Evaluation of the olfactory bulb volume and morphology in patients with coronavirus disease 2019: can differences create predisposition to anosmia?

OBJECTIVE: This study aimed to investigate whether the volume and morphology of the olfactory bulb are effective in the occurrence of anosmia in patients after COVID-19 infection. METHODS: The olfactory bulbus volume was calculated by examining the brain magnetic resonance imaging of cases with positive (+) COVID-19 polymerase chain reaction test with and without anosmia. Evaluated magnetic resonance imaging images were the scans of patients before they were infected with COVID-19. The olfactory bulbus and olfactory nerve morphology of these patients were examined. The brain magnetic resonance imaging of 59 patients with anosmia and 64 controls without anosmia was evaluated. The olfactory bulb volumes of both groups were calculated. The olfactory bulb morphology and olfactory nerve types were examined and compared between the two groups. RESULTS: The left and right olfactory bulb volumes were calculated for the anosmia group and control group as 47.8±15/49.3±14.3 and 50.5±9.9/50.9±9.6, respectively. There was no statistically significant difference between the two groups. When the olfactory bulb morphology was compared between the two groups, it was observed that types D and R were dominant in the anosmia group (p<0.05). Concerning olfactory nerve morphology, type N was significantly more common in the control group (p<0.05). CONCLUSIONS: According to our results, the olfactory bulb volume does not affect the development of anosmia after COVID-19. However, it is striking that the bulb morphology significantly differs between the patients with and without anosmia. It is clear that the evaluation of COVID-19-associated smell disorders requires studies with a larger number of patients and a clinicoradiological approach.

Clinical characteristics and hematological parameters associated with disease severity in COVID-19 positive pregnant women undergoing cesarean section: A single-center experience.

AIM: The study aimed to describe clinical characteristics and outcomes of pregnant women with COVID-19 undergoing cesarean section, and evaluated the association of blood values at admission with severe COVID-19 disease in this group of patients. METHOD: We retrospectively analyzed the clinical data of 110 patients infected with COVID-19 who underwent cesarean section at Adana City Education and Research Hospital in Turkey. The COVID-19 severity of the patients was classified as either severe or nonsevere disease according to World Health Organization of COVID-19 clinical management guidance. We compared blood values, clinical characteristics, and outcomes between severe and nonsevere patients. Receiver operating characteristics (ROC) curves analyses and area under the ROC curve (AUC) value was calculated to evaluate the predictive value of blood parameters on the COVID-19 severity. RESULTS: Of the 110 women, 12 were severe cases. Severe patients had higher ferritin, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), alanine transaminase (ALT), aspartate transaminase (AST), and procalcitonin levels on admission (p < 0.05). The ROC analysis demonstrated AUC of NLR, LDH, AST, ALT, ferritin, and procalcitonin was 0.757, 0.856, 0.840, 0.771, 0.821, and 0.698, respectively. The LDH had a maximum specificity (90.8%), with the cutoff value of 365. The O-blood group was more likely to have severe illness than the non-O-blood group (relative risk: 3.6; 95% confidence interval; 1.2-10.4). CONCLUSION: This study shows that LDH values at admission are an early and powerful predictor of severe infection for pregnant women with COVID-19 who will undergo a cesarean section.

Diagnostic performance and interobserver agreement of CO-RADS: evaluation of classification in radiology practice.

PURPOSE: We aimed to evaluate the use of the COVID-19 reporting and data system (CO-RADS) among radiologists and the diagnostic performance of this system. METHODS: Four radiologists retrospectively evaluated the chest CT examinations of 178 patients. The study included 143 patients with positive reverse transcriptase-polymerase chain reaction (RT-PCR) test results and 35 patients whose RT-PCR tests were negative but whose clinical and/or radiological findings were consistent with COVID-19. Fleiss' kappa (κ) values were calculated, and individual observers' scores were compared. To investigate diagnostic efficiency, receiver operating characteristic (ROC) curves were calculated for each interpreter. RESULTS: The interpreters were in full agreement on 574 of 712 (80.6%) evaluations. The common Fleiss' κ value of all the radiologists combined was 0.712 (95% confidence interval [CI] 0.692-0.769). A reliable prediction on the basis of RT-PCR and clinical findings indicated the mean area under the curve (AUC) of Fleiss' κ value as 0.89 (95% CI 0.708-0.990). General interpreter agreement was found to range from moderate to good. CONCLUSION: The interpreter agreement for CO-RADS categories 1 and 5 was reasonably good. We conclude that this scoring system will make a valuable contribution to efforts in COVID-19 diagnosis. CO-RADS can also be of significant value for the diagnosis and treatment of the disease in cases with false-negative PCR results.

Different chest CT scoring systems in patients with COVID-19: could baseline CT be a helpful tool in predicting survival in patients with matched ages and co-morbid conditions?

BACKGROUND: Computed tomography (CT) gives an idea about the prognosis in patients with COVID-19 lung infiltration. PURPOSE: To evaluate the success rates of various scoring methods utilized in order to predict survival periods, on the basis of the imaging findings of COVID-19. Another purpose, on the other hand, was to evaluate the agreements among the evaluating radiologists. MATERIAL AND METHODS: A total of 100 cases of known COVID-19 pneumonia, of which 50 were deceased and 50 were living, were included in the study. Pre-existing scoring systems, which were the Total Severity Score (TSS), Chest Computed Tomography Severity Score (CT-SS), and Total CT Score, were utilized, together with the Early Decision Severity Score (ED-SS), which was developed by our team, to evaluate the initial lung CT scans of the patients obtained at their initial admission to the hospital. The scans were evaluated retrospectively by two radiologists. Area under the curve (AUC) values were acquired for each scoring system, according to their performances in predicting survival times. RESULTS: The mean age of the patients was 61 ± 14.85 years (age range = 18-87 years). There was no difference in co-morbidities between the living and deceased patients. The survival predicted AUC values of ED-SS, CT-SS, TSS, and Total CT Score systems were 0.876, 0.823, 0.753, and 0.744, respectively. CONCLUSION: Algorithms based on lung infiltration patterns of COVID-19 may be utilized for both survival prediction and therapy planning.