Analysis of Prognostic Factors and Establishment of a Nomogram to Predict Risk for COVID-19 Convalescent Patients Based on Metabolomic and Lipidomic (preprint)

Ethnopharmacological relevance: Qimai Feiluoping Mixture (QM) is a traditional Chinese herbal formulation that has demonstrated efficacy in improving both clinical symptoms and radiological indications of pulmonary fibrosis in patients convalescing from Coronavirus Disease 2019 (COVID-19).Aim of the study: To analyze factors associated with the prognosis of COVID-19 patients. It seeks to develop and validate a nomogram based on metabolomic and lipidomic for predicting improvements in lung imaging in COVID-19 patients. Additionally, the study evaluates the clinical application value of this nomogram.Methods and materials: A retrospective analysis was conducted on the clinical data of COVID-19 recovery patients from January 2020 to April 2022. Non-targeted metabolomic and lipidomic plasma analysis of the patients were performed using LC-MS and normal phase (NP)-HPLC coupled with mass spectrometry. Patients were divided into training and validation sets in a 7:3 ratio based on their omics data. Multivariate logistic regression analysis was conducted to identify independent risk factors associated with the recovery of lung imaging. Based on these factors, a nomogram prediction model was developed. The efficacy of the model was evaluated using receiver operating characteristic (ROC) curves and calibration curves. In addition, decision curve analysis (DCA) was performed to assess the performance of the predictive model in clinical applications.Results The use of QM was found to be associated with the recovery of lung imaging in COVID-19 patients (P < 0.05). Among the 75 metabolites detected in the metabolomic test and 32 lipids identified in the lipidomic test, Pro Ser Ser Val, PC36:1(18:0_18:1), and BMP36:3(18:2_18:1) were utilized for constructing the predictive model. The model demonstrated good discriminative ability, with an Area Under the Curve (AUC) of 0.821 (95% CI: 0.718–0.924) in the training set and 0.808 (95% CI: 0.627–0.989) in the validation set. The calibration curves indicated good agreement between predicted probabilities and actual probabilities in both the training and validation sets. Finally, the DCA curve suggested that the model has good clinical utility.Conclusion The utilization of QM may beneficially influence the recovery of lung imaging in patients with COVID-19. A straightforward nomogram, developed based on metabolomic and lipidomic, could be a valuable tool for clinicians to predict the likelihood of lung imaging recovery in COVID-19 patients.

Spatiotemporal characteristics of coronavirus disease 2019 in 258 cities in China

Objective: To explore the spatiotemporal characteristics of coronavirus disease 2019 (COVID-19) in China and related factors, and provide evidence for COVID-19 prevention and control. Methods Based on the information about COVID-19 incidence, population migration from Wuhan of Hubei, socio-demography and geography in 258 prefecture-level cities in China from January 21 to March 23, 2020, a spatial autocorrelation analysis and a hot spot analysis were conducted to explore the spatial heterogeneity and identify the hot spots of COVID-19 epidemic. The geographically weighted regression (GWR) model combined with linear regression model was used to identify the related factors for spatial heterogeneity. Results During January 21 to March 23, 2020, a total of 29 789 COVID-19 cases were reported in 258 cities. The overall incidence of COVID-19 showed spatial clustering (Moran's I=0.436, Z=25.363, P < 0.001). The Baidu migration index (from Wuhan) was statistically related to COVID-19 incidence at city level (t=14.550, P < 0.001), showing positive effect (beta: 0.564-0.565). Conclusion Spatial heterogeneity was noted in COVID-19 epidemic in China. Cities with more migration from Wuhan were more likely to report higher COVID-19 incidence. Understanding spatiotemporal characteristics of emerging infectious diseases is helpful to the early warning and control of the epidemic.