Underwent Chest Chest Computed Tomography Research Articles

Prognostic value of chest computed tomography in adult inpatients with coronavirus disease 2019 pneumonia in Goma, in the Democratic Republic of the Congo

Objectives: Chest computed tomography (CT) plays a crucial role in evaluating patients with coronavirus disease 2019 (COVID-19) pneumonia. This study aimed to assess the association between initial chest CT findings and mortality in adult inpatients with COVID-19 in the city of Goma, North Kivu province, the Democratic Republic of the Congo. Materials and Methods: This was a multicenter retrospective study of patients hospitalized in Goma with COVID-19 pneumonia who underwent chest CT from January 1, 2021, to December 31, 2022. For each patient included, two experienced radiologists independently reviewed the initial chest CT. A multivariate logistic regression was performed to identify chest CT findings associated with mortality at the 5% significance level. Results: Of 76 patients included in the study, 26 (34.2%) died. The degree of lung parenchymal involvement >25% (adjusted odds ratio [aOR] = 16.27 [3.30–80.16]) and the presence of consolidations (aOR = 3.33 [1.00–11.47]) were predictive of mortality with an area under the receiver operating characteristic curve of 0.8392. Sensitivity was 73.08%, specificity was 82%, positive predictive value was 67.86%, and negative predictive value was 85.42%. Conclusion: Lung involvement >25% and consolidations on chest CT appear to predict death in adult inpatients with COVID-19 pneumonia.

Incidental detection of ground glass nodules and primary lung cancer in patients with breast cancer: prevalence and long-term follow-up on chest computed tomography.

Patients with breast cancer have a higher risk of developing lung cancer than the general population. The study aimed to evaluate the prevalence of ground glass nodule (GGN) and risk factors for GGN growth in patients with breast cancer and to evaluate the prevalence and pathologic features of lung cancer. We retrospectively reviewed the clinical data and chest computed tomography (CT) of 1,384 patients diagnosed with breast cancer who underwent chest CT between January 2008 and December 2022. We evaluated the prevalence of GGNs and their size changes on follow-up chest CT with volume doubling time (VDT) and identified independent risk factors associated with the growth of GGN using multivariable logistic regression analyses. Furthermore, the prevalence and pathologic features of lung cancer were also evaluated. We detected persistent GGNs in 69 of 1,384 (5.0%) patients. The initial diameter of GGNs was 6.3±3.6 mm on average, with primarily (85.5%) pure GGNs. Among them, 27 (39.1%) exhibited interval growth with a median VDT of 1,006.0 days (interquartile range, 622.0-1,528.0 days) during the median 959.0 days (interquartile range, 612.0-1,645.0 days) follow-up period. Older age (P=0.026), part-solid nodules (P=0.006), and total number of GGNs (≥2) (P=0.007) were significant factors for GGN growth. Lung cancer was confirmed in 13 of 1,384 patients (0.9%), all with adenocarcinoma, including one case of minimally invasive adenocarcinoma. The cancers demonstrated a high rate of epidermal growth factor receptor (EGFR) mutation (69.2%). Persistent GGNs in breast cancer patients with high-risk factors should be adequately monitored for early detection and treatment of lung cancer.

Can chest computed tomography be useful to emergency residents in the diagnosis of COVID-19 pneumonia?

