Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols. Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12-14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine-based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development. Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case. Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

Clinical significance of serum transforming growth factor-ß1 and procollagen type I N-propeptide in post-tuberculosis tracheobronchial stenosis.

Non-invasive strategies for monitoring post-tuberculosis (TB) tracheobronchial stenosis (PTTS) are clinically important but currently lacking. Transforming growth factor-ß1 (TGF-ß1) and procollagen type I N-propeptide (PINP) have been identified as markers of fibrosis. The present study aimed to investigate the clinical significance of serum TGF-ß1 and PINP in PTTS. Serum samples were collected from 119 patients with tracheobronchial TB after the condition was treated for at least 6 months (59 patients with airway stenosis and 60 patients with no stenosis). Serum TGF-ß1 and PINP levels were measured using ELISA and compared between the groups. Relationships between serum TGF-ß1 and PINP levels and clinical characteristics, interventional bronchoscopy and outcomes of airway stenosis were analysed. The correlation between TGF-ß1 and PINP, and their diagnostic efficacy for airway stenosis were also analysed. The TGF-ß1 and PINP levels in the airway stenosis group were higher than those in the non-stenosis group. Furthermore, airway stenosis with atelectasis or mucus plugging was associated with higher TGF-ß1 levels, and airway stenosis with atelectasis, mucus plugging, right main bronchus stenosis or severe airway tracheal stenosis was associated with higher PINP levels. In addition, TGF-ß1 and PINP levels increased after interventional bronchoscopy therapy and airway stenosis with recurrent stenosis was associated with higher baseline levels of both markers. Finally, TGF-ß1 levels were positively correlated with PINP levels in patients with airway stenosis. The area under the receiver operating characteristic curve of TGF-ß1 and PINP for distinguishing airway stenosis from non-stenosis cases was 0.824 (95% CI: 0.748-0.900) and 0.863 (95% CI: 0.796-0.930), respectively. Therefore, TGF-ß1 and PINP are potential biomarkers that may be useful for diagnosing and monitoring PTTS.

Comparison of clinical manifestations, pre-existing comorbidities, complications and treatment modalities in severe and non-severe COVID-19 patients: A systemic review and meta-analysis.

The global pandemic of novel coronavirus disease 2019 (COVID-19) has become an emergency of major international concern. We aim to assess the prevalence of clinical manifestations, pre-existing comorbidities, complications and treatment modalities in COVID-19 patients and compare incidence of these clinical data of severe patients with non-severe patients. An electronic search was performed in four databases to identify studies reporting clinical data of severe and non-severe COVID-19 patients. We calculated the odds ratio (OR) using fixed or random effect model. The analysis included 41 studies with 16,495 patients. The most prevalent clinical manifestations were fever 78.1%, cough 64.6%, fatigue 40.8%, and dyspnea 38.6%. Dyspnea (OR: 4.20, 95% CI: 3.09-5.72), cough (OR: 1.45, 95% CI: 1.18-1.78), and fatigue (OR: 1.40, 95% CI: 1.14-1.72) were found to be statistically significant higher in severe COVID-19 patients. We found that the most prevalent comorbidities were hypertension 32.2%, diabetes 17.1%, and cardiovascular disease 15.3%. Compared with non-severe group, proportion of hypertension (OR: 1.98, 95% CI: 1.62-2.42), diabetes (OR: 2.04, 95% CI: 1.67-2.50), cardiovascular disease (OR: 2.78, 95% CI: 2.00-3.86), and cancer (OR: 1.75, 95% CI: 1.40-2.18) were statistically significant higher in severe group. 24.7% patients presented with ARDS. The pooled effect of ARDS in severe and non-severe cases was 42.69 (OR: 42.69, 95% CI: 21.62-84.31). There was significant higher incidence of antiviral drugs, antibiotics, and glucocorticoids use in severe patients. Compared with non-severe patients, symptoms such as fever, cough, dyspnea, existing comorbidities, and complications are prevalent in severe COVID-19 patients.

Clinical characteristics and potential factors for recurrence of positive SARS-CoV-2 RNA in convalescent patients: a retrospective cohort study.

BACKGROUND: The recurrence of positive SARS-CoV-2 RT-PCR is frequently found in discharged COVID-19 patients but its clinical significance remains unclear. The potential cause, clinical characteristics and infectiousness of the recurrent positive RT-PCR patients need to be answered. METHODS: A single-centered, retrospective study of 51 discharged COVID-19 patients was carried out at a designated hospital for COVID-19. The demographic data, clinical records and laboratory findings of 25 patients with recurrent positive RT-PCR from hospitalization to follow-up were collected and compared to 26 patients with negative RT-PCR discharged regularly during the same period. Discharged patients' family members and close contacts were also interviewed by telephone to evaluate patients' potential infectiousness. RESULTS: The titer of both IgG and IgM antibodies was significantly lower (p = 0.027, p = 0.011) in patients with recurrent positive RT-PCR. Median duration of viral shedding significantly prolonged in patients with recurrent positive RT-PCR (36.0 days vs 9.0 days, p = 0.000). There was no significant difference in demographic features, clinical features, lymphocyte subsets count and inflammatory cytokines levels between the two groups of patients. No fatal case was noted in two groups. As of the last day of follow-up, none of the discharged patients' family members or close contact developed any symptoms of COVID-19. CONCLUSIONS: Patients with low levels of IgG and IgM are more likely to have recurrent positive SARS-CoV-2 RT-PCR results and lead to a prolonged viral shedding. The recurrent positive of SARS-CoV-2 RT-PCR may not indicate the recurrence or aggravation of COVID-19. The detection of SARS-CoV-2 by RT-PCR in the patients recovered from COVID-19 is not necessarily correlated with the ability of transmission.

