Article ; Online: Analysis of the predictive factors for a critical illness of COVID-19 during treatment － relationship between serum zinc level and critical illness of COVID-19 −
International Journal of Infectious Diseases, Vol 100, Iss , Pp 230-
2020 Volume 236
Abstract: Objectives: Because most severely ill patients with COVID-19 in our hospital showed zinc deficiency, we aimed to examine the relationship between the patient’s serum zinc level and severe cases of COVID-19. Methods: Serum zinc <70 μg/dL was defined as ... ...
| Abstract | Objectives: Because most severely ill patients with COVID-19 in our hospital showed zinc deficiency, we aimed to examine the relationship between the patient’s serum zinc level and severe cases of COVID-19. Methods: Serum zinc <70 μg/dL was defined as the criterion for hypozincemia, and patients continuously with serum zinc <70 μg/dL were classified in the hypozincemia cohort. To evaluate whether hypozincemia could be a predictive factor for a critical illness of COVID-19, we performed a multivariate analysis by employing logistic regression analysis. Results: Prolonged hypozincemia was found to be a risk factor for a severe case of COVID-19. In evaluating the relationship between the serum zinc level and severity of patients with COVID-19 by multivariate logistic regression analysis, critical illness can be predicted through the sensitivity and false specificity of a ROC curve with an error rate of 10.3% and AUC of 94.2% by only two factors: serum zinc value (P = 0.020) and LDH value (P = 0.026). Conclusions: Proper management of the prediction results in this study can contribute to establishing and maintaining a safe medical system, taking the arrival of the second wave, and the spread of COVID-19 in the future into consideration. |
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| Keywords | COVID-19 ; Serum zinc ; Predictive factors ; Logistic regression analysis ; Critical illness ; Japan ; Infectious and parasitic diseases ; RC109-216 ; covid19 |
| Subject code | 630 |
| Language | English |
| Publishing date | 2020-11-01T00:00:00Z |
| Publisher | Elsevier |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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