<b>Aim:</b> During the novel coronavirus disease (COVID-19) pandemic, the workload of emergency department residents (ER) has increased since emergency departments (EDs) are among the first health units to which patients apply. The aim of this study was to investigate the diagnostic value of chest computed tomography (CT) interpretations of emergency department residents in the diagnosis of COVID-19 pneumonia.<br /> <b>Material and methods: </b>Patients who admitted to the tertiary ED between 30.03.2020 and 30.12.2020 with suspected COVID-19 pneumonia and underwent chest CT imaging were retrospectively analyzed. Chest CT images previously reported by the radiologist were reinterpreted by the ER and the diagnostic performance of the ER for COVID-19 pneumonia was calculated.<br /> <b>Results: </b>The study included 2045 patients with suspected COVID-19 pneumonia. Chest CT images of all patients were interpreted and compared by a radiologist and the ER In this study, interpretation by a radiologist was considered as the gold standard. In all patients, the diagnostic performance capacity of ER for COVID-19 pneumonia included a low sensitivity of 66%, a high specificity of 92%, a positive predictive value (PPV) of 83% and a negative predictive value (NPV) of 82%. When patients with positive COVID-19 RT-PCR test results were analyzed, the highest sensitivity, specificity, PPV and NPV of ER were 96%, 82%, 95%, 95% and 86%, respectively.<br /> <b>Conclusion: </b>ER detected COVID-19 pneumonia in COVID-19 (+) patients with high diagnostic value and excluded inconsistent findings. Chest CT interpretation in the ER has a high diagnostic value. It is as important as any other radiologist in outbreak management in the ED.

Comparison of Chest Computed Tomography (CT) Imaging Patterns and Severity among COVID-19 Patients during the First and Fourth Waves in Addis Ababa, Ethiopia

Background. Studies done globally had shown that chest imaging patterns of Corona virus disease 2019 (COVID-19) infection varied depending on the strains of the virus and the waves of the pandemic. There is no published literature done in Ethiopia to examine whether there is any difference in chest computed tomography (CT) findings of COVID-19 patients during the first and fourth waves. Thus, this study tries to fill the gap of knowledge in that regard. Objective. To describe and compare chest CT scan imaging pattern and assess the predictors of chest CT severity of the first and fourth wave of COVID-19 infection. Methods. An institution-based cross-sectional study was conducted on 200 polymerase chain reaction test confirmed COVID-19 patients who underwent chest CT scan imaging in two diagnostic centers in Addis Ababa city. Pioneer and Wudassie diagnostic centers were selected due to the high case load and availability of well-experienced cardiothoracic radiologists. Data were collected from July 1 to August 3, 2022, using a structured Google form sheet questionnaire. Binary logistic regression was performed, and statistical significance was assessed at a level of significance α = 0.05. Results. Comparatively higher proportion of patients from the first wave had positive chest CT finding than fourth wave (99% vs. 69%). Bilateral lung involvement and lower lobe predilection were seen for both waves of COVID-19. Ground glass opacity and consolidation were the most common CT features for both waves. Delayed chest CT features such as traction bronchiectasis were primarily seen among first-wave patients. Mean global CT severity score was higher for the first-wave patients (13.18 vs. 8.31), and the mean difference is statistically significant ( p < 0.001). Duration of symptoms was a statistically significant predictor of CT severity during the first wave of COVID-19, and patients that presented later than 14 days had 4.12 times higher odds of being in the severe CT score category than those that presented less than 7 days (AOR = 4.12, p = 0.011). There was no statistically significant predictor of CT severity for the fourth wave in this study. Conclusion. Chest CT positivity was comparatively higher for first wave patients. Common features included bilateral involvement, lower lobe involvement, ground glass opacity, and consolidation. Mean chest CT severity was comparatively higher for the first wave than the fourth wave, and the duration of symptoms was a statistically significant predictor of the CT severity for first wave.

Survey on chest CT findings in COVID-19 patients in Okinawa, Japan: differences between the delta and omicron variants

