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1.
Future Virol ; 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1526740

ABSTRACT

Aim: COVID-19 is a major threat to public health worldwide. A large proportion of COVID-19 patients is proved to develop anemia. Herein, we investigate the association between anemia and severe pneumonia. Materials & methods: 137 of COVID-19-confirmed patients admitted to Wuhan Union Hospital (Wuhan, China) from 13 February to 17 March 2020 were included. Their clinical characteristics and laboratory data were studied, and multivariable logistic regression analyses were performed. Results: The anemic patients were less likely to develop fever in the early stage of COVID-19. Elevated IL-6 levels were found in anemic COVID-19 patients compared with those without anemia. COVID-19 patients with anemia had an 8.2 times greater possibility of developing severe pneumonia compared with their counterparts without anemia. Conclusion: This study comprehensively describes the clinical characteristics of anemic patients with ordinary, severe and critical COVID-19 and demonstrates the close relationship between the anemia and severe COVID-19.

2.
Diagnostics (Basel) ; 11(10)2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1480621

ABSTRACT

OBJECTIVE: To provide the quantitative volumetric data of the total lung and lobes in inspiration and expiration from healthy adults, and to explore the value of paired inspiratory-expiratory chest CT scan in pulmonary ventilatory function and further explore the influence of each lobe on ventilation. METHODS: A total of 65 adults (29 males and 36 females) with normal clinical pulmonary function test (PFT) and paired inspiratory-expiratory chest CT scan were retrospectively enrolled. The inspiratory and expiratory volumetric indexes of the total lung (TL) and 5 lobes (left upper lobe [LUL], left lower lobe [LLL], right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]) were obtained by Philips IntelliSpace Portal image postprocessing workstation, including inspiratory lung volume (LVin), expiratory lung volume (LVex), volume change (∆LV), and well-aerated lung volume (WAL, lung tissue with CT threshold between -950 and -750 HU in inspiratory scan). Spearman correlation analysis was used to explore the correlation between CT quantitative indexes of the total lung and ventilatory function indexes (including total lung capacity [TLC], residual volume [RV], and force vital capacity [FVC]). Multiple stepwise regression analysis was used to explore the influence of each lobe on ventilation. RESULTS: At end-inspiratory phase, the LVin-TL was 4664.6 (4282.7, 5916.2) mL, the WALTL was 4173 (3639.6, 5250.9) mL; both showed excellent correlation with TLC (LVin-TL: r = 0.890, p < 0.001; WALTL: r = 0.879, p < 0.001). From multiple linear regression analysis with lobar CT indexes as variables, the LVin and WAL of these two lobes, LLL and RUL, showed a significant relationship with TLC. At end-expiratory phase, the LVex-TL was 2325.2 (1969.7, 2722.5) mL with good correlation with RV (r = 0.811, p < 0.001), of which the LVex of RUL and RML had a significant relationship with RV. For the volumetric change within breathing, the ∆LVTL was 2485.6 (2169.8, 3078.1) mL with good correlation with FVC (r = 0.719, p < 0.001), moreover, WALTL showed a better correlation with FVC (r = 0.817, p < 0.001) than that of ∆LVTL. Likewise, there was also a strong association between ∆LV, WAL of these two lobes (LLL and RUL), and FVC. CONCLUSIONS: The quantitative indexes derived from paired inspiratory-expiratory chest CT could reflect the clinical pulmonary ventilatory function, LLL, and RUL give greater impact on ventilation. Thus, the pulmonary functional evaluation needs to be more precise and not limited to the total lung level.

3.
Radiology ; : 211199, 2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1450622

ABSTRACT

Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after one year are not known. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital due to COVID-19 and underwent CT scans during hospitalization at two isolation centers between 27 January and 31 March 2020. In a prospective study, three serial chest CTs were obtained at approximately 3, 7, and 12 months after symptom onset and longitudinally analyzed. The total CT score of pulmonary lobe involvement from 0 to 25 was assessed (score 1-5 for each lobe). Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for residual CT abnormalities after one year. Results 209 study participants (mean age: 49±13 years, 116 women) were evaluated. At 3 months, 61% of participants (128 of 209) had resolution of CT abnormalities; at 12 months, 75% (156 of 209) had resolution. Of chest CT abnormalities that had not resolved, there were residual linear opacities in 25/209 (12%) and multifocal reticular/cystic lesions in 28/209 (13%) participants. Age≥50 years, lymphopenia, and severe/ARDS aggravation were independent risk factors for residual CT abnormalities at one year (odds ratios of 15.9, 18.9, and 43.9, respectively; P<.001, each). In 53 participants with residual CT abnormalities at 12 months, reticular lesions (41 of 53, 77%) and bronchial dilation (39 of 53, 74%) were observed at discharge and were persistent in 53% (28 of 53) and 45% (24 of 53) of participants, respectively. Conclusion One year after COVID-19 diagnosis, chest CT showed abnormal findings in 25% of participants, with 13% showing subpleural reticular/cystic lesions. Older participants with severe COVID-19 or acute respiratory distress syndrome were more likely to develop lung sequelae that persisted at 1 year. See also the editorial by Lee and Wi.

