Imaging patterns of Lophomonas blattarum infection in the respiratory tract: a registry-based analysis.

BACKGROUND: Lophomonas blattarum is an emerging protozoan that mostly infects the lower respiratory tract and causes pulmonary lophomoniasis. Radiologic findings in patients with pulmonary lophomoniasis have yet to be studied. Thus, we conducted a registry-based clinical investigation to evaluate the radiologic findings of lophomoniasis. METHODS: In this cross-sectional study, 34 Lophomonas positive patients were enrolled. Demographic data, relevant characteristics, and radiologic findings of the patients were recorded and analyzed. RESULTS: Thirty-four (male = 18, female = 16) patients with an average age of 52.21 ± 20.48 years old were examined. Radiological findings such as Alveolar consolidation (26.5%), Ground glass opacity (5.9%), Centrilobular nodules (23.5%), Tree -in- bud (38.2%), Cavitation (23.5%), Pleural effusion (23.5%), Interstitial opacity (8.8%), Lymphadenopathy (23.5%), Bronchocele (5.9%), Bronchiectasis (29.4%), Nodules (8.8%) and Mass (11.8%) were obtained, that the frequency of all radiological findings was less than 50%. CONCLUSION: In this study, the most common radiological findings in patients with lophomoniasis were tree-in-bud nodules, alveolar consolidation, bronchiectasis, and centrilobular nodules which were mostly seen in the right lung and its middle and lower lobes. Given that the radiologic findings of this disease are unknown, it can be considered in differential diagnosis.

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19.

INTRODUCTION: In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results. METHODS: We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters. RESULTS: Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively. CONCLUSIONS: These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

The feasibility of using acoustic measures for predicting the Total Opacity Scores of chest computed tomography scans in patients with COVID-19.

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case-control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI.

Phenotype and Clinicoradiological Differences in Multifocal and Focal Bronchiectasis.

Introduction: Bronchiectasis is a chronic progressive respiratory disease characterized by permanent dilatation of the bronchi. It is a complex condition with numerous different etiologies, co-morbidities, and a heterogeneous presentation. As we know, there is a lack of studies that describe the differences and compare the characteristics between focal and multifocal bronchiectasis. The aim of this study is to identify differences in clinical characteristics presentation, severity or distribution in focal and multifocal bronchiectasis, and prognostic implications. Methods: 126 patients with computed tomography (CT)-verified bronchiectasis were enrolled. Baseline characteristics that included age, sex, smoking history, and respiratory symptoms were recorded, with special attention paid to hemoptysis appearance, body mass index, and comorbidities. The type of bronchiectasis determined by CT scan and the modified Reiff scores indicating radiological severity were recorded. Patients were divided in two groups (I is focal and II is multifocal). Results: There were no statistically significant differences in age, smoking status, comorbidity, and BMI between the two groups. Multifocality was associated with a significantly higher proportion of females (p = 0.014), the rate of hemoptysis (p = 0.023), and the number of hospitalizations, but not of exacerbations and prevalence of immunodeficiency (p = 0.049). Significantly, a high number of subjects with multifocality had bronchiectasis of moderate severity, and post-infective and asthma-associated phenotypes were the dominant in this group. Unexpectedly, the cystic and varicose radiological phenotype (which need more time to develop) were more dominant in the focal group. The cylindrical phenotype was equally observed in the multifocal and focal groups. Conclusions: Our study suggests that multifocality is not related to age, number of exacerbations, or radiological phenotype, but it seems to be associated with the clinical post-infective phenotype, immunodeficiency, frequent hospitalizations, and severity. Thus, the presence of multifocal bronchiectasis may act as a biomarker of severity and poor outcomes in these patients.

Role of chest CT scan in patients with preexisting cancer and COVID-19 pneumonia.