To investigate the frequency of pneumonia and chest computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) during the fifth Delta variant-predominant and sixth Omicron variant-predominant waves of the COVID-19 pandemic in Okinawa, Japan. A survey on chest CT examinations for patients with COVID-19 was conducted byhospitals with board-certified radiologists who provided treatment for COVID-19 pneumonia in Okinawa Prefecture. Data from 11 facilities were investigated. Indications for chest CT; number of COVID-19 patients undergoing chest CT; number of patients with late-onset pneumonia, tracheal intubation, and number of deaths; and COVID-19 Reporting and Data System classifications of initial chest CT scans were compared by the chi-squared test between the two pandemic waves (Delta vs. Omicron variants). A total of 1944 CT scans were performed during the fifth wave, and 1178 were performed during the sixth wave. CT implementation rates, which were the number of patients with COVID-19 undergoing CT examinations divided by the total number of COVID-19 cases in Okinawa Prefecture during the waves, were 7.1% for the fifth wave and 2.1% for the sixth wave. The rates of tracheal intubation and mortality were higher in the fifth wave. Differences between the distributions of the CO-RADS classifications were statistically significant for the fifth and sixth waves (p < 0.0001). In the fifth wave, CO-RADS 5 (typical of COVID-19) was most common (65%); in the sixth wave, CO-RADS 1 (no findings of pneumonia) was most common (50%). The finding of “typical for other infection but not COVID-19” was more frequent in the sixth than in the fifth wave (13.6% vs. 1.9%, respectively). The frequencies of pneumonia and typical CT findings were higher in the fifth Delta variant-predominant wave, and nontypical CT findings were more frequent in the sixth Omicron variant-predominant wave of the COVID-19 pandemic in Okinawa, Japan.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia

Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. A total of 2,907 chest CT scans were included in the training (n= 1,934), validation (n= 408), and performance (n= 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4%and 37.1%in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93%and 86%, respectively, and in the multicenter ILD clinical cohort with 81%and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

FRI465 Thyroid Incidentalomas In Hospitalized Patients With COVID-19: A Single-Center Retrospective Analysis

Abstract Disclosure: M. Jantarapootirat: None. C. Sriphrapradang: None. S. Traiwanatham: None. W. Boonsomsuk: None. S. Sungkanuparph: None. P. Hirunpat: None. Context: During the COVID-19 pandemic, both people with underlying diseases and previously healthy people had been infected with SARS-CoV-2. In our institute, most hospitalized patients underwent chest computed tomography (CT) to evaluate pulmonary involvement and complication of COVID-19. There is currently limited data regarding thyroid incidentalomas in healthy people. Objective: We aim to investigate the prevalence and predictors of thyroid incidentalomas among hospitalized patients with COVID-19. Methods: A single-center retrospective study included hospitalized patients aged ≥ 15 years with COVID-19 who underwent chest CT during April 2020 and October 2021. Thyroid incidentalomas were reviewed and identified by an experienced radiologist. Logistic regression analysis was used to determine predictors for thyroid incidentalomas. Results: Of 1,326 patients (mean age 49.4 years and 55.3% female) that were included, the prevalence of thyroid incidentalomas was 20.2%. Patients with thyroid incidentalomas were older (59.6 years vs 46.8 years, p &amp;lt;0.001) and had a higher proportion of females (63.4% vs 53.2%, p=0.003) than those without incidentalomas. On multivariate analysis, only female gender (OR 1.56; 95%CI, 1.17-2.07) and older age (OR 1.04; 95%CI, 1.03-1.05) were significantly associated with thyroid incidentalomas. Conclusion: In COVID-19 patients, the prevalence of thyroid incidentalomas identified on chest CT was higher (20.2%) compared with previous studies in the general population (&amp;lt;1% to 16.8%). The female gender and older age were independent factors associated with thyroid incidentalomas. The higher prevalence of thyroid incidentalomas among COVID-19 patients needs further studies to explore the association between thyroid nodules and SARS-CoV-2 infection. Presentation: Friday, June 16, 2023

Significance and prognostication of mediastinal lymph node enlargement on chest computed tomography among adult Indigenous Australians.

