Findings of Abdominal Imaging in Patients with COVID-19 - Part 1: Hollow Organs.

Since COVID-19 has spread worldwide, the role of imaging for early detection of the disease has become more prominent. Abdominal symptoms in COVID-19 are common in addition to respiratory manifestations. This review collected the available data about abdominal computed tomography (CT) and ultrasonography indications in hollow abdominal organs in patients with COVID-19 and their findings. Since abdominal imaging is less frequently used in COVID-19, there is limited information about the gastrointestinal findings. The most common indications for abdominal CT in patients with COVID-19 were abdominal pain and sepsis. Bowel wall thickening and fluid-filled colon were the most common findings in abdominal imaging. Acute mesenteric ischemia (AMI) was one of the COVID-19 presentations secondary to coagulation dysfunction. AMI manifests with sudden abdominal pain associated with high morbidity and mortality in admitted patients; therefore, CT angiography should be considered for early diagnosis of AMI. Ultrasonography is a practical modality because of its availability, safety, rapidity, and ability to be used at the bedside. Clinicians and radiologists should be alert to indications and findings of abdominal imaging modalities in COVID-19 to diagnose the disease and its potentially serious complications promptly.

Abdominal Imaging Findings in Patients with COVID-19 Part 2: Solid Organs

[...]although solid abdominal organs are rarely affected by COVID-19, clinicians must be familiar with the manifestations since they are associated with the disease severity and poor outcome. Keywords: COVID-19, Abdominal, Imaging, Computed tomography, Ultrasonography Introduction The world has been confronting the upsurge of coronavirus disease 2019 (COVID-19) since the first novel coronavirus infection (SARS-CoV-2) initially emerged in China in December 2019.1 The most common symptoms reported in COVID-19 are related to respiratory system involvement, including fever, dry cough, fatigue, and dyspnea.2 Angiotensin-converting enzyme 2 (ACE2) plays a significant role in mediating the inflammation of COVID-19, which can contribute to COVID-19 manifestations.3 ACE2 receptors are found in various cells, including hepatocytes, cholangiocytes, podocytes, and enterocytes.2,3 Forty percent of infected patients have shown gastrointestinal (GI) manifestations, including loss of taste, nausea, vomiting, diarrhea, and abdominal pain.4 A significant number of patients have GI symptoms, and sometimes it is the only presentation of the disease without respiratory manifestations.2 The reverse-transcriptase polymerase-chain-reaction (RT-PCR) diagnostic test and chest computed tomography (CT) were reported to be highly sensitive in the early diagnostic stage of suspected COVID-19.5 Cross-sectional abdominal imaging is not usually used in COVID-19.6 Nevertheless, abdominal CT may be performed if specific symptoms exist, such as abdominal pain. Radzina et al found that multiparametric ultrasonography may be more sensitive than CT and Magnetic resonance imaging in assessing liver damage at the cellular level in patients with COVID-19 before progressing into liver cirrhosis.37 Pancreas Given the fact that ACE2 receptors are vastly expressed in pancreatic islet cells, COVID-19 can induce islet cell damage presenting with acute diabetes.38 The pancreatic involvement can occur through the direct invasion by SARS-CoV2, a systemic response to pneumonia, or a destructive immune reaction due to viral stimulation.19 According to Wang and colleagues, the pancreas was affected in 17% of patients with COVID-19 pneumonia.19 In reported cases of SARS-CoV-2 infection, abdominal CT revealed features of acute pancreatitis, including edema and inflammation of the pancreas with surrounding fluid collections and fat stranding30-39 (Figure 3). Kidney According to Pei et al, the most prevalent renal abnormalities in the setting of COVID-19 were proteinuria and hematuria, with acute kidney injury (AKI) happening less often.50 Renal infarct might occur because of hypercoagulation.6 The possible mechanisms of AKI in COVID-19 might be related to a variety of factors, including cytokine release syndrome, hypoxia, endotoxin produced by superimposed infections during ICU admission, and rhabdomyolysis.51 Different studies have established that AKI considerably increased the mortality rate in admitted patients with COVID-19.20 Renal parenchymal hypodensity and perirenal fat stranding on non-enhancement CT in patients with COVID-19 represent severe renal impairment.52 Like the spleen, the most common renal finding in abdominal tomograms was infarction.12 In such conditions, the affected kidney presents with patchy, sharply demarcated heterogeneous areas with hypoenhancement.6 A summary of renal imaging findings is shown in Table 6.