4.
Diagnostics (Basel) ; 11(10)2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1444131

ABSTRACT

BACKGROUND: In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge. METHODS: We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models. RESULTS: Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively. CONCLUSIONS: The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors.

6.
Transl Psychiatry ; 11(1): 307, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1237992

ABSTRACT

This study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.


Subject(s)
COVID-19 , White Matter , Anisotropy , Brain/diagnostic imaging , Humans , Mental Health , Metabolomics , SARS-CoV-2 , White Matter/diagnostic imaging
7.
Chin J Acad Radiol ; : 1-8, 2021 Feb 19.
Article in English | MEDLINE | ID: covidwho-1099019

ABSTRACT

Since the outbreak of the coronavirus disease 2019 (COVID-19), it had rapidly spread to the whole world and seriously threatened the global health. Imaging examination plays an important role in the clinical diagnosis of this disease, which leads to the high infection risk of the medical staff in the radiology department. In this review, the authors thoroughly summed up the experience in the management and operation of radiology department and shared their experience of the protective and control strategies and work plan during the epidemic, including but not limited to the management framework of the radiology department, the environment and layout in the department, the requirements for protection of different posts and the equipment, as well as the essential diagnosis of COVID-19. It is worth noting that the main goal of the radiology department in every country is to complete the radiology examination safely and make an accurate diagnosis of COVID-19 patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00055-5.

8.
Int J Med Sci ; 18(6): 1492-1501, 2021.
Article in English | MEDLINE | ID: covidwho-1089157

ABSTRACT

Objectives: As of 11 Feb 2020, a total of 1,716 medical staff infected with laboratory-confirmed the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) in China had been reported. The predominant cause of the infection among medical staff remains unclear. We sought to explore the epidemiological, clinical characteristics and prognosis of infected medical staff. Methods: Medical staff who infected with SARS-Cov-2 and admitted to Union Hospital, Wuhan between 16 Jan to 25 Feb, 2020 were included in this single-centered, retrospective study. Data were compared by occupation and analyzed with the Kaplan-Meier and Cox regression methods. Results: A total of 101 medical staff (32 males and 69 females; median age: 33) were included in this study and 74.3% were nurses. A small proportion of the cohort had contact with specimens (3%) as well as patients infected with SARS-Cov-2 in fever clinics (15%) and isolation wards (3%). 80% of medical staff showed abnormal IL-6 levels and 33% had lymphocytopenia. Chest CT mainly manifested as bilateral (62%), septal/subpleural (77%) and groundglass opacities (48%). The major differences between doctors and nurses manifested in laboratory indicators. As of the last observed date, no patient was transferred to intensive care unit or died. Fever (HR=0.57; 95% CI 0.36-0.90) and IL-6 levels greater than 2.9 pg/ml (HR=0.50; 95% CI 0.30-0.86) were unfavorable factors for discharge. Conclusions: Our findings suggested that the infection of medical staff mainly occurred at the early stages of SARS-CoV-2 epidemic in Wuhan, and only a small proportion of infection had an exact mode. Meanwhile, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course than ordinary patients, which may be partly due to their medical expertise, younger age and less underlying diseases. The potential risk factors of fever and IL-6 levels greater than 2.9 pg/ml could help to identify medical staff with poor prognosis at an early stage.