BACKGROUND: Detection of COVID-19 in cancer patients is challenging due to probable preexisting pulmonary infiltration caused by many infectious and non-infectious etiologies. We evaluated chest CT scan findings of COVID-19 pneumonia in cancer patients and explored its prognostic role in mortality. METHODS: We studied 266 COVID-19 patients with a history of cancer diagnosis between 2020 and 2022. Chest CT images were reported based on Radiological Society of North America (RSNA) structural report and the CT score and pattern of involvement were noted. We used multivariate logistic regression models to determine the association between CT scan findings and mortality of the cancer COVID-19 patients. RESULTS: The mean age was 56.48 (± 18.59), and 53% were men. Gastrointestinal (29.3%), hematologic (26.3%), and breast (10.5%) cancers were the most frequent types of cancer. The prevalence of atypical or indeterminate findings in the chest CT was 42.8%. Most radiologic findings were consolidation mixed with ground-glass opacity (44.4%), pleural effusion (33.5%), and pure ground-glass opacity (19.5%). The risk of death was higher among those who had typical chest CT for COVID-19 (OR 3.47; 95% CI 1.14-8.98) and those who had a severity of score higher than 18 (OR 1.89; 95% CI 1.07-3.34). Also, presence of consolidation (P value 0.040), pleural effusion (P value 0.000), centrilobular nodules (P value 0.013), and architectural distortion (P value 0.005) were associated with a poorer prognosis. CONCLUSION: Less than half of COVID-19 patients with a history of cancer had typical imaging features of COVID-19. Radiologists should be aware of atypical, rare, or subtle chest CT findings in patients with pre-existing cancer.

An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan.

PURPOSE: To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1). METHODS: Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power. RESULTS: The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%. CONCLUSION: This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Estimation of cancer risks due to chest radiotherapy treatment planning computed tomography (CT) simulations.

The objective of our study was to determine organ doses to estimate the lifetime attributable risk (LAR) of cancer incidence related to chest tomography simulations for Radiotherapy Treatment Planning (RTTP) using patient-specific information. Patient data were used to calculate organ doses and effective dose. The effective dose (E) was calculated by two methods. First, to calculate effective dose in a standard phantom, the collected dosimetric parameters were used with the ImPACT CT Patient Dosimetry Calculator and E was calculated by applying related correction factors. Second, using the scanner-derived Dose Length Product, LARs were computed using the US National Academy of Sciences (BEIR VII) model for age- and sex-specific risks at each exposure. DLP, CTDIvol, and scan length were 507 ± 143 mGy.cm, 11 ± 4 mGy, and 47 ± 7 cm, respectively. The effective dose was 10 ± 3 mSv using ImPACT patient dosimetry calculator software and 9 ± 2 mSv using the scanner-derived Dose Length Product. The LAR of cancer incidence for all cancers, all solid cancers and leukemia were 65 ± 29, 62 ± 27, 7 ± 2 cases per 100,000 individuals, respectively. Radiation exposure from the usage of CT for radiotherapy treatment planning (RTTP) causes non-negligible increases in lifetime attributable risk. The results of this study can be used as a guide by physicians to implement strategies based on the As Low As Reasonably Achievable (ALARA) principle that lead to a reduction dose without sacrificing diagnostic information.

Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?

Introduction: This study intended to assess the dose length product (DLP), effective cumulative radiation dose (E.D.), and additional cancer risk (ACR) due to a chest CT scan to detect or follow up the Covid-19 disease in four university-affiliated hospitals that used different imaging protocols. Indeed, this study aimed to examine the differences in decision-making between different imaging centers in choosing chest CT imaging protocols during the pandemic, and to assess whether a new diagnostic reference level (DRL) is needed in pandemic situations. Methods: This retrospective study assessed the E.D. of all chest imagings for Covid-19 for six months in four different hospitals in our country. Imaging parameters and DLP (mGy.cm) were recorded. The E.D.s and ACRs from chest CT scans were calculated using an online calculator. Results: Thousand-six hundred patients were included in the study. The mean cumulative dose due to chest CT was 3.97 mSv which might cause 2.59 × 10-2 ACR. The mean cumulative E.D. in different hospitals was in the range of 1.96-9.51 mSv. Conclusions: The variety of mean E.D.s shows that different hospitals used different imaging protocols. Since there is no defined DRL in the pandemic, some centers use routine protocols, and others try to reduce the dose but insufficiently.In pandemics such as Covid-19, when CT scan is used for screening or follow-up, DLPs can be significantly lower than in normal situations. Therefore, international regularized organizations such as the international atomic energy agency (IAEA) or the international commission on radiological protection (IRCP) should provide new DRL ranges.

