BackgroundElderly patients infected with COVID-19 are reported to be facing a substantially increased risk of mortality. Clinical characteristics, treatment options, and potential survival factors remain under investigation. This study aimed to fill this gap and provide clinically relevant factors associated with survival of elderly patients with COVID-19.MethodsIn this multi-center study, elderly patients (age ≥65 years old) with laboratory-confirmed COVID-19 from 4 Wuhan hospitals were included. The clinical end point was hospital discharge or deceased with last date of follow-up on Mar. 08, 2020. Clinical, demographic, and laboratory data were collected.Univariate and multivariate analysis were performed to analyze survival and risk factors. A metabolic flux analysis using a large-scale molecular model was applied to investigate the pathogenesis of SARS- CoV-2 with regard to metabolism pathways.ResultsA total of 223 elderly patients infected with COVID-19 were included, 91 (40.8%) were discharged and 132 (59.2%) deceased. Acute respiratory distress syndrome (ARDS) developed in 140 (62.8%) patients, 23 (25.3%) of these patients survived. Multivariate analysis showed that potential risk factors were D- Dimer (odds ratio: 1.13 [95% CI 1.04 - 1.22], p=.005), immune-related metabolic index (6.42 [95% CI 2.66 - 15.48], p<.001), and neutrophil-to-lymphocyte ratio (1.08 [95% 1.03 - 1.13], p<.001). Elderly patients receiving interferon atmotherapy showed an increased probability of survival (0.29 [95% CI0.17 - 0.51], p<.001). Based on these factors, an algorithm (AlgSurv) was developed to predict survival for elderly patients. The metabolic flux analysis showed that 12 metabolic pathways including phenylalanine (odds ratio: 28.27 [95% CI 10.56 - 75.72], p<0.001), fatty acid (15.61 [95% CI 6.66 - 36.6], p<0.001), and pyruvate (12.86 [95% CI 5.85 - 28.28], p<0.001) showed a consistently lower flux in the surviving versus the deceased subgroup. This may reflect a key pathogenesis of COVID-19 infection.ConclusionAlthough a high mortality has been reported for elderly patients with COVID-19, in this analysis, several factors such as interferon atmotherapy and activity of metabolic pathways were found to be associated with survival of elderly patients. Based on these findings, the survival algorithm (AlgSurv) was developed to assist the clinical stratification for elderly patients. Deregulation of metabolic pathways revealed in this study may aid in the drug development against COVID-19.

Background: We aimed to assess the utility of the poisoning severity score (PSS) as early prognostic predictors in patients with wasp stings, and to explore a reliable and simple predictive tool for short-term outcomes.Methods: From January 2016 to December 2018, 363 patients with wasp stings in Suining Central Hospital were taken as research subjects. In the first 24h of hospital admission, the PSS and Chinese expert consensus on standardized diagnosis and treatment of wasp stings (CECC) were used as the criterion for severity classification, and their correlation was analyzed. The patients were divided into survival and death groups according to the state of discharge. The factors that affect outcome were analyzed by logistic regression analysis. A clinical prognostic model of death was constructed according to the risk factors, and 1000 times repeated sampling was done to include the data to verify the model internally.Results: The mortality of wasp sting patients was 3.9%. There was a correlation between PSS and CECC (r=0.435, P<0.001) for severity classification. Sex, age, number of stings, and PSS were independent risk factors for death. Based on the 4 independent risk factors screened by the above regression analysis, a nomogram model was constructed to predict the risk of death in wasp sting patients. The predicted value C-index was 0.962, and the internally verified AUC was 0.962(95%C.I. 0.936-0.988, P<0.001).Conclusions: PSS is helpful in the early classification of the severity of wasp stings. Sex, age, number of stings, and PSS were independent risk factors for death in wasp sting patients. The nomogram model established in this study can accurately predict the occurrence of the risk of death.