Effect of combination antiviral therapy on hematological profiles in 151 adults hospitalized with severe coronavirus disease 2019.

OBJECTIVES: The current diagnosis and medicines approach in coronavirus disease 2019 (COVID-19) does not reflect the heterogeneous characteristics of this disease. This study aims to find a new antiviral combination regimen by investigating the frequency of clinically relevant and objectively identified comorbidities, and the clustering of these clinical syndromes and varying results of treatment with antiviral drugs in patients hospitalized with severe COVID-19. METHODS: This study recruited 151 severe COVID-19 infection cases diagnosed in our hospital examination and illustrated the clinical potential during a consecutive 25-day medication period. Potential differences in disease severity and clinical characteristics, hematological profile, and current pharmacologic treatments (single agent, double or triple combinations, and the combined antiviral drugs plus Lianhua Qingwen) among comorbidity clusters were explored. RESULTS: Although disease severity was comparable among three clusters, it was markedly different in terms of laboratory test status. Coagulable abnormality was mainly present in cluster 1 and cluster 2. Other indicators were normal, except for a significant increase of neutrophils presented in cluster 2. Patients showed the most complicated haematological results in cluster 3, including severe coagulation abnormalities, leukocytosis, neutrophilic granulocytosis, and lymphopenia. Our results for the first time suggest that a quadruple combination therapy (Ribavirin, Lopinavir/ritonavir, Umifenovir, and Lianhua Qingwen) can be considered as a preferred treatment approach to severe COVID-19 patients. After treatment, abnormal coagulation and leukocyte had markedly improved with a better prognosis. CONCLUSION: This study expands the understanding of the co-occurrence of combination therapy in patients with COVID-19, which provides the probability of developing novel combined therapy. Furthermore, explore clinical trials of variable antivirus treatments based on subgroup analyses or on using subgroups in the selection criteria would be the next step.

Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis.

BACKGROUND: To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19. METHODS: A cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories. RESULTS: We characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7-4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2-12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5-19.8). The lymphocytes continuously dropped to 0.75 × 109/L after one day post-onset before slowly recovering. (4) Group 4 (15.4%), the peak lesion percentage reached 41.4% (IQR 34.8-47.9), much higher than other groups. Lymphopenia was aggravated until the lymphocytes declined to 0.80 × 109/L on the fourth day and slowly recovered later. Patients in the higher order groups were older and more likely to have hypertension and diabetes (all P values < 0.05), and have more severe disease. CONCLUSIONS: Our findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.

Role of Prealbumin in Predicting the Prognosis of Severely and Critically Ill COVID-19 Patients.

Most critically ill patients experience malnutrition, resulting in a poor prognosis. This study aimed to evaluate the association of prealbumin (PAB) with the prognosis for severely and critically ill coronavirus disease 2019 (COVID-19) patients and explore factors related to this association. Patients with laboratory-confirmed COVID-19 from West Campus of Union Hospital in Wuhan from January 29, 2020 to March 31, 2020 were enrolled in this study. Patients were classified into the PAB1 (150-400 mg/L; N = 183) and PAB2 (< 150 mg/L; N = 225) groups. Data collection was performed using the hospital's electronic medical records system. The predictive value of PAB was evaluated by measuring the area under the receiver-operating characteristic (AUROC) curve. Patients were defined as severely or critically ill based on the Guidance for COVID-19 (7th edition) by the National Health Commission of China. During this analysis, 316 patients had severe cases and 65 had critical cases. A reduced PAB level was associated with a higher risk of mortality and a longer hospital stay. The AUROC curve for the prognosis based on the PAB level was 0.93, with sensitivity of 97.2% and specificity of 77.6%. For severe cases, a lower level of PAB was associated with a higher risk of malnutrition, higher NK cell counts, and lower B lymphocyte counts; these factors were not significant in critical cases. C-reactive protein and nutritional status mediated the association between PAB and prognosis. This retrospective analysis suggests that the PAB level on admission is an indicator of the prognosis for COVID-19.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

The effects of narrative exposure therapy on COVID-19 patients with post-traumatic stress symptoms: A randomized controlled trial.

