Background: We studied the clinical characteristics and outcomes of 905 hospitalized coronavirus disease 2019 (COVID-19) patients admitted to Imam Khomeini Hospital Complex (IKHC), Tehran, Iran. Methods: COVID-19 patients were recruited based on clinical symptoms and patterns of computed tomography (CT) imaging between February 20 and March 19. All patients were tested for the presence of COVID-19 RNA. The Poisson regression model estimated the incidence rate ratio (IRR) for different parameters. Results: The average age (± standard deviation) was 56.9 (±15.7) years and 61.77% were male. The most common symptoms were fever (93.59%), dry cough (79.78%), and dyspnea (75.69%). Only 43.76% of patients were positive for the RT-PCR COVID-19 test. Prevalence of lymphopenia was 42.9% and more than 90% had elevated lactate dehydrogenase (LDH) or C-reactive protein (CRP). About 11% were severe cases, and 13.7% died in the hospital. The median length of stay (LOS) was 3 days. We found higher risks of mortality in patients who were older than 70 years (IRR = 11.77, 95% CI 3.63–38.18), underwent mechanical ventilation (IRR = 7.36, 95% CI 5.06–10.7), were admitted to the intensive care unit (ICU) (IRR = 5.47, 95% CI 4.00–8.38), tested positive on the COVID-19 test (IRR = 2.80, 95% CI 1.64–3.55), and reported a history of comorbidity (IRR = 1.76, 95% CI 1.07–2.89) compared to their corresponding reference groups. Hydroxychloroquine therapy was not associated with mortality in our study. Conclusion: Older age, experiencing a severe form of the disease, and having a comorbidity were the most important prognostic factors for COVID-19 infection. Larger studies are needed to perform further subgroup analyses and verify high-risk groups.

Background The clinical course of COVID-19 may vary significantly. The presence of comorbidities prolongs the recovery time. The recovery in patients with mild-to-moderate symptoms might take 10 days, while in those with a critical illness or immunocompromised status could take 15 days. Considering the lack of data about predictors that could affect the recovery time, we conducted this study to identify them. Methods This cross-sectional study was implemented in the COVID-19 clinic of a teaching and referral university hospital in Tehran. Patients with the highly suggestive symptoms who had computed tomography (CT) imaging results with typical findings of COVID-19 or positive results of reverse transcriptase-polymerase chain reaction (RT-PCR) were enrolled in the study. Inpatient and outpatient COVID-19 participants were followed up by regular visits or phone calls, and the recovery period was recorded. Results A total of 478 patients were enrolled. The mean age of patients was 54.11 ± 5.65 years, and 44.2% were female. The median time to recovery was 13.5 days (IQR: 9). Although in the bivariate analysis, multiple factors, including hypertension, fever, diabetes mellitus, gender, and admission location, significantly contributed to prolonging the recovery period, in multivariate analysis, only dyspnea had a significant association with this variable (p = 0.02, the adjusted OR of 2.05; 95% CI 1.12–3.75). Conclusion This study supports that dyspnea is a predictor of recovery time. It seems like optimal management of the comorbidities plays the most crucial role in recovery from COVID-19.

Background Since the COVID-19 outbreak, pulmonary involvement was one of the most significant concerns in assessing patients. In the current study, we evaluated patient’s signs, symptoms, and laboratory data on the first visit to predict the severity of pulmonary involvement and their outcome regarding their initial findings. Methods All referred patients to the COVID-19 clinic of a tertiary referral university hospital were evaluated from April to August 2020. Four hundred seventy-eight COVID-19 patients with positive real-time reverse-transcriptase-polymerase chain reaction (RT-PCR) or highly suggestive symptoms with computed tomography (CT) imaging results with typical findings of COVID-19 were enrolled in the study. The clinical features, initial laboratory, CT findings, and short-term outcomes (ICU admission, mortality, length of hospitalization, and recovery time) were recorded. In addition, the severity of pulmonary involvement was assessed using a semi-quantitative scoring system (0–25). Results Among 478 participants in this study, 353 (73.6%) were admitted to the hospital, and 42 (8.7%) patients were admitted to the ICU. Myalgia (60.4%), fever (59.4%), and dyspnea (57.9%) were the most common symptoms of participants at the first visit. A review of chest CT scans showed that Ground Glass Opacity (GGO) (58.5%) and consolidation (20.7%) were the most patterns of lung lesions. Among initial clinical and laboratory findings, anosmia (P = 0.01), respiratory rate (RR) with a cut point of 25 (P = 0.001), C-reactive protein (CRP) with a cut point of 90 (P = 0.002), white Blood Cell (WBC) with a cut point of 10,000 (P = 0.009), and SpO2 with a cut point of 93 (P = 0.04) was associated with higher chest CT score. Lung involvement and consolidation lesions on chest CT scans were also associated with a more extended hospitalization and recovery period. Conclusions Initial assessment of COVID-19 patients, including symptoms, vital signs, and routine laboratory tests, can predict the severity of lung involvement and unfavorable outcomes.