There is a lack of data on chest computed tomography (CT) findings on mediastinal lymph node enlargement (MLE), including normal size threshold of less than 10 or 15 mm for MLE among Indigenous Australians. In this study, we assessed the significance and the applicability of the current guidelines for the threshold for abnormal MLE among adult Indigenous Australians. Patients who underwent chest CT between 2012 and 2020 among those referred to undergo lung function test (spirometry) were assessed for the presence of MLE which were classified as Group A (no measurable nodes), Group B (<10 mm), Group C (≥10 to 14.99 mm) and Group D (≥15 mm). Of the total 67 patients identified to have MLE, 49 patients had at least two CT scans available for assessment over a median follow-up period of 101.3 weeks (IQR: 62.4, 235.6) and were included in the analysis. Evidence of chronic lung disease was common, with a significant proportion demonstrating either COPD or bronchiectasis and a high proportion with smoking history (93%). During the first CT scan, 34/49 (69%) had >10 mm nodes, of which 12/34 (35%) reduced in size, 22/34 (65%) remained stable, and 3/34 (9%) had malignancy on follow-up. Despite most patients demonstrating the presence of significant MLE with varying size and in most >10 mm, the majority remain stable or benign in nature and only a minor proportion showed evidence of lung malignancy. Further prospective studies are needed in the characterisation of MLE among Indigenous patients.

Coronary artery calcium score predicts outcome in patients with COVID-19

Abstract Funding Acknowledgements Type of funding sources: None. Aim Coronary artery calcium (CAC) score is a proven prognostic marker of cardiovascular risk. The aim of this study was to evaluate the prognostic value of CAC score on standard chest computed tomography (CT) in patients admitted with COVID-19. Methods We enrolled 144 consecutive patients (mean age 67± 13, 60.7% male) hospitalized with COVID-19, who on admission underwent chest CT to detect and assess pulmonary involvement. CAC score was calculated by two investigators blinded to patients clinical data. Patients were followed-up for 12 months after their admission. Results 106 patients had image quality sufficient for analysis. The median CAC score was 249 Agatston units (interquartile range 658). 33 deaths were recorded in the study group during the follow-up period. The median values of CAC of patients who died during the study period and of the survivors were 592 (interquartile range 1492) and 142 (interquartile range 400), respectively. The difference in CAC between these two subgroups was statistically significant (p = 0,0001). The area under the ROC curve for CAC score for predicting all-cause mortality was 0.738 (95% CI 0,644 to 0,819). The criterion with the highest prognostic accuracy was CAC score &amp;gt;143 Agatston units (positive and negative predictive values were 44,4% and 88,4%, respectively). The presence of this criterion was associated with 3,82 times higher relative risk of mortality (95%CI 1.60 – 9,11; p = 0.0025). Conclusion CAC score measurement based on standard chest CT performed on admission in patients with COVID-19 to detect pulmonary involvement is a prognostic marker of increased mortality during the 12-month follow-up.

Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS).

In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.

Thyroid Incidentalomas in Hospitalized Patients With COVID-19: A Single-Center Retrospective Analysis.

During the COVID-19 pandemic, both people with underlying diseases and previously healthy people were infected with SARS-CoV-2. In our institute, most hospitalized patients underwent chest computed tomography (CT) to evaluate pulmonary involvement and complication of COVID-19. There are currently limited data regarding thyroid CT incidentalomas in healthy people. We aimed to investigate the prevalence and predictors of thyroid incidentalomas among hospitalized patients with COVID-19. A single-center retrospective study included hospitalized patients aged ≥15 years with COVID-19 who underwent chest CT during April 2020 and October 2021. Thyroid incidentalomas were reviewed and identified by an experienced radiologist. Logistic regression analysis was used to determine predictors for thyroid incidentalomas. In the 1326 patients (mean age 49.4 years and 55.3% female) that were included, the prevalence of thyroid incidentalomas was 20.2%. Patients with thyroid incidentalomas were older (59.6 years vs 46.8 years, P < .001) and more often female than those without incidentalomas (63.4% vs 53.2%, P = .003). On multivariate analysis, only female sex (OR 1.56; 95% CI 1.17-2.07) and older age (OR 1.04; 95% CI 1.03-1.05) were significantly associated with thyroid incidentalomas. In COVID-19 patients, the prevalence of thyroid incidentalomas identified on chest CT was higher (20.2%) than in previous studies in the general population (<1% to 16.8%). Female sex and older age were independent factors associated with thyroid incidentalomas.

The effect of CFTR modulators on structural lung disease in cystic fibrosis.

