A differential diagnosis model for patients with COVID-19 and non-COVID-19 pneumonia based on laboratory indicators

OBJECTIVE: To retrospectively analyze the clinical characteristics, imaging features and laboratory indexes of the patients with COVID-19 and non-COVID-19 so as to seek for differential diagnosis indexes. METHODS: A total of 66 patients with COVID-19 and 40 non-COVID-19 patients were recruited as study subjects who were treated in the hospital from Jan 2020 to Apr 2020. The demographic data, clinical symptoms, underlying diseases, imaging features, length of hospital stay and laboratory test indexes at the admission were statistically analyzed. RESULTS: The white blood cell(WBC),albumin(ALB) and prealbumin(PALB) of the COVID-19 patients were remarkably lower than those of the non-COVID-19 patients;while the length of hospital stay, aspartate aminotransferase(AST), international normalized ratio(INR), fibrinogen(Fbg), lactate dehydrogenase(LDH), tumor specific growth factor(TSGF) and ferritin(Ferritin) of the COVID-19 group were remarkably higher than those of the non-COVID-19 group. The COVID-19 patients had a higher frequency of air bronchogram, reticular pattern, number of affected lobes and number of affected segments, but a lower frequency of centrilobular nodules than did the non-COVID-19 patients. The length of hospital stay of the COVID-19 patients was positively correlated with the age but was negatively correlated with LYM and ALB, and the length of hospital stay of the patients complicated with diabetes mellitus and hypertension was longer than the patients with other complications. Receiver operating characteristic(ROC) curve analysis showed that the areas under curves of WBC, TSGF, LDH and Ferritin were more than 75% in distinguishing between COVID-19 and non-COVID-19. Multivariate logistic regression analysis showed that TSGF, LDH and Ferritin were the independent factors for distinguishing between COVID-19 and non-COVID-19, and the area under curve of the joint detection of the three indexes was 0.9181. CONCLUSION: The ordinary COVID-19 patients and non-COVID-19 patients vary in some clinical characteristic, imaging features and clinical laboratory indexes. The joint diagnosis model of TSGF, LDH and Ferritin may be used as an effective indicator for distinguishing between ordinary COVID-19 and non-COVID-19.

Elevated frequencies of CD14+HLA-DRlo/neg MDSCs in COVID-19 patients.

BACKGROUND: The immune responses, hyper-inflammation or immunosuppression, may be closely related to COVID-19 progression. We aimed to evaluate the changes of frequency of CD14+HLA-DRlo/neg MDSCs, a population of cells with potent immunosuppressive capacity, in COVID-19 patients. METHODS: The levels of CD14+HLA-DRlo/neg MDSCs were determined by flow cytometry in 27 COVID-19 patients, and their association with clinical characteristics and laboratory data were analyzed. RESULTS: The frequency of CD14+HLA-DRlo/neg MDSCs was elevated in COVID-19 patients, particularly severe patients. A follow-up comparison revealed a decline of CD14+HLA-DRlo/neg MDSCs percentages in most patients 1 day after testing negative for SARS-CoV-2 nucleic acid, but the levels of CD14+HLA-DRlo/neg MDSCs were still greater than 50.0% in 3 ICU patients 4-10 days after negative SARS-CoV-2 results. Elevated frequency of CD14+HLA-DRlo/neg MDSCs was positively correlated with oropharyngeal viral loads and length of hospital stay, while negatively correlated with lymphocyte counts and serum albumin. Moreover, strong correlations were observed between the frequency of CD14+HLA-DRlo/neg MDSCs and T cell subsets, NK cell counts, and B cell percentages. The frequency of CD14+HLA-DRlo/neg MDSCs could be used as a predictor of COVID-19 severity. CONCLUSIONS: A high frequency of CD14+HLA-DRlo/neg MDSCs, especially in severe patients, may indicate an immunoparalysis status and could be a predictor of disease severity and prognosis.

Novel serological biomarkers for inflammation in predicting disease severity in patients with COVID-19.

BACKGROUND: Patients with severe coronavirus disease 2019 (COVID-19) develop acute respiratory distress and multi-system organ failure and are associated with poor prognosis and high mortality. Thus, there is an urgent need to identify early diagnostic and prognostic biomarkers to determine the risk of developing serious illness. METHODS: We retrospectively analyzed 114 patients with COVID-19 at the Jinyintan Hospital, Wuhan based on their clinical and laboratory data. Patients were categorized into severe and mild to moderate disease groups. We analyzed the potential of serological inflammation indicators in predicting the severity of COVID-19 in patients using univariate and multivariate logistic regression, receiver operating characteristic curves, and nomogram analysis. The Spearman method was used to understand the correlation between the serological biomarkers and duration of hospital stay. RESULTS: Patients with severe disease had reduced neutrophils and lymphocytes; severe coagulation dysfunction; altered content of biochemical factors (such as urea, lactate dehydrogenase); elevated high sensitivity C-reactive protein levels, neutrophil-lymphocyte, platelet-lymphocyte, and derived neutrophil-lymphocyte ratios, high sensitivity C-reactive protein-prealbumin ratio (HsCPAR), systemic immune-inflammation index, and high sensitivity C-reactive protein-albumin ratio (HsCAR); and low lymphocyte-monocyte ratio, prognostic nutritional index (PNI), and albumin-to-fibrinogen ratio. PNI, HsCAR, and HsCPAR correlated with the risk of severe disease. The nomogram combining the three parameters showed good discrimination with a C-index of 0.873 and reliable calibration. Moreover, HsCAR and HsCPAR correlated with duration of hospital stay. CONCLUSION: Taken together, PNI, HsCAR, and HsCPAR may serve as accurate biomarkers for the prediction of disease severity in patients with COVID-19 upon admission/hospitalization.