Susceptibility of Patients with Inflammatory Bowel Disease to COVID-19 Compared with Their Households.

BACKGROUND: Immunosuppressive agents used in the treatment of inflammatory bowel diseases (IBDs) could potentially increase the risk of coronavirus disease 2019 (COVID-19). We aimed to compare COVID-19 frequency in patients with IBD with their households and identify the related risk factors. METHODS: Firstly, a multi-centered, observational study on 2110 patients with IBD and 2110 age-matched household members was conducted to compare COVID-19 frequency. Secondly, the data of patients with IBD and COVID-19 who had called the COVID-19 hotline were added. Multivariable logistic regression was used to evaluate the effect of age, type and severity of IBD, the number of comorbidities, and medications on the frequency of COVID-19 among the patients with IBD. RESULTS: The prevalence of COVID-19 in patients with IBD and household groups was similar (34 [1.61%] versus 35 [1.65%]; P = 0.995). The prevalence of COVID-19 increased from 2.1% to 7.1% in those with three or more comorbidities (P = 0.015) and it was significantly higher in those with severe IBD (P = 0.026). The multivariable analysis only showed a significant association with anti-TNF monotherapy (OR: 2.5, CI: 0.97-6.71, P = 0.05), and other medications were not associated with COVID-19. CONCLUSION: The prevalence of COVID-19 in patients with IBD was similar to the household members. Only patients with IBD receiving anti-TNF monotherapy had a higher risk of COVID-19 susceptibility. This finding could be attributed to the higher exposure to the virus during administration in health care facilities.

Black Fungus and beyond: COVID-19 associated infections.

Globally, many hospitalized COVID-19 patients can experience an unexpected acute change in status, prompting rapid and expert clinical assessment. Superimposed infections can be a significant cause of clinical and radiologic deviations in this patient population, further worsening clinical outcome and muddling the differential diagnosis. As thrombotic, inflammatory, and medication-induced complications can also trigger an acute change in COVID-19 patient status, imaging early and often plays a vital role in distinguishing the cause of patient decline and monitoring patient outcome. While the common radiologic findings of COVID-19 infection are now widely reported, little is known about the clinical manifestations and imaging findings of superimposed infection. By discussing case studies of patients who developed bacterial, fungal, parasitic, and viral co-infections and identifying the most frequently reported imaging findings of superimposed infections, physicians will be more familiar with common infectious presentations and initiate a directed workup sooner. Ultimately, any abrupt changes in the expected COVID-19 imaging presentation, such as the presence of new consolidations or cavitation, should prompt further workup to exclude superimposed opportunistic infection.

Proposing a novel deep network for detecting COVID-19 based on chest images.

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Outcomes of COVID-19 in Patients with Inflammatory Bowel Disease: Comparison with Household Members and the Role of IBD Medications.

BACKGROUND: Most data on the effect of inflammatory bowel disease (IBD) and its treatments on coronavirus disease 2019 (COVID-19) outcomes have not had non-IBD comparators. Hence, we aimed to describe COVID-19 outcomes in IBD compared to non-IBD patients. METHODS: We conducted a prospective cohort study of registered IBD patients with confirmed COVID-19 from six provinces in Iran from February to April 2020. Proven COVID-19 patients were followed up at four weeks and the frequency of outcomes was assessed. Multivariable logistic regression was used to assess associations between demographics, clinical characteristics and COVID-19 outcomes. RESULTS: Overall, 2159 IBD patients and 4721 household members were enrolled, with 84 (3.9%) and 49 (1.1%) participants having confirmed COVID-19, respectively. Household spread of COVID-19 was not common in this cohort (1.2%). While hospitalization was significantly more frequent in IBD patients compared with non-IBD household members (27.1% vs. 6.0%, P=0.002), there was no significant difference in the frequency of severe cases. Age and presence of IBD were positively associated with hospitalization in IBD compared with non-IBD household members (OR: 1.06, 95% CI: 1.03-1.10; OR: 5.7, 95% CI: 2.02- 16.07, respectively). Age, presence of new gastrointestinal symptoms, and 5-aminosalicylic acid (5-ASA) use were associated with higher hospitalization rate in IBD patients (OR: 1.13, 95% CI: 1.05-1.23; OR: 6.49, 95% CI: 1.87-22.54; OR: 6.22, 95% CI: 1.90-20.36, respectively). Anti-tumor necrosis factor (TNF) was not associated with more severe outcomes. CONCLUSION: Age, presence of new gastrointestinal symptoms and use of 5-ASA were associated with increased hospitalization rate among IBD patients, while anti-TNF therapy had no statistical association.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.