Chest computer tomography is safe without additional interrogation or monitoring for modern cardiac implantable electrical devices.

INTRODUCTION: Whether diagnostic computed tomography (CT) scans to cardiac implantable electronic devices (CIED) is safe in recent models remains unknown. METHODS: A two-centers observational study. Over 14 years, consecutive 2362 chest CT scans (1666 pacemakers [PMs], 145 cardiac resynchronization therapy PM, 316 implantable cardioverter-defibrillator, and 233 cardiac resynchronization therapy defibrillator) were interrogated and monitored upon imaging. RESULTS: Electromagnetic interference occurred only in a few old models: InSync 8040 (n = 14), InSync III Marquis (n = 1), and Kappa (n = 4), which resulted no adverse events. CONCLUSION: CIEDs, especially recent ones, are confirmed safe on chest CT.

Residual lung damage following ARDS in COVID-19 ICU survivors.

BACKGROUND: Coronavirus disease 2019 acute respiratory distress syndrome (COVID-19 ARDS) is a disease that often requires invasive ventilation. Little is known about COVID-19 ARDS sequelae. We assessed the mid-term lung status of COVID-19 survivors and investigated factors associated with pulmonary sequelae. METHODS: All adult COVID-19 patients admitted to the intensive care unit from 25th February to 27th April 2020 were included. Lung function was evaluated through chest CT scan and pulmonary function tests (PFT). Logistic regression was used to identify predictors of persisting lung alterations. RESULTS: Forty-nine patients (75%) completed lung assessment. Chest CT scan was performed after a median (interquartile range) time of 97 (89-105) days, whilst PFT after 142 (133-160) days. The median age was 58 (52-65) years and most patients were male (90%). The median duration of mechanical ventilation was 11 (6-16) days. Median tidal volume/ideal body weight (TV/IBW) was 6.8 (5.71-7.67) ml/Kg. 59% and 63% of patients showed radiological and functional lung sequelae, respectively. The diffusion capacity of carbon monoxide (DLCO ) was reduced by 59%, with a median per cent of predicted DLCO of 72.1 (57.9-93.9) %. Mean TV/IBW during invasive ventilation emerged as an independent predictor of persistent CT scan abnormalities, whilst the duration of mechanical ventilation was an independent predictor of both CT and PFT abnormalities. The extension of lung involvement at hospital admission (evaluated through Radiographic Assessment of Lung Edema, RALE score) independently predicted the risk of persistent alterations in PFTs. CONCLUSIONS: Both the extent of lung parenchymal involvement and mechanical ventilation protocols predict morphological and functional lung abnormalities months after COVID-19.

Management of primary spontaneous pneumothorax in teenagers: An 11-year study.

AIM: Management of teenagers with primary spontaneous pneumothorax (PSP) is not consensual. We report our experience over an 11-year period. METHODS: For each patient under 20 years hospitalised with PSP from 2008 to 2018, demographic data, smoking habits, clinical presentation, hospitalisation unit, radiological management and its results, therapeutic management (observation, needle aspiration, chest tube drainage and surgery), complications, length of stay, given advice at discharge and recurrence were collected. RESULTS: Seventy patients were included in different paediatric or adult surgery or pulmonology wards (82.9% boys; 16.8 ± 1.7 years; one severe presentation; 18/58 smokers). Chest CT-scan (n = 42/70, 60%) revealed blebs/bullae in 18/39 examinations (46.2%). Treatment consisted of observation (14/70, 20%), needle aspiration (2/70, 2.9%), chest tube (53/70, 75.7%) and video-assisted thoracoscopy surgery (27/70, 38.6%). Half patients with interventional procedure presented complications. A median of 10 chest X-rays was noted during a median stay of 8 days. Advice concerning sport practice, flying, smoking, etc., was variably delivered. PSP recurrence concerned 35/70 patients (50%) without identified predictive factors. CONCLUSION: Compared to recent recommendations of a more conservative approach, chest CT-scan and interventional strategy are overused in our teenagers with PSP. Observation, more or less needle aspiration, should be clearly the first-line treatments.

Clinical and imaging findings of patients diagnosed with adenovirus-positive pneumonia during 2015-2019 in Shanghai, China.