BACKGROUND: With the global attack of Coronavirus Disease 2019 (COVID-19), cases with Post-traumatic Stress Disorder (PTSD) have been increasing steadily, which seriously affects the quality of life of patients and as such, seeking effective treatments is an urgent matter. Narrative Exposure Therapy (NET) is a typical cognitive behavioral therapy targeting trauma-related psychological disorders and may be an effective intervention. METHODS: A total of 111 COVID-19 patients near the discharge stage with positive screening results for posttraumatic stress symptoms (PTSS) were randomly assigned (1:1) to either the study group or the control group. The study group received NET and personalized psychological intervention, while the control group only received personalized psychological intervention. PTSS, depression, anxiety and sleep quality were measured pre- and post-intervention to evaluate the effect of NET. This trial was registered with the International Standard Randomized Clinical Trial Registry (No. ChiCTR2000039369). RESULTS: NET participants showed a significantly greater PTSS reduction in comparison with the control group after the intervention. Improvement in sleep quality, anxiety and depression after the intervention were pronounced but not significantly different between the two treatment groups. LIMITATIONS: The assessors weren't blinded for the convenience of measurement and protection of participants' psychological security. CONCLUSIONS: NET likely had a positive impact on PTSS of COVID-19 patients. Clinical staff should consider applying NET to improve the psychological well-being of patients who have experienced an epidemic such as COVID-19.

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images.

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to refine the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9670, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

Early cardiac involvement in patients with acute COVID-19 infection identified by multiparametric cardiovascular magnetic resonance imaging.

AIMS: In order to determine acute cardiac involvement in patients with COVID-19, we quantitatively evaluated tissue characteristics and mechanics by non-invasive cardiac magnetic resonance (CMR) in a cohort of patients within the first 10 days of the onset of COVID symptoms. METHODS AND RESULTS: Twenty-five patients with reverse transcription polymerase chain reaction confirmed COVID-19 and at least one marker of cardiac involvement [cardiac symptoms, abnormal electrocardiograph (ECG), or abnormal cardiac biomarkers] and 25 healthy age- and gender-matched control subjects were recruited to the study. Patients were divided into those with elevated (n = 8) or normal TnI (n = 17). There were significant differences in global longitudinal strain among patients who were positive and negative for hs-TnI, and controls [-12.3 (-13.3, -11.5)%, -13.1 (-14.2, -9.8)%, and -15.7 (-18.3, -12.7)%, P = 0.004]. Native myocardial T1 relaxation times in patients with positive and negative hs-TnI manifestation (1169.8 ± 12.9 and 1113.2 ± 31.2 ms) were significantly higher than the normal (1065 ± 57 ms) subjects, respectively (P < 0.001). The extracellular volume (ECV) of patients who were positive and negative for hs-TnI was higher than that of the normal controls [32 (31, 33)%, 29 (27, 30)%, and 26 (24, 27.5)%, P < 0.001]. In our study, quantitative T2 mapping in patients who were positive and negative for hs-TnI [51 (47.9, 52.8) and 48 (47, 49.4) ms] was significantly higher than the normal [42 (41, 45.2) ms] subjects (P < 0.001). CONCLUSION: In patients with early-stage COVID-19, myocardial oedema, and functional abnormalities are a frequent finding, while irreversible regional injury such as necrosis may be infrequent.

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). CONCLUSIONS: Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

Gender-associated difference following COVID-19 virus infection: Implications for thymosin alpha-1 therapy.

Gender influences clinical presentations, duration and severity of symptoms, and therapy outcome in coronavirus disease 2019 (COVID-19) infection. Whether the immune response to Tα1 treatment for SARS-CoV-2 differs between the sexes, and whether this difference explains the male susceptibility to COVID-19, is unclear. This study aimed to investigate the efficiency and safety of Tα1 treatment and provide a basis for practically identifying gender differences characteristics and features of COVID-19. One hundred twenty-seven patients had COVID-19 symptoms and tested COVID19-positive (female 42.52%) in Wuhan union hospital were enrolled for medication. They were randomly divided into groups Control and Tα1 intervention. Seventy-eight patients received a subcutaneous injection of 1.6 mg Tα1, based on supportive treatment for 15 days. The control group included untreated 49 COVID19 patients closely matched for gender and age and received regular supportive treatment. In this retrospective analysis, we found that COVID-19-infected males reported more symptoms than COVID-19-infected females. A high degree of gender differences-related variability was observed in CRP and PCT levels and the cell counts of many lymphocyte subpopulations in the COVID-19 patients after Tα1 intervention. Levels of CRP and IL-6 were higher in Tα1-treated male group than Tα1-treated female group, while the level of PCT was significantly lower in Tα1-treated male group. Gender differences may be a factor in sustaining COVID-19 immunity responded to Tα1, male and female show statistically significant differences in relevance to cytokine production associated with the development of a more significant number of symptoms. This leaves the question of identifying gender-specific risk factors to explain these differences.