We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

A Reliable Prognostic Marker for Liver Dysfunction in COVID-19 Infection.

BACKGROUND In December 2019, COVID-19 emerged from China and spread to become a pandemic, killing over 1,350,000 up to November 18, 2020. Some patients with COVID-19 have abnormal liver function tests. We aimed to determine the clinical significance of liver chemistries in patients with COVID-19. METHODS We performed a cross-sectional study of 1044 consecutive patients with confirmed COVID-19 in two referral hospitals in Tehran, Iran, from February to April 2020. All cases were diagnosed by clinical criteria and confirmed by characteristic changes in the spiral chest computed tomography (CT) and nucleic acid testing of the nasopharyngeal samples. We evaluated the association between abnormal liver enzymes or function tests and survival, intensive care unit (ICU) admission and fatty liver changes in CT scans. RESULTS The mean age was 61.01 ± 16.77 years, and 57.68% were male. Of 495 patients with elevated alanine transaminase (ALT) levels, 194 had chest CT scans, in which fatty liver disease was seen in 38.1%. 41 patients (21.13%) had moderate to severe, and 33 (17.01%) had borderline fatty liver disease. Bilirubin, albumin, and partial thromboplastin time (PTT), along with other markers such as HCO3, C-reactive protein (CRP), triglyceride, and length of admission, were significantly associated with ICU admission and mortality. Prothrombin time (PT), platelet count, and low-density lipoprotein (LDL) levels were also correlated with mortality. Fasting blood sugar (FBS) and pH were important indices in ICU admitted patients. CONCLUSION Liver function tests accurately predict a worse prognosis in patients with COVID-19. However, liver enzymes were only slightly increased in those who died or needed ICU admission and were not related to the fatty liver changes.

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.

OBJECTIVE: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). CONCLUSION: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

Clinical characteristics and outcomes of diabetics hospitalized for COVID-19 infection: a single-centered, retrospective, observational study.

Some debates exist regarding the association of diabetes mellitus (DM) with COVID-19 infection severity and mortality. In this study, we aimed to describe and compare the clinical characteristics and outcomes of hospitalized COVID-19 patients with and without DM. In this single-centered, retrospective, observational study, we enrolled adult patients with COVID-19 who were admitted to the Shariati hospital, Tehran, Iran, from February 25, 2020, to April 21, 2020. The clinical and paraclinical information as well as the clinical outcomes of patients were collected from inpatient medical records. A total of 353 cases were included (mean age, 61.67 years; 57.51 % male), of whom 111 patients were diabetics (mean age, 63.66 years; 55.86 % male). In comparison to those without DM, diabetic patients with COVID-19 were more likely to have other comorbidities, elevated systolic blood pressure (SBP), elevated blood sugar (BS), lower estimated glomerular filtration rate (eGFR) and elevated blood urea nitrogen (BUN). The association of DM with severe outcomes of COVID-19 infection (i.e. mechanical ventilation, median length of hospital stay and mortality) remained non-significant before and after adjustments for several factors including age, sex, body mass index (BMI), smoking status, and comorbidities. Based on our results DM has not been associated with worse outcomes in hospitalized patients for COVID-19 infection.

SARS-CoV-2 antibody seroprevalence in the general population and high-risk occupational groups across 18 cities in Iran: a population-based cross-sectional study.

