Background The worldwide spread of SARS-CoV-2 has infected millions of people leading to over 0.3 million mortalities. The disruption of sodium homeostasis, tends to be a common occurrence in patients with COVID-19. Methods and results A total of 1,254 COVID-19 patients comprising 124 (9.9%) hyponatremic patients (under 135 mmol/L) and 30 (2.4%) hypernatremic patients (over 145 mmol/L) from three hospitals in Hubei, China, were enrolled in the study. The relationships between sodium balance disorders in COVID-19 patients, its clinical features, implications, and the underlying causes were presented. Hyponatremia patients were observed to be elderly, had more comorbidities, with severe pneumonic chest radiographic findings. They were also more likely to have a fever, nausea, higher leukocyte and neutrophils count, and a high sensitivity C-reactive protein (HS-CRP). Compared to normonatremia patients, renal insufficiency was common in both hyponatremia and hypernatremia patients. In addition, hyponatremia patients required extensive treatment with oxygen, antibiotics, and corticosteroids. The only significant differences between the hypernatremia and normonatremia patients were laboratory findings and clinical complications, and patients with hypernatremia were more likely to use traditional Chinese medicine for treatment compared to normonatremia patients. This study indicates that severity of the disease, the length of stay in the hospital of surviving patients, and mortality were higher among COVID-19 patients with sodium balance disorders. Conclusion Sodium balance disorder, particularly hyponatremia, is a common condition among hospitalized patients with COVID-19 in Hubei, China, and it is associated with a higher risk of severe illness and increased in-hospital mortality.

Early identification of severe patients with coronavirus disease 2019 (COVID-19) is very important for individual treatment. We included 203 patients with COVID-19 by propensity score matching in this retrospective, case-control study. The effects of serum lactate dehydrogenase (LDH) at admission on patients with COVID-19 were evaluated. We found that serum LDH levels had a 58.7% sensitivity and 82.0% specificity, based on a best cut-off of 277.00 U/L, for predicting severe COVID-19. And a cut-off of 359.50 U/L of the serum LDH levels resulted in a 93.8% sensitivity, 88.2% specificity for predicting death of COVID-19. Additionally, logistic regression analysis and Cox proportional hazards model respectively indicated that elevated LDH level was an independent risk factor for the severity (HR: 2.73, 95% CI: 1.25-5.97; P=0.012) and mortality (HR: 40.50, 95% CI: 3.65-449.28; P=0.003) of COVID-19. Therefore, elevated LDH level at admission is an independent risk factor for the severity and mortality of COVID-19. LDH can assist in the early evaluating of COVID-19. Clinicians should pay attention to the serum LDH level at admission for patients with COVID-19.

Objectives To investigate laboratory markers for COVID‐19 progression in patients with different medical conditions. Methods We performed a multicenter retrospective study of 836 cases in Hubei. To avoid the collinearity among the indicators, principal component analysis (PCA) followed by partial least squares discriminant analysis (PLS‐DA) was performed to obtain an overview of laboratory assessments. Multivariable logistic regression analysis and multivariable Cox proportional hazards regression analysis were respectively used to explore risk factors associated with disease severity and mortality. Survival analysis was performed in patients with the most common comorbidities. Results Lactate dehydrogenase (LDH) and prealbumin were associated with disease severity in patients with or without comorbidities, indicated by both PCA/PLS‐DA and multivariable logistic regression analysis. The mortality risk was associated with age, LDH, C‐reactive protein (CRP), D‐dimer, and lymphopenia in patients with comorbidities. CRP was a risk factor associated with short‐term mortality in patients with hypertension, but not liver diseases; additionally, D‐dimer was a risk factor for death in patients with liver diseases. Conclusions Lactate dehydrogenase was a reliable predictor associated with COVID‐19 severity and mortality in patients with different medical conditions. Laboratory biomarkers for mortality risk were not identical in patients with comorbidities, suggesting multiple pathophysiological mechanisms following COVID‐19 infection.

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ObjectiveThis study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.Web calculatorWe provided a freely accessible web calculator (https://www.whuyijia.com/).