Evaluation of Nutrition Risk and Its Association With Mortality Risk in Severely and Critically Ill COVID-19 Patients.

BACKGROUND: The nutrition status of coronavirus disease 2019 patients is unknown. This study evaluates clinical and nutrition characteristics of severely and critically ill patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and investigates the relationship between nutrition risk and clinical outcomes. METHODS: A retrospective, observational study was conducted at West Campus of Union Hospital in Wuhan. Patients confirmed with SARS-CoV-2 infection by a nucleic acid-positive test and identified as severely or critically ill were enrolled in this study. Clinical data and outcomes information were collected and nutrition risk was assessed using Nutritional Risk Screening 2002 (NRS). RESULTS: In total, 413 patients were enrolled in this study, including 346 severely and 67 critically ill patients. Most patients, especially critically ill patients, had significant changes in nutrition-related parameters and inflammatory markers. As for nutrition risk, the critically ill patients had significantly higher proportion of high NRS scores (P < .001), which were correlated with inflammatory and nutrition-related markers. Among 342 patients with NRS score ≥3, only 84 (of 342, 25%) received nutrition support. Critically ill patients and those with higher NRS score had a higher risk of mortality and longer stay in hospital. In logistic regression models, 1-unit increase in NRS score was associated with the risk of mortality increasing by 1.23 times (adjusted odds ratio, 2.23; 95% CI, 1.10-4.51; P = .026). CONCLUSIONS: Most severely and critically ill patients infected with SARS-CoV-2 are at nutrition risk. The patients with higher nutrition risk have worse outcome and require nutrition therapy.

Evaluation of antiviral therapies for coronavirus disease 2019 pneumonia in Shanghai, China.

The aim of our study was to evaluate the therapeutic effect of antiviral drugs on coronavirus disease 2019 (COVID-19) pneumonia. Patients confirmed with COVID-19 pneumonia were enrolled and divided into seven groups according to the treatment option. Information including age, sex, and duration from illness onset to admission, clinical manifestations, and laboratory data at admission, and length of hospital stay were evaluated. The chest computed tomography (CT) imaging obtained at admission and after a 5-day treatment cycle were assessed. The clinical symptoms and laboratory tests at discharge were also assessed. At admission, no significant differences were found among the groups, including the duration from illness onset to admission, clinical symptoms, and main laboratory results. No significant differences were found among the groups in terms of the proportion of patients with pneumonia resolution (P = .151) after treatment or the length of hospital stay (P = .116). At discharge, 7 of 184 (4%) patients had a mild cough while their other symptoms had disappeared, and the proportion of patients with abnormal liver function and with increased leukocytes, neutrophils or erythrocyte sedimentation rate among the 184 patients were close to those at admission. According to the results, the inclusion of antiviral drugs in therapeutic regimens based on symptomatic treatment had no significant additional impact on the improvement in COVID-19 patients. In addition, the results of chest CT imaging, clinical manifestations, and laboratory tests at discharge were not completely consistent.

Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.

OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS: The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% ± 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% ± 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS: A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

Preliminary investigation of relationship between clinical indicators and CT manifestation patterns of COVID-19 pneumonia improvement.

BACKGROUND: To retrospectively evaluate several clinical indicators related to the improvement of COVID-19 pneumonia on CT. METHODS: A total of 62 patients with COVID-19 pneumonia were included. The CT scores based on lesion patterns and distributions in serial CT were investigated. The improvement and deterioration of pneumonia was assessed based on the changes of CT scores. Grouped by using the temperature, serum lymphocytes and high sensitivity CRP (hs-CRP) on admission respectively, the CT scores on admission, at peak time and at discharge were evaluated. Correlation analysis was carried out between the time to onset of pneumonia resolution on CT images and the recovery time of temperature, negative conversion of viral nucleic acid, serum lymphocytes and hs-CRP. RESULTS: The CT scores of the fever group and lymphopenia group were significantly higher than those of normal group on admission, at peak time and at discharge; and the CT scores of normal hs-CRP group were significantly lower than those of the elevated hs-CRP group at peak time and at discharge (P all<0.05). The time to onset of pneumonia resolution on CT image was moderately correlated with negative conversion duration of viral nucleic acid (r =0.501, P<0.05) and the recovery time of hs-CPR (r =0.496, P<0.05). CONCLUSIONS: COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge. The negative conversion duration of viral nucleic acid and the recovery time of hs-CPR may be the indicator of the pneumonia resolution.

