Clinical analysis of 132 cases COVID-19 from Wuhan.

Numerous cases of pneumonia from a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China during December 2019.We determined the correlations of patient parameters with disease severity in patients with COVID-19.A total of 132 patients from Wuhan Fourth Hospital who had COVID-19 from February 1 to February 29 in 2020 were retrospectively analyzed.Ninety patients had mild disease, 32 had severe disease, and 10 had critical disease. The severe/critical group was older (P < .05), had a higher proportion of males (P < .05), and had a greater mortality rate (0% vs 61.9%, P < .05). The main symptoms were fever (n = 112, 84.8%) and cough (n = 96, 72.7%). Patients were treated with antiviral agents (n = 94, 71.2%), antibiotics (n = 92, 69.7%), glucocorticoids (n = 46, 34.8%), intravenous immunoglobulin (n = 38, 27.3%), and/or traditional Chinese medicine (n = 40, 30.3%). Patients in the severe/critical group received mechanical ventilation (n = 22, 16.7%) or high-flow nasal can-nula oxygen therapy (n = 6, 4.5%). Chest computed tomography (CT) indicated bilateral pneumonia in all patients. Relative to the mild group, the severe/critical group had higher levels of leukocytes, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, B-type natriuretic peptide (BNP), liver enzymes, and myocardial enzymes (P < .05), and decreased levels of lymphocytes and blood oxygen partial pressure (P < .05).The main clinical symptoms of patients from Wuhan who had COVID-19 were fever and cough. Patients with severe/critical disease were more likely to be male and elderly. Disease severity correlated with increased leukocytes, CRP, PCT, BNP, D-dimer, liver enzymes, and myocardial enzymes, and with decreased lymphocytes and blood oxygen partial pressure.

Comparative Analysis of Clinical and CT Findings in Patients with SARS-CoV-2 Original Strain, Delta and Omicron Variants.

OBJECTIVES: To compare the clinical characteristics and chest CT findings of patients infected with Omicron and Delta variants and the original strain of COVID-19. METHODS: A total of 503 patients infected with the original strain (245 cases), Delta variant (90 cases), and Omicron variant (168 cases) were retrospectively analyzed. The differences in clinical severity and chest CT findings were analyzed. We also compared the infection severity of patients with different vaccination statuses and quantified pneumonia by a deep-learning approach. RESULTS: The rate of severe disease decreased significantly from the original strain to the Delta variant and Omicron variant (27% vs. 10% vs. 4.8%, p < 0.001). In the Omicron group, 44% (73/168) of CT scans were categorized as abnormal compared with 81% (73/90) in the Delta group and 96% (235/245, p < 0.05) in the original group. Trends of a gradual decrease in total CT score, lesion volume, and lesion CT value of AI evaluation were observed across the groups (p < 0.001 for all). Omicron patients who received the booster vaccine had less clinical severity (p = 0.015) and lower lung involvement rate than those without the booster vaccine (36% vs. 57%, p = 0.009). CONCLUSIONS: Compared with the original strain and Delta variant, the Omicron variant had less clinical severity and less lung injury on CT scans.

Longitudinal Assessment of Chest CT Findings and Pulmonary Function after COVID-19 Infection.

Background Information on pulmonary sequelae and pulmonary function 2 years after recovery from SARS-CoV-2 infection is lacking. Purpose To longitudinally assess changes in chest CT abnormalities and pulmonary function in individuals after SARS-CoV-2 infection. Materials and Methods In this prospective study, participants discharged from the hospital after SARS-CoV-2 infection from January 20 to March 10, 2020, were considered for enrollment. Participants without chest CT scans at admission or with complete resolution of lung abnormalities at discharge were excluded. Serial chest CT scans and pulmonary function test results were obtained 6 months (June 20 to August 31, 2020), 12 months (December 20, 2020, to February 3, 2021), and 2 years (November 16, 2021, to January 10, 2022) after symptom onset. The term interstitial lung abnormality (ILA) and two subcategories, fibrotic ILAs and nonfibrotic ILAs, were used to describe residual CT abnormalities on follow-up CT scans. Differences between groups were compared with the χ2 test, Fisher exact test, or independent samples t test. Results Overall, 144 participants (median age, 60 years [range, 27-80 years]; 79 men) were included. On 2-year follow-up CT scans, 39% of participants (56 of 144) had ILAs, including 23% (33 of 144) with fibrotic ILAs and 16% (23 of 144) with nonfibrotic ILAs. The remaining 88 of 144 participants (61%) showed complete radiologic resolution. Over 2 years, the incidence of ILAs gradually decreased (54%, 42%, and 39% of participants at 6 months, 12 months, and 2 years, respectively; P < .001). Respiratory symptoms (34% vs 15%, P = .007) and abnormal diffusing capacity of lung for carbon monoxide (43% vs 20%, P = .004) occurred more frequently in participants with ILAs than in those with complete radiologic resolution. Conclusion More than one-third of participants had persistent interstitial lung abnormalities 2 years after COVID-19 infection, which were associated with respiratory symptoms and decreased diffusion pulmonary function. Chinese Clinical Trial Registry no. ChiCTR2000038609 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by van Beek in this issue.