BACKGROUND: Rapid increases in cases of COVID-19 were observed in multiple cities in Iran towards the start of the pandemic. However, the true infection rate remains unknown. We aimed to assess the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 18 cities of Iran as an indicator of the infection rate. METHODS: In this population-based cross-sectional study, we randomly selected and invited study participants from the general population (from lists of people registered with the Iranian electronic health record system or health-care centres) and a high-risk population of individuals likely to have close social contact with SARS-CoV-2-infected individuals through their occupation (from employee lists provided by relevant agencies or companies, such as supermarket chains) across 18 cities in 17 Iranian provinces. Participants were asked questions on their demographic characteristics, medical history, recent COVID-19-related symptoms, and COVID-19-related exposures. Iran Food and Drug Administration-approved Pishtaz Teb SARS-CoV-2 ELISA kits were used to detect SARS-CoV-2-specific IgG and IgM antibodies in blood samples from participants. Seroprevalence was estimated on the basis of ELISA test results and adjusted for population weighting (by age, sex, and city population size) and test performance (according to our independent validation of sensitivity and specificity). FINDINGS: From 9181 individuals who were initially contacted between April 17 and June 2, 2020, 243 individuals refused to provide blood samples and 36 did not provide demographic information and were excluded from the analysis. Among the 8902 individuals included in the analysis, 5372 had occupations with a high risk of exposure to SARS-CoV-2 and 3530 were recruited from the general population. The overall population weight-adjusted and test performance-adjusted prevalence of antibody seropositivity in the general population was 17·1% (95% CI 14·6-19·5), implying that 4 265 542 (95% CI 3 659 043-4 887 078) individuals from the 18 cities included were infected by the end of April, 2020. The adjusted seroprevalence of SARS-CoV-2-specific antibodies varied greatly by city, with the highest estimates found in Rasht (72·6% [53·9-92·8]) and Qom (58·5% [37·2-83·9]). The overall population weight-adjusted and test performance-adjusted seroprevalence in the high-risk population was 20·0% (18·5-21·7) and showed little variation between the occupations included. INTERPRETATIONS: Seroprevalence is likely to be much higher than the reported prevalence of COVID-19 based on confirmed COVID-19 cases in Iran. Despite high seroprevalence in a few cities, a large proportion of the population is still uninfected. The potential shortcomings of current public health policies should therefore be identified to prevent future epidemic waves in Iran. FUNDING: Iranian Ministry of Health and Medical Education. TRANSLATION: For the Farsi translation of the abstract see Supplementary Materials section.

Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study.

BACKGROUND: COVID-19 has caused great concern for patients with underlying medical conditions. We aimed to determine the prognosis of patients with current or previous cancer with either a PCR-confirmed COVID-19 infection or a probable diagnosis according to chest CT scan. METHODS: We conducted a case control study in a referral hospital on confirmed COVID-19 adult patients with and without a history of cancer from February25th to April21st, 2020. Patients were matched according to age, gender, and underlying diseases including ischemic heart disease (IHD), diabetes mellitus (DM), and hypertension (HTN). Demographic features, clinical data, comorbidities, symptoms, vital signs, laboratory findings, and chest computed tomography (CT) images have been extracted from patients' medical records. Multivariable logistic regression was used to estimate odd ratios and 95% confidence intervals of each factor of interest with outcomes. RESULTS: Fifty-three confirmed COVID-19 patients with history of cancer were recruited and compared with 106 non-cancerous COVID-19 patients as controls. Male to female ratio was 1.33 and 45% were older than 65. Dyspnea and fever were the most common presenting symptoms in our population with 57.86 and 52.83% respectively. Moreover, dyspnea was significantly associated with an increased rate of mortality in the cancer subgroup (p = 0.013). Twenty-six patients (49%) survived among the cancer group while 89 patients (84%) survived in control (p = 0.000). in cancer group, patients with hematologic cancer had 63% mortality while patients with solid tumors had 37%. multivariate analysis model for survival prediction showed that history of cancer, impaired consciousness level, tachypnea, tachycardia, leukocytosis and thrombocytopenia were associated with an increased risk of death. CONCLUSION: In our study, cancer increased the mortality rate and hospital stay of COVID-19 patients and this effect remains significant after adjustment of confounders. Compared to solid tumors, hematologic malignancies have been associated with worse consequences and higher mortality rate. Clinical and para-clinical indicators were not appropriate to predict death in these patients.