<p>The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.</p><p>The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both</p><p>attention and attribution maps, resulting in a more interpretable model.</p>

BackgroundUltrasound imaging has been suggested for studying the structure and function of nerves and muscles; however, reliability studies are limited to support the usage. The main aim of this study was to explore the intrarater within-session reliability of evaluating the sciatic nerve and some related muscles morphology by ultrasound imaging.MethodsThree B-mode images from two scans (transverse and longitudinal) were acquired from the multifidus, biceps femoris, soleus and medial gastrocnemius muscles bilaterally from 15 participants with sciatica and 15 controls in one session, 1-h apart. The data were collected from March to July 2017. Contraction ratio was measured only by longitudinal scan, while the echo intensity was measured using maximum rectangular region of interest in two scans (transverse and longitudinal) for all muscles. Cross-sectional area, direct (tracing) and indirect (ellipsoid formula) methods were used to measure the sciatic nerve. Intraclass correlation coefficient (ICC 3,1), standard error of measurement and minimal detectable change were calculated.ResultsGood to high ICCs (0.80–0.96) were found for muscle contraction ratio in the longitudinal scans in all the muscles in both sciatica and control groups. For echo intensity measurements ICCs ranged from moderate to high, with higher ICCs seen with the maximum region of interest in the transverse scans. The minimal detectable change values ranged between 0.11 and 0.53 cm for contraction ratio.ConclusionsUltrasound imaging has high intrarater within-session reliability for assessing the sciatic nerve Cross-sectional area and muscle contraction ratios. Transverse scans with the maximum region of interest result in higher reliability. The sciatic Cross-sectional area is most accurately measured utilizing the direct tracing method rather than the indirect ellipsoid method.

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Background: Coronavirus disease 2019 (COVID-19) was initially detected in Wuhan city, China. Chest CT features of COVID-19 pneumonia have been investigated mostly in China, and there is very little information available on the radiological findings occurring in other populations. In this study, we aimed to describe the characteristics of chest CT findings in confirmed cases of COVID-19 pneumonia in an Iranian population, based on a time classification.Methods: Eighty-nine patients with COVID-19 pneumonia, confirmed by a real-time RT-PCR test, who were admitted to non-ICU wards and underwent a chest CT scan were retrospectively enrolled. Descriptive evaluation of radiologic findings was performed using a classification based on the time interval between the initiation of the symptoms and chest CT-scan.Results: The median age of patients was 58.0 years, and the median time interval from the onset of symptoms to CT scan evaluation was 7 days. Most patients had bilateral (94.4%) and multifocal (91.0%) lung involvement with peripheral distribution (60.7%). Also, most patients showed involvement of all five lobes (77.5%). Ground-glass opacities (GGO) (84.3%), and mixed GGO with consolidation (80.9%) were the most common identified patterns. We also found that as the time interval between symptoms and CT scan evaluation increased, the predominant pattern changed from GGO to mixed pattern and then to elongated-containing and band-like-opacities-containing pattern; on the other hand, the percentage of lung involvement increased.Conclusions: Bilateral multifocal GGO, and mixed GGO with consolidation were the most common patterns of COVID-19 pneumonia in our study. However, these patterns might change according to the time interval from symptoms.

Background:Little is known about the neuromuscular morphometric characteristics in patients with sciatica.Objective: To evaluate the possible changes of nerve and muscle structures in patients with low back pain with unilateral radiculopathy due to lumbar disc herniation by ultrasound imaging. Design:A case-control observational study.Methods: Forty individuals were divided into case (n=20; low back pain with unilateral radiculopathy due to disc herniation), and healthy control groups (n=20). The thickness of lumbar multifidus at L5 level, and of lower limb muscles (i.e., biceps femoris, medial gastrocnemius, and soleus) was measured during both rest and full contraction to calculate the rest/contraction ratio of these muscles. Additionally, the sciatic nerve cross-sectional area and the echogenicity of the nerve and muscles were measured based on ultrasound imaging.The association between severity of low back pain radiculopathy (i.e., pain and patients' perceived disability) and rest/contraction ratio was assessed.Results: Patients with sciatica showed sciatic nerve enlargement, and different contraction ratios for multifidus (at L5) / ankle plantar flexors compared to the controls. The rest/contraction ratio for biceps femoris was similar between the two groups. Conclusion:According to these findings, ultrasound imaging can be considered a useful tool to detect changes in the sciatic nerve and muscles due to disc herniation. Furthermore, regarding the observation of significant changes in muscle rest/contraction ratio in the multifidus and gastrosoleus, one might attribute these changes to the nerve root compression.