Clinical features, comorbidities, complications and treatment options in severe and non-severe COVID-19 patients: A systemic review and meta-analysis.

OBJECTIVES: The aim of this analysis was to assess the prevalence of clinical features, comorbidities, complications and treatment options in the patients with COVID-19 and compare incidence of these clinical data in severe and non-severe patients. DESIGN: Systemic review and Meta-analysis. METHODS: PubMed, Embase, Scopus and Web of Sciences databases were searched to identify relevant papers until 20 July 2020. All studies comparing clinical data of severe and non-severe patients of COVID-19 were included. Heterogeneity across included studies was determined using Cochrane's Q test and the I2 statistic. Results were expressed as odds ratio with accompanying 95% confidence intervals. RESULTS: Twelve studies with 3,046 patients were included. The result showed the most prevalent clinical symptoms were fever 88.3%, cough 62.2%, fatigue 39.5% and dyspnoea 31.5%. Further meta-analysis showed incidence of fever, cough, fatigue and dyspnoea was higher in severe patients. The most prevalent comorbidities were hypertension 22.6%, diabetes 11.5%, cardiovascular disease 10.3% and cancer 2.5%. We found that compared with non-severe patients, the symptoms, existing comorbidities and complications are prevalent in severe COVID-19 patients. Future well-methodologically designed studies from other populations are strongly recommended.

Application value of SARS-CoV-2 IgG and IgM antibodies in diagnosis and treatment of new coronavirus pneumonia

Objective: To explore the application value of chemiluminescence detection of SARS-CoV-2 IgM/IgG antibodies in the adjuvant diagnosis and treatment of coronavirus disease 2019 (COVID-19).

High incidence and mortality of pneumothorax in critically Ill patients with COVID-19.

BACKGROUND: The clinical characteristics of the patients with COVID-19 complicated by pneumothorax have not been clarified. OBJECTIVES: To determine the epidemiology and risks of pneumothorax in the critically ill patients with COVID-19. METHODS: Retrospectively collecting and analysing medical records, laboratory findings, chest X-ray and CT images of 5 patients complicated by pneumothorax. RESULTS: The incidence of pneumothorax was 10% (5/49) in patients with ARDS, 24% (5/21) in patients receiving mechanical ventilation, and 56% (5/9) in patients requiring invasive mechanical ventilation, with 80% (4/5) patients died. All the 5 patients were male and aged ranging from 54 to 79 years old. Pneumothorax was most likely to occur 2 weeks after the beginning of dyspnea and associated with reduction of neuromuscular blockers, recruitment maneuver, severe cough, changes of lung structure and function. CONCLUSIONS: Pneumothorax is a frequent and fatal complication of critically ill patients with COVID-19.

Correlation between the variables collected at admission and progression to severe cases during hospitalization among patients with COVID-19 in Chongqing.

Mortality is high among severe patients with 2019 novel coronavirus-infected disease (COVID-19). Early prediction of progression to severe cases is needed. We retrospectively collected patients with COVID-19 in two hospital of Chongqing from 1st January to 29th February 2020. At admission, we collected the demographics and laboratory tests to predict whether the patient would progress to severe cases in hospitalization. Severe case was confirmed when one of the following criteria occurred: (a) dyspnea, respiratory rate ≥30 breaths/min, (b) blood oxygen saturation ≤93%, and (c) PaO2 /FiO2 ≤ 300 mm Hg. At admission, 348 mild cases were enrolled in this study. Of them, 20 (5.7%) patients progressed to severe cases after median 4.0 days (interquartile range: 2.3-6.0). Pulmonary inflammation index, platelet counts, sodium, C-reactive protein, prealbumin, and PaCO2 showed good distinguishing power to predict progression to severe cases (each area under the curve of receiver operating characteristics [AUC] ≥ 0.8). Age, heart rate, chlorine, alanine aminotransferase, aspartate aminotransferase, procalcitonin, creatine kinase, pH, CD3 counts, and CD4 counts showed moderate distinguishing power (each AUC between 0.7-0.8). And potassium, creatinine, temperature, and D-dimer showed mild distinguishing power (each AUC between 0.6-0.7). In addition, higher C-reactive protein was associated with shorter time to progress to severe cases (r = -0.62). Several easily obtained variables at admission are associated with progression to severe cases during hospitalization. These variables provide a reference for the medical staffs when they manage the patients with COVID-19.