Antineutrophil Cytoplasmic Antibody-Associated Vasculitis and COVID-19: The Clinical Course and Prognosis of 15 Patients From a Tertiary Care Center.

OBJECTIVE: The aim of this study was to evaluate incidence rates, prognoses, and disease-related factors associated with poor outcomes in patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV) who had coronavirus disease (COVID-19). METHODS: Patients with AAV were questioned for a history of COVID-19 in the outpatient setting. Cumulative clinical findings and treatment history were obtained from the patients' medical records. The clinical, laboratory, and imaging findings of inpatients with COVID-19 were recorded. The data of patients who developed symptomatic COVID-19 and/or died of the disease were used for comparison. RESULTS: Eighty-nine patients (47.2% female; mean age, 56 ± 12.5 years) were included. The diagnosis was granulomatosis with polyangiitis in 56 patients (62.9%) and microscopic polyangiitis in 33 (37.1%). Sixty-one (68.2%) and 21 patients (23.6%) had renal and peripheral nerve involvement, respectively. Ten patients had a history of diffuse alveolar hemorrhage. Fifteen patients (16.9%) had COVID-19, including 9 (60%) with severe pneumonia. Twelve patients (85.7%) were hospitalized, 6 (42.9%) were admitted to the intensive care unit, and 5 (35.7%) died. All deceased patients had hypogammaglobulinemia (IgG levels <700 mg/dL) during hospital admission. Symptomatic COVID-19 was associated with higher disease activity, glucocorticoid and rituximab treatments, and glomerular filtration rate <30 mL/min. A history of peripheral nerve involvement, higher organ damage scores, and hypogammaglobulinemia was associated with mortality. CONCLUSIONS: The prognosis was poor in our patients with AAV who had COVID-19, especially those with severe multisystem involvement. Hypogammaglobulinemia was associated with mortality. Serum IgG level monitoring in patients with AAV would be beneficial during the COVID-19 pandemic.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study.

Background: Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims: We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods: Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results: RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (ß = 0.097, p < 0.05) and pleural effusion (ß = 10.631, p < 0.05) on COVID-19 pneumonia patients. Conclusion: The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

Can invasive diagnostic methods be reduced by magnetic resonance imaging in the diagnosis of diaphragmatic injuries in left thoracoabdominal penetrating injuries?

Aim: In this study, we aimed to investigate the effect of magnetic resonance imaging (MRI) in detecting diaphragmatic injury by comparing preoperative computed tomography (CT) and MRI imaging results with diagnostic laparoscopy/thoracoscopy results in patients with left thoracoabdominal penetrating injury. We investigated whether MRI reduces the rate of unnecessary surgery by examining its sensitivity and specificity. Materials and Methods: Patients with left thoracoabdominal penetrating injuries who applied to the Emergency Surgery Unit of Istanbul University Istanbul Faculty of Medicine between November 2017 and December 2020 were evaluated. Patients who underwent emergency surgery, who could not undergo MRI or CT for any reason or who could not be operated on were excluded from the study. Preoperative MRI and CT images of patients who underwent diagnostic laparoscopy/thoracoscopy due to left thoracoabdominal injury in our clinic were evaluated retrospectively by a radiologist who did not know the surgical results. MRI results of the cases were compared with surgical findings and CT images. Results: A total of 43 (41 males, mean age: 31, range: 15-57) patients were included in the study. The most common physical examination finding was lateral injury. The diaphragmatic injury was detected in 13 (30%) cases during surgical interventions. Laparoscopic repair was performed in 11 (84%) cases and thoracoscopic repair was performed in 2 (15%) cases with diaphragmatic injuries. MRI images of 14 (32%) cases were found to be compatible with diaphragmatic injury, in 1 of them no injury was observed during surgical intervention. According to these data, the sensitivity of MRI was calculated as 100%, specificity 94%, positive predictive value 86%, and negative predictive value 100%. The mean hospital stay was 6 days (1-30) in all cases. Conclusion: In our study, MRI was found to have high specificity and sensitivity in detecting diaphragmatic injuries. The number of negative laparoscopy/thoracoscopy can be reduced by performing surgical intervention only in cases with positive or suspected diaphragmatic injury on MRI. Results should be supported by conducting new studies with larger case series with normal MRI findings and long follow-ups.

Covid-19: Correlation of Early Chest Computed Tomography Findings With the Course of Disease.

OBJECTIVE: To investigate the role of chest computed tomography (CT) examinations acquired early after initial onset of symptoms in predicting disease course in coronavirus disease 2019. METHODS: Two hundred sixty-two patients were categorized according to intensive care unit (ICU) admission, survival, length of hospital stay, and reverse transcriptase-polymerase chain reaction positivity. Mean time interval between the onset of symptoms and CT scan was 5.2 ± 2.3 days. Groups were compared using Student t test, Mann-Whitney U, and Fisher exact tests. RESULTS: In the ICU (+) and died groups, crazy paving (64% and 57.1%), bronchus distortion (68% and 66.7%), bronchiectasis-bronchiolectasis (80% and 76.2%), air trapping (52% and 52.4%) and mediastinal-hilar lymph node enlargement (52% and 52.4%) were significantly more encountered (P < 0,05). These findings were correlated with longer hospital stays (P < 0.05). There were no differences between reverse transcriptase-polymerase chain reaction-positive and -negative patients except bronchiectasis-bronchiolectasis. CONCLUSION: Computed tomography examinations performed early after the onset of symptoms may help in predicting disease course and planning of resources, such as ICU beds.

Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models.

PURPOSE: This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODS: Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTS: Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSION: Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

Criteria for Hyperinflammation Developing in COVID-19: Analysis of 2 Cohorts From Different Periods of the Pandemic.

OBJECTIVE: Hyperinflammation (HI) that develops in week 2 of COVID-19 contributes to a worse outcome. Because week 2 laboratory findings can be relatively mild, the available criteria for classification of hemophagocytic lymphohistiocytosis or macrophage activation syndrome are not helpful. METHODS: Our study included a discovery cohort of patients from Turkey with symptomatic COVID-19 who were followed up while hospitalized during the initial wave and a replication cohort of hospitalized patients from a later period, all of whom required oxygen support and received glucocorticoids. Diagnosis of HI was made by an expert panel; most patients with COVID-19-associated HI (HIC) received tocilizumab or anakinra. Clinical and laboratory data from start day of treatment with tocilizumab or anakinra in HIC patients were compared with the data from day 5-6 in patients without HIC. Values maximizing the sensitivity and specificity of each parameter were calculated to determine criteria items. RESULTS: The discovery cohort included 685 patients, and the replication cohort included 156 patients, with 150 and 61 patients receiving treatment for HI, respectively. Mortality rate in HI patients in the discovery cohort (23.3%) was higher than the rate in patients without HI (3.7%) and the rate in patients in the overall replication cohort (10.3%). The 12-item criteria that we developed for HIC showed that a score of 35 provided 85.3% sensitivity and 81.7% specificity for identification of HIC. In the replication cohort, the same criteria resulted in 90.0% sensitivity for HIC; however, lower specificity values were observed because of the inclusion of milder cases of HIC responding only to glucocorticoids. CONCLUSION: The use of the 12-item criteria for HIC can better define patients with HIC with reasonable sensitivity and specificity and enables an earlier treatment start.