CD45: a critical regulator in immune cells to predict severe and non-severe COVID-19 patients.

The ongoing outbreak of COVID-19 has been announced by the World Health Organization as a worldwide public health emergency. The aim of this study was to distinguish between severe and non-severe patients in early diagnosis. The results showed that the mortality of COVID-19 patients increased accompanied by age. Host factors CRP, IL-1ß, hs-CRP, IL-8, and IL-6 levels in severe pneumonia patients were higher than in non-severe patients. CD3, CD8, and CD45 counts were decreased in COVID-19 patients. The results of this study suggest that the K-values of CD45 might be useful in distinguishing between severe and non-severe cases. The cut-off value for CD45 was -94.33. The K-values for CD45 in non-severe case were above the cut-off values, indicating a 100% prediction success rate for severe and non-severe cases following SARS-CoV-2 infection. The results confirmed that immune system dysfunction is a potential cause of mortality following COVID-19 infection, particularly for the elderly. CD45 deficiency dysfunction the naïve and memory T lymphocytes which may affects the long-term effectiveness of COVID-19 vaccines. K-values of CD45 might be useful in distinguishing between severe and non-severe cases in the early infection. May be CD45 could increase the diagnostic sensitivity.

An unusual case of coronavirus disease 2019: Delayed chest CT evidence.

Asymptomatic patients and infected patients with normal chest CT imaging are considered carriers of SARS-CoV-2. Before a diagnosis of coronavirus disease 2019 (COVID-19) is made, these patients with negative chest CT findings may be ignored, causing the possibility of virus transmission. For patients with suspected infections, reliable epidemiological information and clinical symptoms, clinical management is necessary even when the chest CT is negative.

Epidemiology and clinical course of COVID-19 in Shanghai, China.

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission.

Clinical Potential of UTE-MRI for Assessing COVID-19: Patient- and Lesion-Based Comparative Analysis.

BACKGROUND: Chest computed tomography (CT) has shown tremendous clinical potential for screening, diagnosis, and surveillance of COVID-19. However, safety concerns are warranted due to repeated exposure of X-rays over a short period of time. Recent advances in MRI suggested that ultrashort echo time MRI (UTE-MRI) was valuable for pulmonary applications. PURPOSE: To evaluate the effectiveness of UTE-MRI for assessing COVID-19. STUDY TYPE: Prospective. POPULATION: In all, 23 patients with COVID-19 and with an average interval of 2.81 days between hospital admission and image examination. FIELD STRENGTH/SEQUENCE: 3T; Respiratory-gated three-dimensional radial UTE pulse sequence. ASSESSMENT: Image quality score. Patient- and lesion-based interobserver and intermethod agreement for identifying the representative image findings of COVID-19. STATISTICAL TESTS: Wilcoxon-rank sum test, Kendall's coefficient of concordance (Kendall's W), intraclass coefficients (ICCs), and weighted kappa statistics. RESULTS: There was no significant difference between the image quality of CT and UTE-MRI (CT vs. UTE-MRI: 4.3 ± 0.4 vs. 4.0 ± 0.5, P = 0.09). Moreover, both patient- and lesion-based interobserver agreement of CT and UTE-MRI for evaluating the image signs of COVID-19 were determined as excellent (ICC: 0.939-1.000, P < 0.05; Kendall's W: 0.894-1.000, P < 0.05.). In addition, the intermethod agreement of two image modalities for assessing the representative findings of COVID-19 including affected lobes, total severity score, ground glass opacities (GGO), consolidation, GGO with consolidation, the number of crazy paving pattern, and linear opacities, as well as pseudocavity were all determined as substantial or excellent (kappa: 0.649-1.000, P < 0.05; ICC: 0.913-1.000, P < 0.05). DATA CONCLUSION: Pulmonary MRI with UTE is valuable for assessing the representative image findings of COVID-19 with a high concordance to CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:397-406.

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.

Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19.

BACKGROUND: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. METHODS AND MATERIALS: In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. RESULTS: Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. CONCLUSION: The model was robust and effective in predicting the severe/critical COVID cases.