BACKGROUND: This study was to describe the clinical characteristics, chest CT image findings, and potential role of T cells immunity in adenovirus positive pneumonia. METHODS: In this retrospective study, medical records of 53 adult Adv+ patients who were admitted to the Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, from May 2015 to August 2019 were included. The presence of adenovirus and other respiratory viruses was detected using polymerase chain reaction of throat swabs samples. Clinical features and chest computed tomography (CT) findings were compared between patients with Adv+ pneumonia and Adv+ non-pneumonia. RESULTS: The top 3 most commonly occurring symptoms in Adv+ pneumonia patients were fever (66.7%), cough (63.3%), and tachypnea (16.7%). Patients with Adv+ pneumonia showed significantly higher rates of cough and fever and longer duration of hospitalization than patients with Adv+ non-pneumonia. In the Adv+ pneumonia group, consolidation (73.3%) was the most common imaging finding on chest CT scan, and the likelihood of involvement of bilateral lobes (60%) was high. Classical conspicuous consolidation with surrounding ground-glass opacity was observed in 5 (16.6%) patients with Adv+ pneumonia. Patients with Adv+ pneumonia showed a higher inhibition of T-cell immunity than did patients with Adv+ non-pneumonia, and counts of CD3+, CD4+, and CD8+ T-cells may predict the presence of pneumonia in Adv+ patients. DISCUSSION: With regard to Adv+ pneumonia, the most frequent symptoms were cough and fever, and the most common CT pattern was consolidation; classical CT findings such as consolidation with surrounding ground-glass opacity could also be observed. Furthermore, our data indicated the incidence of abrogated cellular immunity in patients with Adv+ pneumonia.

Cancer surgery during the COVID-19 pandemic: The experience of a comprehensive cancer center performing preoperative screening by RT-PCR and chest CT scan.

BACKGROUND AND OBJECTIVES: During the worldwide pandemic of coronavirus disease 2019 (COVID-19), oncological procedures considered to be urgent could not be delayed, and a specific procedure was required to continue surgical activity. The objective was to assess the efficacy of our preoperative screening algorithm. METHODS: This observational retrospective study was performed between the 25th of March and the 12th of May 2020 in a comprehensive cancer center in France. Patients undergoing elective oncologic surgery were tested by preoperative nasopharyngeal reverse-transcription polymerase chain reaction (RT-PCR) that could be associated with a chest computerized tomography (CT) scan. RESULTS: Of the 510 screening tests (in 477 patients), only 5% (15/477) were positive for COVID-19 in 24 patients (18 RT-PCR+ and 7 CT scan+/RT-PCR-). Four patients were ultimately false positives based on the CT scan. In total, only 4.2% (20/477) of the patients were COVID-19+. The positivity rate decreased with time after the containment measures were implemented (from 7.4% to 0.8%). In the COVID-19+ group, 20% of the patients had postoperative pulmonary complications, whereas this was the case for 5% of the patients in the COVID-19 group. CONCLUSIONS: Maintaining secure surgical activity is achievable and paramount in oncology care, even during the COVID-19 pandemic, with appropriate screening based on preoperative RT-PCR.

Pneumothorax leading to pneumopericardium after transvenous lead implantation in a patient with previous epicardial lead.

A 44-year-old lady, a follow-up case of idiopathic dilated cardiomyopathy and cardiac resynchronization therapy defibrillator device implantation with epicardial left ventricular (LV) lead, underwent a transvenous LV lead revision in view of epicardial lead malfunction. A chest X-ray after this, done for worsening dyspnea, revealed pneumopericardium along with left pneumothorax. The computed tomography (CT) revealed a communication between the left pleural and pericardial cavities, around the old epicardial lead. Drainage of the left pleural cavity resolved both the pneumothorax and pneumopericardium and the patient remained well on follow up.

Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study.

Background and Aims Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19). Methods A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed. Results Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p = 0.019), mixed ground-glass opacities (GGOs) and consolidation (p < 0.001), pleural effusion (p < 0.001), and intralesional traction bronchiectasis (p = 0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p < 0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71 ± 6.26 versus 4.86 ± 3.52, p < 0.001), with an area under the curve of 0.91 (0.88-0.94) for COVID-19 detection. Conclusions Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC = 0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

[Computed tomographic features of coronavirus disease-2019 (COVID-19) pneumonia in three groups of Iranian patients: A single center study]. / Características en tomografía computarizada de la neumonía por coronavirus-2019 (COVID-19) en tres grupos de pacientes iraníes: estudio de un solo centro.