Subject(s)
COVID-19/epidemiology , Medical Staff/statistics & numerical data , SARS-CoV-2/pathogenicity , Adult , COVID-19/diagnostic imaging , China/epidemiology , Cohort Studies , Female , Fever/epidemiology , Hospitalization/statistics & numerical data , Humans , Male , Prognosis , Retrospective Studies , Risk Factors
9.
Int J Med Sci ; 18(5): 1277-1284, 2021.
Article in English | MEDLINE | ID: covidwho-1060234

ABSTRACT

Rationale: To assess the longitudinal changes and relationships of clinical measures and extent of CT lung abnormalities in COVID-19. Methods: 81 patients with COVID-19 were prospectively enrolled and followed until discharge. CT scores were quantified on a basis of a CT scoring system where each lung was divided into 3 zones: upper (above the carina), middle, and lower (below the inferior pulmonary vein) zones; each zone was evaluated for percentage of lung involvement on a scale of 0-4 (0, 0%; 1, 0-24%; 2, 25% - 49%; 3, 50% -74%; 4, >74%).Temporal trends of CT scores and the laboratory parameters characteristic of COVID-19 were analyzed. Correlations between the two were determined at three milestones (initial presentation, worst CT manifestation, and recovery finding before discharge). Their correlations with duration to worst CT manifestation and discharge from symptom onset were evaluated. Results: CT scores peaked during illness days 6-11 (median: 5), and stayed steady. C-reactive protein and lactate dehydrogenase increased, peaked on illness days 6-8 and 8-11 (mean: 23.5 mg/L, 259.9 U/L), and gradually declined. Continual decrease and increase were observed in hemoglobin and lymphocyte count, respectively. Albumin reduced and remained at low levels with a nadir on illness days 12-15 (36.6 g/L). Both initial (r = 0.58, 0.64, p < 0.05) and worst CT scores (r = 0.47, 0.65, p < 0.05) were correlated with C-reactive protein and lactate dehydrogenase; and CT scores before discharge, only with albumin (r = -0.41, p < 0.05). Duration to worst CT manifestation was associated with initial and worst CT scores (r = 0.33, 0.29, p < 0.05). No parameters were related to timespan to discharge. Conclusion: Our results illustrated the temporal changes of characteristic clinical measures and extent of CT lung abnormalities in COVID-19. CT scores correlated with some important laboratory parameters, and might serve as prognostic factors.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Adult , C-Reactive Protein/metabolism , COVID-19/blood , Female , Humans , L-Lactate Dehydrogenase/blood , Longitudinal Studies , Male , Middle Aged , Prospective Studies , Radiography, Thoracic , Tomography, X-Ray Computed
10.
Sci Rep ; 11(1): 417, 2021 01 11.
Article in English | MEDLINE | ID: covidwho-1019886

ABSTRACT

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Lung/diagnostic imaging , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2/isolation & purification , Tomography, X-Ray Computed/methods
11.
Acta Diabetol ; 58(5): 575-586, 2021 May.
Article in English | MEDLINE | ID: covidwho-1014138

ABSTRACT

AIMS: Increasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome. METHODS: A total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions. RESULTS: Patients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (ß = 0.401, p < 0.001), PGV (ß = 0.364, p = 0.001) and PCV (ß = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes. CONCLUSIONS: Increased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.


Subject(s)
Blood Glucose/analysis , COVID-19/diagnostic imaging , Diabetes Mellitus/epidemiology , Adult , Aged , Artificial Intelligence , COVID-19/epidemiology , Comorbidity , Female , Humans , Male , Middle Aged , Tomography, X-Ray Computed/methods
12.
Sci Rep ; 10(1): 21849, 2020 12 14.
Article in English | MEDLINE | ID: covidwho-977276

ABSTRACT

This study aimed to determine the characteristics of CT changes in patients with severe coronavirus disease 2019 (COVID-19) based on prognosis. Serial CT scans in 47 patients with severe COVID-19 were reviewed. The patterns, distribution and CT score of lung abnormalities were assessed. Scans were classified according to duration in weeks after onset of symptoms. These CT abnormalities were compared between discharged and dead patients. Twenty-six patients were discharged, whereas 21 passed away. Discharged patients were characterized by a rapid rise in CT score in the first 2 weeks followed by a slow decline, presence of reticular and mixed patterns from the second week, and prevalence of subpleural distribution of opacities in all weeks. In contrast, dead patients were characterized by a progressive rise in CT score, persistence of ground-glass opacity and consolidation patterns in all weeks, and prevalence of diffuse distribution from the second week. CT scores of death group were significantly higher than those of discharge group (P < 0.05). The CT changes differed between the discharged and dead patients. An understanding of these differences can be of clinical significance in the assessment of the prognosis of severe COVID-19 patients.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Aged, 80 and over , China , Disease Progression , Female , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Young Adult
13.
Sci Rep ; 10(1): 17543, 2020 10 16.
Article in English | MEDLINE | ID: covidwho-872736