Background: Newly developed quantitative chest computed tomography (CT) outcomes designed specifically to assess structural abnormalities related to cystic fibrosis (CF) lung disease are now available. CFTR modulators potentially can reduce some structural lung abnormalities. We aimed to investigate the effect of CFTR modulators on structural lung disease progression using different quantitative CT analysis methods specific for people with CF (PwCF). Methods: PwCF with a gating mutation (Ivacaftor) or two Phe508del alleles (lumacaftor-ivacaftor) provided clinical data and underwent chest CT scans. Chest CTs were performed before and after initiation of CFTR modulator treatment. Structural lung abnormalities on CT were assessed using the Perth Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF), airway-artery dimensions (AA), and CF-CT methods. Lung disease progression (0-3years) in exposed and matched unexposed subjects was compared using analysis of covariance. To investigate the effect of treatment in early lung disease, subgroup analyses were performed on data of children and adolescents aged <18years. Results: We included 16 modulator exposed PwCF and 25 unexposed PwCF. Median (range) age at the baseline visit was 12.55 (4.25-36.49) years and 8.34 (3.47-38.29) years, respectively. The change in PRAGMA-CF %Airway disease (-2.88 (-4.46, -1.30), p = 0.001) and %Bronchiectasis extent (-2.07 (-3.13, -1.02), p < 0.001) improved in exposed PwCF compared to unexposed. Subgroup analysis of paediatric data showed that only PRAGMA-CF %Bronchiectasis (-0.88 (-1.70, -0.07), p = 0.035) improved in exposed PwCF compared to unexposed. Conclusion: In this preliminary real-life retrospective study CFTR modulators improve several quantitative CT outcomes. A follow-up study with a large cohort and standardization of CT scanning is needed to confirm our findings.

Inadvertent Thyroid Radiation During Computed Tomography of the Chest: A Retrospective Study.

The thyroid, along with the breast, lung, and bone marrow, is among the most radiosensitive organs. This study aims to assess the rate of unnecessary radiation exposure to the thyroid gland in patients who had chest Computed Tomography (CT) at a large teaching hospital. Hospital-based retrospective cross-sectional study on 1,306 patients who underwent chest CT from July 2018 to January 2019. Thyroid gland inclusion along with the CT dose of the studies was evaluated. Data was collected by evaluating chest CT scans from Picture Archive and Communication System (MedWeb). Out of 1306 patients, who had Chest CT scans intravenous iodinated contrast media was used in 95.4% of the CT scans. The thyroid was included in 99.8% of the scans, out of which 76.9% included the whole thyroid gland. Among the patients who had previous CT scans, 75.3% had one previous scan and 24.7% had two previous scans. DLP (Dose Length Product) in mGycm was lower in females (360.33±32) compared to males (426.45±378.4). The lowest DLP value was observed in the pediatric patients in the age range of 1-5 years which was 146.83, while the highest was observed among those above 18 years of age with mean DLP of 418.31. The majority of chest CT scans unnecessarily include the whole thyroid gland, which is one of the most sensitive organs for radiation-induced effects. Authors recommend optimized technique for chest scans to avoid future impacts.

Epipericardial fat necrosis: a case report of an underdiagnosed disease.

Epipericardial fat necrosis: a case report of an underdiagnosed disease.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis.

The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans. This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively. The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

Fibrosing mediastinitis in a child with Mendelian susceptibility to mycobacterial disease possibly due to Bacillus Calmette-Guérin

