Objective To evaluate the nutritional risk and therapy in severe and critical patients with COVID-19. Methods A total of 523 patients enrolled from four hospitals in Wuhan, China. The inclusion time was from January 2, 2020 to February 15. Clinical characteristics and laboratory values were obtained from electronic medical records, nursing records, and related examinations. Results Of these patients, 211 (40.3%) were admitted to the ICU and 115 deaths (22.0%). Patients admitted to the ICU had lower BMI and plasma protein levels. The median Nutrition risk in critically ill (NUTRIC) score of 211 patients in the ICU was 5 (4, 6) and Nutritional Risk Screening (NRS) score was 5 (3, 6). The ratio of parenteral nutrition (PN) therapy in non-survivors was greater than that in survivors, and the time to start nutrition therapy was later than that in survivors. The NUTRIC score can independently predict the risk of death in the hospital (OR = 1.197, 95%CI: 1.091–1.445, p = 0.006) and high NRS score patients have a higher risk of poor outcome in the ICU (OR = 1.880, 95%CI: 1.151–3.070, p = 0.012). After adjusted age and sex, for each standard deviation increase in BMI, the risk of in-hospital death was reduced by 13% (HR = 0.871, 95%CI: 0.795–0.955, p = 0.003), and the risk of ICU transfer was reduced by 7% (HR = 0.932, 95%CI:0.885–0.981, p = 0.007). The in-hospital survival time of patients with albumin level ≤35 g/L was significantly decreased (15.9 d, 95% CI: 13.7–16.3, vs 24.2 d, 95% CI: 22.3–29.7, p < 0.001). Conclusion Severe and critical patients with COVID-19 have a high risk of malnutrition. Low BMI and protein levels were significantly associated with adverse events. Early nutritional risk screening and therapy for patients with COVID-19 are necessary.

Objective To investigate the prognostic value of serum amyloid A (SAA) in the patients with Corona Virus Disease 2019 (COVID-19). Methods The medical data of 89 COVID-19 patients admitted to Renmin Hospital of Wuhan University from January 3, 2020 to February 26, 2020 were collected. Eighty-nine cases were divided into survival group (53 cases) and non-survival group (36 cases) according to the results of 28-day follow-up. The SAA levels of all patients were recorded and compared on 1 day after admission (before treatment) and 3 days, 5 days, and 7 days after treatment. The ROC curve was drawn to analyze the prognosis of patients with COVID-19 by SAA. Results The difference of comparison of SAA between survival group and non-survival group before treatment was not statistically significant, Z 1 = − 1.426, P = 0.154. The Z 1 values (Z 1 is the Z value of the rank sum test) of the two groups of patients at 3 days, 5 days, and 7 days after treatment were − 5.569, − 6.967, and − 7.542, respectively. The P values were all less than 0.001, and the difference was statistically significant. The ROC curve results showed that SAA has higher sensitivity to the prognostic value of 1 day (before treatment), 3 days, 5 days, and 7 days after treatment, with values of 0.806, 0.972, 0.861, and 0.961, respectively. Compared with SAA on the 7th day and C-reactive protein, leukocyte count, neutrophil count, lymphocyte count, and hemoglobin on the 7th day, the sensitivities were: 96.1%, 83.3%, 88.3%, 83.3%, 67.9%, and 83.0%, respectively, of which SAA has the highest sensitivity. Conclusion SAA can be used as a predictor of the prognosis in patients with COVID-19.

Background Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials. Methods Based on the transmission mechanism of COVID-19 in the population and the implemented prevention and control measures, we establish the dynamic models of the six chambers, and establish the time series models based on different mathematical formulas according to the variation law of the original data. Findings The results based on time series analysis and kinetic model analysis show that the cumulative diagnosis of pneumonia of COVID-19 in mainland China can reach 36,343 after one week (February 8, 2020), and the number of basic regenerations can reach 4.01. The cumulative number of confirmed diagnoses will reach a peak of 87,701 on March 15, 2020; the number of basic regenerations in Wuhan will reach 4.3, and the cumulative number of confirmed cases in Wuhan will reach peak at 76,982 on March 20. Whether in Mainland China or Wuhan, both the infection rate and the basic regeneration number of COVID-19 continue to decline, and the results of the sensitivity analysis show that the time it takes for a suspected population to be diagnosed as a confirmed population can have a significant impact on the peak size and duration of the cumulative number of diagnoses. Increased mortality leads to additional cases of pneumonia, while increased cure rates are not sensitive to the cumulative number of confirmed cases. Interpretation Chinese governments at various levels have intervened in many ways to control the epidemic. According to the results of the model analysis, we believe that the emergency intervention measures adopted in the early stage of the epidemic, such as blocking Wuhan, restricting the flow of people in Hubei province, and increasing the support to Wuhan, had a crucial restraining effect on the original spread of the epidemic. It is a very effective prevention and treatment method to continue to increase investment in various medical resources to ensure that suspected patients can be diagnosed and treated in a timely manner. Based on the results of the sensitivity analysis, we believe that enhanced treatment of the bodies of deceased patients can be effective in ensuring that the bodies themselves and the process do not result in additional viral infections, and once the pneumonia patients with the COVID-19 are cured, the antibodies left in their bodies may prevent them from reinfection COVID-19 for a longer period of time.

Background: Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials. Methods: Based on the transmission mechanism of COVID-19 in the population and the implemented prevention and control measures, we establish the dynamic models of the six chambers, and establish the time series models based on different mathematical formulas according to the variation law of the original data. Findings: The results based on time series analysis and kinetic model analysis show that the cumulative diagnosis of pneumonia of COVID-19 in mainland China can reach 36,343 after one week (February 8, 2020), and the number of basic regenerations can reach 4.01. The cumulative number of confirmed diagnoses will reach a peak of 87,701 on March 15, 2020; the number of basic regenerations in Wuhan will reach 4.3, and the cumulative number of confirmed cases in Wuhan will reach peak at 76,982 on March 20. Whether in Mainland China or Wuhan, both the infection rate and the basic regeneration number of COVID-19 continue to decline, and the results of the sensitivity analysis show that the time it takes for a suspected population to be diagnosed as a confirmed population can have a significant impact on the peak size and duration of the cumulative number of diagnoses. Increased mortality leads to additional cases of pneumonia, while increased cure rates are not sensitive to the cumulative number of confirmed cases. Interpretation: Chinese governments at various levels have intervened in many ways to control the epidemic. According to the results of the model analysis, we believe that the emergency intervention measures adopted in the early stage of the epidemic, such as blocking Wuhan, restricting the flow of people in Hubei province, and increasing the support to Wuhan, had a crucial restraining effect on the original spread of the epidemic. It is a very effective prevention and treatment method to continue to increase investment in various medical resources to ensure that suspected patients can be diagnosed and treated in a timely manner. Based on the results of the sensitivity analysis, we believe that enhanced treatment of the bodies of deceased patients can be effective in ensuring that the bodies themselves and the process do not result in additional viral infections, and once the pneumonia patients with the COVID-19 are cured, the antibodies left in their bodies may prevent them from reinfection COVID-19 for a longer period of time.