ABSTRACT

The aim of this study was to assess the prognostic value of baseline clinical and high resolution CT (HRCT) findings in patients with severe COVID-19. In this retrospective, two-center study, we included two groups of inpatients with severe COVID-19 who had been discharged or died in Jin Yin-tan hospital and Wuhan union hospital between January 5, 2020, and February 22, 2020. Cases were confirmed by real-time polymerase chain reaction. Demographic, clinical, and laboratory data, and HRCT imaging were collected and compared between discharged and deceased patients. Univariable and multivariable logistic regression models were used to assess predictors of mortality risk in these patients. 101 patients were included in this study, of whom 66 were discharged and 35 died in the hospital. The mean age was 56.6 ± 15.1 years and 67 (66.3%) were men. Of the 101 patients, hypertension (38, 37.6%), cardiovascular disease (21,20.8%), diabetes (18,17.8%), and chronic pulmonary disease (16,15.8%) were the most common coexisting conditions. The multivariable regression analysis showed older age (OR: 1.142, 95% CI 1.059-1.231, p < 0.001), acute respiratory distress syndrome (ARDS) (OR: 10.142, 95% CI 1.611-63.853, p = 0.014), reduced lymphocyte count (OR: 0.004, 95% CI 0.001-0.306, p = 0.013), and elevated HRCT score (OR: 1.276, 95% CI 1.002-1.625, p = 0.049) to be independent predictors of mortality risk on admission in severe COVID-19 patients. These findings may have important clinical implications for decision-making based on risk stratification of severe COVID-19 patients.


Subject(s)
Coronavirus Infections/pathology , Pneumonia, Viral/pathology , Tomography, X-Ray Computed , Adult , Aged , Betacoronavirus/isolation & purification , COVID-19 , Comorbidity , Coronavirus Infections/mortality , Coronavirus Infections/virology , Female , Humans , Logistic Models , Lymphocyte Count , Male , Middle Aged , Odds Ratio , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Thorax/diagnostic imaging
14.
Nat Commun ; 11(1): 5088, 2020 10 09.
Article in English | MEDLINE | ID: covidwho-841267

ABSTRACT

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Deep Learning , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia/diagnostic imaging , ROC Curve , SARS-CoV-2 , Tomography, X-Ray Computed , Young Adult
16.
Front Psychiatry ; 11: 845, 2020.
Article in English | MEDLINE | ID: covidwho-760884

ABSTRACT

[This corrects the article DOI: 10.3389/fpsyt.2020.00459.].

17.
Hepatol Int ; 14(5): 733-742, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-743772

ABSTRACT

BACKGROUND AND AIMS: Liver injury is found in some of patients with COVID-19. Liver injury of COVID-19 patients based on severity grading and abdominal radiological signs have not been reported until now. The aim of our study is to determine clinical profiles of the patients based on severity grading, describe abdominal radiological signs, and investigate the correlations of the severity with clinical profiles and radiological signs. METHODS: This retrospective cohort study included 115 patients with COVID-19 from Jan 2020 to Feb 2020. Medical records of the patients were collected and CT images were reviewed. RESULTS: Common clinical manifestations of patients with COVID-19 were fever (68.70%), cough (56.52%), fatigue (31.30%); some of them had gastrointestinal symptoms (diarrhea, 12.17%; nausea or vomiting 7.83%; inappetence, 7.83%). Abnormal liver function was observed in some of patients with COVID-19. Significant differences in the levels of AST, albumin,CRP were observed among different groups classified by the severity. Common findings of upper abdominal CT scan were liver hypodensity (26.09%) and pericholecystic fat stranding (21.27%); liver hypodensity was more frequently found in critical cases (58.82%). The severity of COVID-19 correlated with semi-quantitative CT score of pulmonary lesions, CT-quantified liver/spleen attenuation ratio in patients with COVID-19. CONCLUSIONS: Some of the patients with COVID-19 displayed liver damage revealed by liver functional tests and upper abdominal CT imaging, and the severity of COVID-19 patients correlated with some of liver functional tests and CT signs; thus, it will allow an earlier identification of high-risk patients for early effective intervention.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections , Liver Diseases , Liver Function Tests/methods , Liver , Pandemics , Pneumonia, Viral , Tomography, X-Ray Computed/methods , COVID-19 , China/epidemiology , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/physiopathology , Female , Humans , Liver/diagnostic imaging , Liver/metabolism , Liver Diseases/diagnosis , Liver Diseases/epidemiology , Liver Diseases/etiology , Liver Diseases/physiopathology , Male , Middle Aged , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Pneumonia, Viral/physiopathology , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
18.
IEEE Trans Med Imaging ; 39(8): 2615-2625, 2020 08.
Article in English | MEDLINE | ID: covidwho-739128

ABSTRACT

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.