BackgroundMendelian susceptibility to mycobacterial disease (MSMD) is an uncommon disorder with increased susceptibility to less virulent mycobacteria including bacillus Calmette-Guérin (BCG). Fibrosing mediastinitis (FM) is also a rare condition defined by excessive fibrotic reactions in the mediastinum. So far, some infectious organisms and autoimmune diseases have been introduced as possible etiologies of FM. However, no study has ever discussed the possible association of BCG infection and FM.Case presentationIn this study, we report a 3-year-old female presenting with persistent fever, weakness, and bloody diarrhea in addition to mediastinal lymphadenopathy, hepatosplenomegaly, and pleural and pericardial effusion. Further examinations established a diagnosis of MSMD based on her clinical condition, immunologic data, positive tests for mycobacterial species, positive family history, and genetic study (IL12RB1 gene, c.G1193C, p.W398S). A year and a half later, she was referred with submandibular lymphadenitis and underwent immunologic work-up which revealed high inflammatory indices, a slight reduction in numbers of CD3 + and CD4 + cells as well as elevated CD16/56 + cell count and hyperimmunoglobulinemia. Purified protein derivative (PPD), QuantiFERON, and gastric washing test were all negative. Her chest computed tomography (CT) scan revealed suspicious para-aortic soft tissue and her echocardiography was indicative of strictures in superior vena cava and pulmonary veins. She further underwent chest CT angiography which confirmed FM development. Meanwhile, she has been treated with anti-mycobacterial agents and subcutaneous IFN-γ.ConclusionIn summary, we described a novel case of MSMD in a child presenting with granulomatous FM possibly following BCG infection. This is the first report introducing aberrant BCG infection as the underlying cause of FM. This result could assist physicians in identifying early-onset FM in suspicious cases with MSMD. However, more studies are required to support this matter.

Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study.

Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061–0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.

Epipericardial Fat Necrosis: A Retrospective Analysis in Japan.

Objective Epipericardial fat necrosis (EFN) has been considered to be a rare cause of acute chest pain, and especially important for emergency physicians. Chest computed tomography (CT) is often used for the diagnosis of EFN after excluding life-threatening states, such as acute coronary syndrome and pulmonary embolism. While the proportion of EFN patients who underwent chest CT in emergency departments is being clarified, little is still known about other departments in Japan. To investigate the proportion of EFN patients who underwent chest CT for acute chest pain in various departments. Methods Chest CT performed from January 2015 to July 2020 in Asahikawa Medical University Hospital in Japan was retrospectively analyzed in this study. All images were reviewed by two radiologists. Results There were 373 outpatients identified by a search using the word ‘chest pain’ who underwent chest CT. Eight patients satisfying the imaging criteria were diagnosed with EFN. The proportions of patients diagnosed with EFN were 10.7%, 4.8%, 2.8%, 0.9% and 0% in the departments of general medicine, cardiovascular surgery, emergency medicine, cardiovascular internal medicine and respiratory medicine, respectively. Only 12.5% of the patients were correctly diagnosed with EFN, and the other patients were treated for musculoskeletal symptoms, acute pericarditis or hypochondriasis. Conclusion EFN is not rare and is often overlooked in various departments. All physicians as well as emergency physicians should consider the possibility of EFN as the cause of pleuritic chest pain.

Factors associated with gravity-dependent distribution on chest CT in elderly patients with community-acquired pneumonia: a retrospective observational study

Although lung involvement in aspiration pneumonia typically has a gravity-dependent distribution on chest images, which patient’s conditions contribute to its radiological pattern has not been fully elucidated. This study was designed to determine the factors associated with the gravity-dependent distribution of community-acquired pneumonia (CAP) on chest computed tomography (CT). This retrospective study included elderly patients aged ≥ 65 years with CAP who underwent chest CT within 1 week before or after admission. The factors associated with lower lobe- and posterior-predominant distributions of ground glass opacity or airspace consolidation were determined. Of the 369 patients with CAP, 348 (94%) underwent chest CT. Multivariate analyses showed that impaired consciousness, a low Barthel index of activities of daily living, and high hemoglobin levels were associated with lower lobe-predominant distribution, while male sex and impaired consciousness were associated with posterior-predominant distribution. Cerebrovascular diseases were unrelated to these distributions. While male sex, impaired consciousness, high hemoglobin levels, low albumin levels, and the number of involved lobes were associated with in-hospital mortality, gravity-dependent distributions were not. Impaired consciousness might be the most significant predictor of aspiration pneumonia; however, the gravity-dependent distribution of this disease is unlikely to affect disease prognosis.