A Multicenter Survey on the Trend of Chest CT Scan Utilization: Tracing the First Footsteps of COVID-19 in Iran.

BACKGROUND: Chest computed tomography (CT) scan has been used widely to diagnose COVID-19 in Iran. OBJECTIVES: To trace the footsteps of COVID-19 in Iran by exploring the trend in using chest CT scans and its economic impact on radiology departments. Methods: In this cross-sectional study, the number of imaging examinations from 33 tertiary radiology departments in 9 large cities of Iran was collected from September 23, 2019 to March 20, 2020 (Months 1 to 6) and the corresponding months in 2018-2019. RESULTS: A 50.2% increase was noted in the chest CT scan utilization in 2019-2020 compared to 2018-2019. This increase was +15%, +15%, +27%, +2%, +1% in Months 1-5 of 2019-2020, respectively. In Month 6 of 2019-2020, a 251% increase in the acquisition of chest CT scans was observed compared to the Month 6 of 2018-2019. Following negative balance of revenue from Month 1 to 5 with respect to the inflation rate, the total income in Month 6 was further 1.5% less than the same Month in 2018-19. CONCLUSION: The observed peak in chest CT utilization in Month 3 prior to the surge in Month 6 could be explained by the seasonal influenza. However, unawareness about an emerging viral disease, i.e. COVID-19, might have underutilized chest CT in Months 4 and 5 before the official announcement in Month 6. The unbalanced increase in the workload of radiology departments in the shortage of cardiothoracic radiologists with the simultaneous decrease in income initiated a vicious cycle that worsened the economic repercussions of the pandemic.

A Supporting System for Management of Patients with Inflammatory Bowel Disease during COVID-19 Outbreak: Iranian Experience-Study Protocol.

BACKGROUND The COVID-19 pandemic has affected the health care infrastructure dramatically, with abundant resources necessarily being redirected to COVID-19 patients and their care. Also, patients with chronic diseases like inflammatory bowel disease (IBD) may be affected in several ways during this pandemic. METHODS We used the Iranian registry of Crohn's and colitis (IRCC) infrastructure. We called and sent messages to follow-up and support the care of all registered patients. Besides, we prepared and distributed educational materials for these patients and physicians to reduce the risk of COVID-19 infection. We risk-stratified them and prepared outpatient clinics and hospitalization guidance for IBD patients. RESULTS Of 13165 Iranian patients with IBD, 51 have been diagnosed as having COVID-19. IBD patients made 1920 hotline calls. Among the patients with suspicious presentations, 14 COVID-19 infections were diagnosed. Additionally, 1782 patients with IBD from five provinces actively phone-called among whom 28 definite cases were diagnosed. CONCLUSION IBD patients' follow-up could help in diagnosing the affected IBD patients with COVID-19. Additionally, the performance of protective actions and preparing the patients and physicians for decisive proceedings are the principles of protection of IBD patients.

Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia.

BACKGROUND: Chest computed tomography (CT) scan is frequently used in the diagnosis of COVID-19 pneumonia. OBJECTIVES: This study investigates the predictive value of CT severity score (CSS) for length-of-stay (LOS) in hospital, initial disease severity, ICU admission, intubation, and mortality. METHODS: In this retrospective study, initial CT scans of consecutively admitted patients with COVID-19 pneumonia were reviewed in a tertiary hospital. The association of CSS with the severity of disease upon admission and the final adverse outcomes was assessed using Pearson's correlation test and logistic regression, respectively. RESULTS: Total of 121 patients (60±16 years), including 54 women and 67 men, with positive RT-PCR tests were enrolled. We found a significant but weak correlation between CSS and qSOFA, as a measure of disease severity (r: 0.261, p = 0.003). No significant association was demonstrated between CSS and LOS. Patients with CSS>8 had at least three-fold higher risk of ICU admission, intubation, and mortality. CONCLUSIONS: CSS in baseline CT scan of patients with COVID-19 pneumonia can predict adverse outcomes and is weakly correlated with initial disease severity.