Quantitative inspiratory-expiratory chest CT findings in COVID-19 survivors at the 6-month follow-up.

We evaluated pulmonary sequelae in COVID-19 survivors by quantitative inspiratory-expiratory chest CT (QCT) and explored abnormal pulmonary diffusion risk factors at the 6-month follow-up. This retrospective study enrolled 205 COVID-19 survivors with baseline CT data and QCT scans at 6-month follow-up. Patients without follow-up pulmonary function tests were excluded. All subjects were divided into group 1 (carbon monoxide diffusion capacity [DLCO] < 80% predicted, n = 88) and group 2 (DLCO ≥ 80% predicted, n = 117). Clinical characteristics and lung radiological changes were recorded. Semiquantitative total CT score (0-25) was calculated by adding five lobes scores (0-5) according to the range of lesion involvement (0: no involvement; 1: < 5%; 2: 5-25%; 3: 26-50%; 4: 51-75%; 5: > 75%). Data was analyzed by two-sample t-test, Spearman test, etc. 29% survivors showed air trapping by follow-up QCT. Semiquantitative CT score and QCT parameter of air trapping in group 1 were significantly greater than group 2 (p < 0.001). Decreased DLCO was negatively correlated with the follow-up CT score for ground-glass opacity (r = - 0.246, p = 0.003), reticulation (r = - 0.206, p = 0.002), air trapping (r = - 0.220, p = 0.002) and relative lung volume changes (r = - 0.265, p = 0.001). COVID-19 survivors with lung diffusion deficits at 6-month follow-up tended to develop air trapping, possibly due to small-airway impairment.

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important. METHOD: In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data. RESULTS: The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy. CONCLUSION: Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

Follow-up study of pulmonary sequelae in discharged COVID-19 patients with diabetes or secondary hyperglycemia.

PURPOSE: To determine chest CT changes 6 months and 12 months after the onset of coronavirus disease 2019 (COVID-19) in patients with diabetes or hyperglycemia and the risk factors for these residual lung abnormalities. METHODS: In total, 141 COVID-19 patients were assigned to group 1 (diabetes), group 2 (secondary hyperglycemia) or group 3 (controls). Initial and six- and twelve-month follow-up computed tomography (CT) scans were performed 16 days, 175 days and 351 days after symptom onset, respectively. CT findings and clinical and peak laboratory parameters were collected and compared. Univariable and multivariable logistic regression analyses were performed to identify the independent predictors for the presence of residual lung abnormalities at the 6-month follow-up exam. Seven variables (age; the presence of acute respiratory distress syndrome; the duration of hospitalization; the peak levels of lactate dehydrogenase (LDH) and C-reactive protein; and the initial total CT score) were chosen in the final multivariable models. RESULTS: At the six-month follow-up, abnormalities were still observed on chest CT in 77/141 (54.6%) patients. Reticular patterns (40/141, 28.4%) and ground-glass opacities (GGOs) (29/141, 20.6%) were the most common CT abnormalities on the follow-up CT scans. Patients in Groups 1 and 2 had significantly higher incidences of residual lung abnormalities than those in Group 3 (65.4% and 58.3%, respectively vs. 36.6%; p < 0.05). Twelve months after disease onset, the chest CT changes persisted in 13/25 (52.0%) patients. A duration of hospitalization > 20 days (OR: 5.630, 95% CI: 1.394-22.744, p = 0.015), an LDH level ≥ 317 U/L (OR: 7.020, 95% CI: 1.032-47.743, p = 0.046) and a total CT score > 15 (OR: 9.919, 95% CI: 1.378-71.415, p = 0.023) were independent predictors of residual pulmonary abnormalities in patients with diabetes or secondary hyperglycemia. CONCLUSIONS: A considerable proportion of surviving COVID-19 patients with diabetes or secondary hyperglycemia had residual pulmonary abnormalities six months after disease onset, and we found evidence of persistent chest CT changes at the one-year follow-up. Residual lung abnormalities were associated with longer hospital stays, higher peak LDH levels and higher initial total CT scores.