Introduction and Objectives: The pivotal role of chest computed tomographic (CT) to diagnosis and prognosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. This study was conducted to assess the CT features in confirmed cases with COVID-19. Materials and Methods: Retrospectively, initial chest CT data of 363 confirmed cases with COVID-19 were reviewed. All subjects were stratified into three groups based on patients' clinical outcomes; non-critical group (n=194), critical group (n=65), and death group (n=104). The detailed of CT findings were collected from patients' medical records and then evaluated for each group. In addition, multinomial logistic regression was used to analyze risk factors according to CT findings in three groups of patients with COVID-19. Results: Compared with the non-critical group, mixed ground-glass opacities (GGO) and consolidation lesion, pleural effusion lesion, presence of diffuse opacity in cases, more than 2 lobes involved and opacity scores were significantly higher in the critical and death groups (P<0.05). Having more mixed GGO with consolidation, pleural effusion, lack of pure GGO, more diffuse opacity, involvement of more than 2 lobes and high opacity score identified as independent risk factors of critical and death groups. Conclusion: CT images of non-critical, critical and death groups with COVID-19 had definite characteristics. CT examination plays a vital role in managing the current COVID-19 outbreak, for early detection of COVID-19 pneumonia. In addition, initial CT findings may be useful to stratify patients, which have a potentially important utility in the current global medical situation.

Sensitivity and specificity of chest computed tomography scan based on RT-PCR in COVID-19 diagnosis.

PURPOSE: COVID-19 is a novel, severely contagious and progressive infection occurring worldwide. The diagnosis of the disease is based on real-time polymerase chain reaction (RT-PCR) and computed tomography (CT) scan, even though they are still controversial methods. MATERIAL AND METHODS: We studied 54 patients with suspected COVID-19 and the two mentioned methods were compared with each other. RESULTS: Sensitivity and specificity of the abnormal chest CT scan, ground-glass opacity (GGO), consolidation opacity, and both of GGO and consolidation were also surveyed based on RT-PCR. The results showed that RT-PCR assay was negative in 23 (42.6%) patients and positive in 31 (57.4%) cases. Also, the patients with an abnormal chest CT scan comprised 37 (68.5%). The sensitivity and specificity of abnormal CT scan were 78.6% and 42.3%, respectively, based on the RT-PCR method. CONCLUSIONS: Other techniques alongside CT scan and RT-PCR are advocated for accuracy of the COVID-19 diagnosis.

Clinical features of 50 cases of 2019 novel coronavirus in Bandar Abbas, Iran.

Background: There is sparse information to describe the clinical features and outcomes of patients infected with coronavirus disease 2019 (COVID-19). Methods: In a single-center retrospective observational study, 50 patients infected with COVID-19 were studied. Epidemiological, demographic, clinical, laboratory, radiological, and treatment data were collected and analyzed. Outcomes of critically ill patients and noncritically ill patients were compared. Results: The mean age of the patients was 48.8 years, with male predominance. Dry cough, fever, and dyspnea were the most complaining symptoms on admission. Chronic medical illnesses before admission were present in 56% of the patients. The most common laboratory abnormalities were lymphopenia, neutrophilia, thrombocytopenia, increased aspartate aminotransferase, high serum creatinine level, elevated lactate dehydrogenase, and increasing ESR and CRP levels. Bilateral mixed ground-glass opacity and consolidation were observed in chest CT scan of most patients. Some patients required supplemental oxygen and some needed invasive mechanical ventilation. Blood oxygen saturation was different between survivors and nonsurvivors. 10% of patients died, of whom 60% were men. 40% of dead cases had chronic medical illnesses; 60% underwent invasive mechanical ventilation. Conclusion: Among the patients diagnosed with COVID-19 infection, the frequent clinical presentation was with a wide range of signs and symptoms. The laboratory changes suggest that COVID-19 infection may be related to cellular immune deficiency, myocardial, hepatic, and kidney injury. Additional research is needed to elucidate COVID-19 pathogenesis.