Usefulness of laboratory parameters and chest CT in the early diagnosis of COVID-19

In the present study, the importance of laboratory parameters and CT findings in the early diagnosis of COVID-19 was investigated. To this end, 245 patients admitted between April 1st, and May 30th, 2020 with suspected COVID-19 were enrolled. The patients were divided into three groups according to chest CT findings and RT-PCR results. The non-COVID-19 group consisted of 71 patients with negative RT-PCR results and no chest CT findings. Ninety-five patients with positive RT-PCR results and negativechest CT findings were included in the COVID-19 group; 79 patients with positive RT-PCR results and chest CT findings consistent with COVID-19 manifestations were included in COVID-19 pneumonia group. Chest CT findings were positive in 45% of all COVID-19 patients. Patients with positive chest CT findings had mild (n=30), moderate (n=21) andor severe (n=28) lung involvement. In the COVID-19 group, CRP levels and the percentage of monocytes increased significantly. As disease progressed from mild to severe, CRP, LDH and ferritin levels gradually increased. In the ROC analysis, the area under the curve corresponding to the percentage value of monocytes (AUC=0.887) had a very good accuracy in predicting COVID-19 cases. The multinomial logistic regression analysis showed that CRP, LYM and % MONO were independent factors for COVID-19. Furthermore, the chest CT evaluation is a relevant tool in patients with clinical suspicion of COVID-19 pneumonia and negative RT-PCR results. In addition to decreased lymphocyte count, the increased percentage of monocytes may also guide the diagnosis

Forecasting the consumptions of coagulation tests using a deep learning model

Background: Laboratory professionals aim to provide a reliable laboratory service using public resources efficiently while planning a test's procurement. This intuitive approach is ineffective, as seen in the COVID-19 pandemic, where the dramatic changes in admissions (e.g. decreased patient admissions) and the purpose of testing (e.g. D-dimer) were experienced. A model based on objective data was developed that predicts the future test consumption of coagulation tests whose consumptions were highly variable during the pandemic. Methods: Between December 2018 and July 2021, monthly consumptions of coagulation tests (PTT, aPTT, D-dimer, fibrinogen), total-, inpatient-, outpatient-, emergency-, non-emergency -admission numbers were collected. The relationship between input and output is modeled with an external input nonlinear autoregressive artificial neural network (NARX) using the MATLAB program. Monthly test consumption between January and July 2021 was used to test the power of the forecasting model. Results: According to the co-integration analysis, the total number as well as the number of emergency and nonurgent examinations and the number of working days per month are included in the model. When the consumption of aPTT and fibrinogen was estimated, it was possible to predict the consumption of other tests. Fifty months of data were used to predict consumption over the next six months, and prediction based on NARX was the more robust approach for both tests. Conclusion: The deep learning model gives better results than the intuitive approach in forecasting, even in the pandemic era, and it shows that more effective and efficient planning will be possible if ANN-supported decision mechanisms are used in forecasting

Diagnostic utility of C-reactive protein to albumin ratio as an early warning sign in hospitalized severe COVID-19 patients

C-reactive protein-to-albumin ratio (CAR) has been used as an indicator of prognosis in various diseases. Here, we intended to assess the CAR's diagnostic power in early differentiation of hospitalized severe COVID-19 cases. In this retrospectively designed study, we evaluated 197 patients in total. They were divided into two groups based on their severity of COVID-19 as non-severe (n = 113) and severe (n = 84). The comparison of groups' demographic data, comorbidities, clinical symptoms, and laboratory test results were done. Laboratory data of the patients within the first 24 h after admission to the hospital were evaluated. The calculation of receiver operating characteristic (ROC) curve was used to determine the diagnostic power of CAR in differentiating severity of COVID-19. Independent risk factors predictive of COVID-19 severity were determined by using logistic regression analysis. Although lymphocyte count levels were lower, severe COVID-19 patients had higher mean age, higher levels of neutrophil count, CRP, aspartate aminotransferase (AST), ferritin, and prothrombin time (P < 0.05). Compared with non-severe patients (median, 0.23 [IQR = 0.07-1.56]), patients with severe COVID-19 had higher CAR levels (median, 1.66 [IQR = 0.50-3.35]; P < 0.001). Age (OR = 1.046, P = 0.003), CAR (OR =1.264, P = 0.037), and AST (OR = 1.029, P = 0.037) were independent risk factors for severe COVID19 based on the multivariate logistic regression analysis. ROC curve analysis assigned 0.9 as the cut-off value for CAR for differentiation of severe COVID-19 (area under the curve = 0.718, 69.1% sensitivity, 70.8% specificity, P < 0.001). CAR is a useful marker in early differentiation of severity in patients hospitalized due to COVID-19 that have longer hospital stay and higher mortality