Differential Psychological Factors Associated With Unnecessary Dental Avoidance and Attendance Behavior During the Early COVID-19 Epidemic.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 is challenging the dental community to an unprecedented degree. Knowledge of the increased risk of infection in dental settings has been disseminated to the public and guidelines have been formulated to assist dental attendance decision-making. However, dental attendance behaviors incompatible with treatment need is not uncommon in clinical settings. Important gaps remain in the knowledge about how psychological factors are affecting dental attendance behaviors during the COVID-19 epidemic. In this cross-sectional study, a questionnaire survey was performed during February and March 2020. A total of 342 and 294 dental patients who attended and avoided dental visits, respectively, were included. The participants were classified into four groups based on dental attendance behavior and emergent/urgent dental treatment need. Bivariate analysis was performed to investigate factors associated with dental attendance. Multivariable logistic regression based on principal component scores was performed to identify major psychological constructs associated with unnecessary dental avoidance and attendance. Among all the factors explored, inability to wear masks during dental treatment (P < 0.001; effect size: 0.32) was most closely associated with the overall pattern of dental attendance among participants. Multivariable regression suggested that unnecessary dental avoidance was associated with perceived risk of infection in general and in dental settings (odds ratio [95% CI]: 0.62 [0.53, 0.72]; p < 0.001), perceived impact of COVID-19 and dental problems on general health (0.79 [0.65, 0.97]; 0.021), and personal traits such as trust and anxiety (0.77 [0.61, 0.98]; 0.038). Unnecessary dental attendance was associated with optimism toward the epidemic (1.68 [1.42, 2.01]; <0.001) and trust (1.39 [1.13, 1.74]; 0.002). Multidisciplinary efforts involving dental and medical professionals as well as psychologists are warranted to promote more widespread adoption, among the general public, of dental attendance behaviors compatible with dental treatment need during the COVID-19 epidemic.

Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia.

Background Little is known about the long-term lung radiographic changes in patients who have recovered from coronavirus disease 2019 (COVID-19), especially those with severe disease. Purpose To prospectively assess pulmonary sequelae and explore the risk factors for fibrotic-like changes in the lung at 6-month follow-up chest CT of survivors of severe COVID-19 pneumonia. Materials and Methods A total of 114 patients (80 [70%] men; mean age, 54 years ± 12) were studied prospectively. Initial and follow-up CT scans were obtained a mean of 17 days ± 11 and 175 days ± 20, respectively, after symptom onset. Lung changes (opacification, consolidation, reticulation, and fibrotic-like changes) and CT extent scores (score per lobe, 0-5; maximum score, 25) were recorded. Participants were divided into two groups on the basis of their 6-month follow-up CT scan: those with CT evidence of fibrotic-like changes (traction bronchiectasis, parenchymal bands, and/or honeycombing) (group 1) and those without CT evidence of fibrotic-like changes (group 2). Between-group differences were assessed with the Fisher exact test, two-sample t test, or Mann-Whitney U test. Multiple logistic regression analyses were performed to identify the independent predictive factors of fibrotic-like changes. Results At follow-up CT, evidence of fibrotic-like changes was observed in 40 of the 114 participants (35%) (group 1), whereas the remaining 74 participants (65%) showed either complete radiologic resolution (43 of 114, 38%) or residual ground-glass opacification or interstitial thickening (31 of 114, 27%) (group 2). Multivariable analysis identified age of greater than 50 years (odds ratio [OR]: 8.5; 95% CI: 1.9, 38; P = .01), heart rate greater than 100 beats per minute at admission (OR: 5.6; 95% CI: 1.1, 29; P = .04), duration of hospital stay greater than or equal to 17 days (OR: 5.5; 95% CI: 1.5, 21; P = .01), acute respiratory distress syndrome (OR: 13; 95% CI: 3.3, 55; P < .001), noninvasive mechanical ventilation (OR: 6.3; 95% CI: 1.3, 30; P = .02), and total CT score of 18 or more (OR: 4.2; 95% CI: 1.2, 14; P = .02) at initial CT as independent predictors for fibrotic-like changes in the lung at 6 months. Conclusion Six-month follow-up CT showed fibrotic-like changes in the lung in more than one-third of patients who survived severe coronavirus disease 2019 pneumonia. These changes were associated with an older age, acute respiratory distress syndrome, longer hospital stays, tachycardia, noninvasive mechanical ventilation, and higher initial chest CT score. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wells et al in this issue.