature elimination (RFE) was used to select the optimal subset. Then, two random forest (RF) algorithms models were built to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses.

RESULTS

Comparisons of the laboratory data of the two age groups revealed many different laboratory indicators. RFE was used to select the optimal subset for analysis, from which 11 variables were screened out for the two groups. The RF algorithm were built to predict the prognoses of COVID-19 patients based on the best subset, and the area under ROC curve (AUC) of the two groups is 0.874 (95% CI: 0.833-0.915) and 0.842 (95% CI: 0.765-0.920).

CONCLUSION

Two prediction models for COVID-19 were developed in the patients with COVID-19 based on random forest algorithm, which provides a simple tool for the early prediction of COVID-19 mortality.