The Effect of Blood Lactate Level on Mortality in COVID-19 Positive Patients

Aim: We aimed to investigate the relationship between increased lactate values and mortality in COVID-19 patients. Materials and Methods: This study was conducted in a tertiary training and research hospital. According to the order of application, a total of 316 patients over the age of 18 who were admitted to the emergency department (ED) with symptoms of COVID-19 during the two months and whose data could be completely accessed were included in the study retrospectively. Plasma lactate values and mortality within 28 days were determined. Results: The median age of the patients was 69 years. Of the patients, 53.5% were male, 72.2% had comorbidities, and the most common comorbidity was COPD (13.0%). Of the patients, 83.5% were hospitalized. The mean lactate value of the patients was 2.05 +/- 1.45mmol / L. Mortality developed in 14.2% of the patients during the first 28 days. The 28-day mortality was significantly higher in patients with a positive Polymerase Chain Reaction (PCR) (23.8%) than that of negative PCR (8.2%) (p < 0.001). The lactate level was found to be significantly different in both PCR positive and negative groups in which mortality developed within 28 days (p < 0.001;p < 0.001). If the cut-off value of lactate in terms of mortality was 2.45, the sensitivity and specificity were determined as 80.0% and 81.2%, respectively. Conclusion: In patients with COVID-19 infection, the blood lactate level examined at the first admission to ED can be used as a practical screening test to predict mortality.

Detection of new coronavirus disease from chest x-ray images using pre-trained convolutional neural networks

The COVID-19 is a virus that spreads quickly with a high mortality rate. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits and diagnosis using medical imaging methods have been investigated. Among the medical imaging tools, diagnosis with the help of Computed Tomography and X-ray images is very important. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In conclusion, the pre- trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. We believe that this study will help to defeat the epidemic.