Subject(s)
Coronavirus Infections/diagnostic imaging , Image Interpretation, Computer-Assisted/methods , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Supervised Machine Learning , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Coronavirus Infections/pathology , Female , Humans , Lung/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology , Retrospective Studies , SARS-CoV-2 , Thorax/diagnostic imaging , Young Adult
19.
Clin Infect Dis ; 71(15): 723-731, 2020 07 28.
Article in English | MEDLINE | ID: covidwho-719209

ABSTRACT

BACKGROUND: Our objective was to retrospectively analyze the evolution of clinical features and thin-section computed tomography (CT) imaging of novel coronavirus disease 2019 (COVID-19) pneumonia in 17 discharged patients. METHODS: Serial thin-section CT scans of 17 discharged patients with COVID-19 were obtained during recovery. Longitudinal changes of clinical parameters and a CT pattern were documented in all patients during the 4 weeks after admission. A CT score was used to evaluate the extent of the disease. RESULTS: There were marked improvements of fever, lymphocyte counts, C-reactive proteins, and erythrocyte sedimentation rates within the first 2 weeks after admission. However, the mean CT score rapidly increased from the first to the third week, with a top score of 8.2 obtained in the second week. During the first week, the main CT pattern was ground-glass opacities (GGO; 76.5%). The frequency of GGO (52.9%) decreased in the second week. Consolidation and mixed patterns (47.0%) were noted in the second week. Thereafter, consolidations generally dissipated into GGO, and the frequency of GGO increased in the third week (76.5%) and fourth week (71.4%). Opacities were mainly located in the peripheral (76.5%) and subpleural (47.1%) zones of the lungs; they presented as focal (35.3%) or multifocal (29.4%) in the first week and became more diffuse in the second (47.1%) and third weeks (58.8%), then showed a reduced extent in fourth week (50%). CONCLUSIONS: The progression course of the CT pattern was later than the progression of the clinical parameters within the first 2 weeks after admission; however, there were synchronized improvements in both the clinical and radiologic features in the fourth week.


Subject(s)
Coronavirus Infections/pathology , Pneumonia, Viral/pathology , Pneumonia/pathology , Adult , Betacoronavirus/pathogenicity , COVID-19 , Coronavirus Infections/virology , Disease Progression , Female , Fever/pathology , Fever/virology , Hospitalization , Humans , Lung/pathology , Lung/virology , Male , Middle Aged , Pandemics , Patient Discharge , Pneumonia/virology , Pneumonia, Viral/virology , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
20.
Radiology ; 298(2): E63-E69, 2021 02.
Article in English | MEDLINE | ID: covidwho-690185

ABSTRACT

The World Health Organization (WHO) undertook the development of a rapid guide on the use of chest imaging in the diagnosis and management of coronavirus disease 2019 (COVID-19). The rapid guide was developed over 2 months by using standard WHO processes, except for the use of "rapid reviews" and online meetings of the panel. The evidence review was supplemented by a survey of stakeholders regarding their views on the acceptability, feasibility, impact on equity, and resource use of the relevant chest imaging modalities (chest radiography, chest CT, and lung US). The guideline development group had broad expertise and country representation. The rapid guide includes three diagnosis recommendations and four management recommendations. The recommendations cover patients with confirmed or who are suspected of having COVID-19 with different levels of disease severity, throughout the care pathway from outpatient facility or hospital entry to home discharge. All recommendations are conditional and are based on low certainty evidence (n = 2), very low certainty evidence (n = 2), or expert opinion (n = 3). The remarks accompanying the recommendations suggest which patients are likely to benefit from chest imaging and what factors should be considered when choosing the specific imaging modality. The guidance offers considerations about implementation, monitoring, and evaluation, and also identifies research needs. Published under a CC BY 4.0 license. Online supplemental material is available for this article.


Subject(s)
COVID-19/diagnosis , Lung/diagnostic imaging , Radiography/methods , Tomography, X-Ray Computed/methods , Ultrasonography/methods , World Health Organization , Humans , SARS-CoV-2
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