ABSTRACT
Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance.
ABSTRACT
OBJECTIVE: To analyze and predict the effect of coronavirus infection on hematopoietic system and potential intervention drugs, and explore their significance for coronavirus disease 2019 (COVID-19). METHODS: The gene expression omnibus (GEO) database was used to screen the whole genome expression data related with coronavirus infection. The R language package was used for differential expression analysis and KEGG/GO enrichment analysis. The core genes were screened by PPI network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. RESULTS: A database in accordance with the criteria was found, which was derived from SARS coronavirus. A total of 3606 differential genes were screened, including 2148 expression up-regulated genes and 1458 expression down-regulated genes. GO enrichment mainly related with viral infection, hematopoietic regulation, cell chemotaxis, platelet granule content secretion, immune activation, acute inflammation, etc. KEGG enrichment mainly related with hematopoietic function, coagulation cascade reaction, acute inflammation, immune reaction, etc. Ten core genes such as PTPRC, ICAM1, TIMP1, CXCR5, IL-1B, MYC, CR2, FSTL1, SOX1 and COL3A1 were screened by protein interaction network analysis. Ten drugs with potential intervention effects, including glucocorticoid, TNF-α inhibitor, salvia miltiorrhiza, sirolimus, licorice, red peony, famciclovir, cyclosporine A, houttuynia cordata, fluvastatin, etc. were screened by EpiMed plotform. CONCLUSION: SARS coronavirus infection can affect the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened on these grounds are of useful reference significance for the basic and clinical research of COVID-19.