Vitamin D Deficiency and Low Serum Calcium as Predictors of Poor Prognosis in Patients with Severe COVID-19.

BACKGROUND: The severity of Coronavirus Disease 2019 (COVID-19) is a multifactorial condition. An increasing body of evidence argues for a direct implication of vitamin D deficiency, low serum calcium on poor outcomes in COVID-19 patients. This study was designed to investigate the relationship between these two factors and COVID-19 in-hospital mortality. MATERIALS: This is a prospective study, including 120 severe cases of COVID-19, admitted at the department of Reanimation-Anesthesia. Vitamin D was assessed by an immuno-fluoroassay method. Total serum calcium by a colorimetric method, then, corrected for serum albumin levels. The association with in-hospital mortality was assessed using the Kaplan-Meier survival curve, proportional Cox regression analyses and the receiver operating characteristic curve. RESULTS: Hypovitaminosis D and hypocalcemia were very common, occurring in 75% and 35.8% of patients. When analyzing survival, both were significantly associated with in-hospital mortality in a dose-effect manner (pLog-Rank = 0.009 and 0.001 respectively). A cutoff value of 39 nmol/l for vitamin D and 2.05 mmol/l for corrected calcemia could predict poor prognosis with a sensitivity of 76% and 84%, and a specificity of 69% and 60% respectively. Hazard ratios were (HR = 6.9, 95% CI [2.0-24.1], p = 0.002 and HR = 6.2, 95% CI [2.1-18.3], p = 0.001) respectively. CONCLUSION: This study demonstrates the high frequency of hypocalcemia and hypovitaminosis D in severe COVID-19 patients and provides further evidence of their potential link to poor short-term prognosis. It is, therefore, possible that the correction of hypocalcemia, as well as supplementation with vitamin D, may improve the vital prognosis.

Development and validation of a laboratory risk score for the early prediction of COVID-19 severity and in-hospital mortality.

BACKGROUND AND AIMS: Coronavirus Disease 2019 is characterized by a spectrum of clinical severity. This study aimed to develop a laboratory score system to identify high-risk individuals, to validate this score in a separate cohort, and to test its accuracy in the prediction of in-hospital mortality. METHODS: In this cohort study, biological data from 330 SARS-CoV-2 infected patients were used to develop a risk score to predict progression toward severity. In a second stage, data from 240 additional COVID-19 patients were used to validate this score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). RESULTS: In the development cohort, a step-wise decrease in the average survival duration was noted with the increment of the risk score (pANOVA < 0.0001). A similar trend was confirmed when analyzing this association in the validation cohort (p < 0.0001). The AUC was 0.74 [0.66-0.82] and 0.90 [0.87-0.94], p < 0.0001, respectively for severity and mortality prediction. CONCLUSION: This study provides a useful risk score based on biological routine parameters assessed at the time of admission, which has proven its effectiveness in predicting both severity and short-term mortality of COVID-19. Improved predictive scores may be generated by including other clinical and radiological features.

Usefulness of biological markers in the early prediction of corona virus disease-2019 severity.

Coronavirus Disease 2019 is a very fast-spreading infectious disease. Severe forms are marked by a high mortality rate. The objective of this study is to identify routine biomarkers that can serve as early predictors of the disease progression. This is a prospective, single-center, cohort study involving 330 SARS-CoV-2 infected patients who were admitted at the University Hospital of Blida, Algeria in the period between the 27th of March and 22nd of April 2020. The ROC curve was used to evaluate the predictive performance of biomarkers, assessed at admission, in the early warning of progression toward severity. Multivariate logistic regression was used to quantify the independent risk for each marker. After an average follow-up period of 13.9 ± 3.5 days, 143 patients (43.3%) were classified as severe cases. Six biological abnormalities were identified as potential risk markers independently related to the severity: elevated urea nitrogen (>8.0 mmol/L, OR = 9.3 [2.7-31.7], p < .00001), elevated CRP (>42mg/L, OR = 7.5 [2.4-23.3], p = .001), decreased natremia (<133. 6 mmol/L, OR = 6.0 [2.0-17.4], p = .001), decreased albumin (<33.5 g/L, OR = 5.2 [1.7-16.6], p = .003), elevated LDH (>367 IU/L, OR = 4.9 [1.7-14.2], p = .003) and elevated neutrophil to lymphocyte ratio (>7.99, OR = 4.2, [1.4-12.2], p = .009). These easy-to-measure, time-saving and very low-cost parameters have been shown to be effective in the early prediction of the COVID-19 severity. Their use at the early admission stage can improve the risk stratification and management of medical care resources in order to reduce the mortality rate.