[Proposal of Living Evidence-based Guideline for Combination of Traditional Chinese and Western Medicine for Treatment of COVID-19].

In order to standardize the clinical diagnosis and treatment decision-making with traditional Chinese medicine for pa-tients of coronavirus disease 2019(COVID-19) and put the latest clinical study evidence into clinical practice, the international trust-worthy traditional Chinese medicine recommendations( TCM Recs) working group started the compilation of Living Evidence-based Guideline for Combination of Traditional Chinese and Western Medicine for Treatment of COVID-19 on the basis of the standards and re-quirements of WHO handbook, GRADE and RIGHT. This proposal mainly introduces the formulation methods and processes of the living guidelines in details, such as the composition of the working group, the collection and identification of clinical issues and out-comes, the production of the living systematic review and the consensus of recommendations. The guidelines will continue to monitor the clinical study evidences of TCM in the prevention and treatment of COVID-19, and conduct regular evidence updating, retrieval and screening. When there is new study evidence, the steering committee will evaluate the possibility of the evidence to change clinical practice or previous recommendations, so as to decide whether the recommendations for the guidelines shall be implemented or upda-ted. The main criteria considered in the guideline updating are as follows:(1) There are new high-quality randomized controlled trial(RCT) evidences for TCM uninvolved in the previous edition of the guidelines;(2) as for the TCM involved in the guidelines, living sys-tematic review shows that new evidence may change the direction or strength of the existing recommendations. The specific implementation of the living evidence-based guidelines will take this proposal as the study basis and framework, in order to ensure the standardization of the formulation process and methods. This will be the first exploration of the methodology for living guidelines in the field of TCM.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Clinico-Radiological Features and Outcomes in Pregnant Women with COVID-19 Pneumonia Compared with Age-Matched Non-Pregnant Women.

PURPOSE: To investigate the clinico-radiological findings and outcomes in pregnant women with COVID-19 pneumonia compared to age-matched non-pregnant women. METHODS: A retrospective case-controlled study was conducted to review clinical and CT data of 21 pregnant and 19 age-matched non-pregnant women with COVID-19 pneumonia. Four stages of CT images were analyzed and compared based on the time interval from symptom onset: stage 1 (0-6 days), stage 2 (7-9 days), stage 3 (10-16 days), and stage 4 (>16 days). The initial and follow-up data were analyzed and compared. RESULTS: Compared with age-matched non-pregnant women, initial absence of fever (13/21, 62%) and normal lymphocyte count (11/21, 52%) were more frequent in pregnant group. The predominant patterns of lung lesions were pure ground-glass opacity (GGO), GGO with consolidation or reticulation, and pure consolidation in both groups. Pure consolidation on chest CT was more common at presentation in pregnant cases. Pregnant women progressed with a higher consolidation frequency compared with non-pregnant group in stage 2 (95% vs 82%). Improvement was identified in stages 3 and 4 for both groups, but consolidation was still more frequent for pregnant women in stage 4. Most patients (38/40, 95%) were grouped as mild or common type. The length of hospitalization between the two groups was similar. CONCLUSION: Pregnant women with COVID-19 pneumonia did not present typical clinical features, while developing a relatively more severe disease at imaging with a slower recovery course and experiencing similar outcomes compared with the non-pregnant women.