Non-NAT Definite Diagnosis Models of COVID-19 Based on Hematological Features (preprint)

Background: Given that 2019 novel coronavirus (COVID-19) spreads rapidly, it is critical to make rapid and accurate detection of COVID-19 patients towards containment of SARS-CoV-2 virus. At present, COVID-19 patients are mainly identified through viral nuclear acid testing (NAT). However, factors such as time for patients being tested, experience of test operators, and specimen’s preparation, might affect the accuracy of testing results. The purpose of this study was to use different classification and feature selection methods to improve the diagnostic accuracy of COVID-19 patients. Methods: We utilized seven machine learning algorithms for assisting diagnosis of COVID-19 by developing a non-NAT algorithm. In order to reduce the number of input features while maintaining the models’ performance so as to decrease the cost and time consumption, we adopted three algorithms, such as Chi-square test, variance analysis, and feature importance tests to identify the optimal feature sets. Findings: The XGBoost and RF models displayed the best performance for COVID-19 detection, with the highest accuracy rate more than 0·96. The accuracy of RF model was 0·968 when using only ten hematological features and body temperature. Interpretation: Ten blood features and body temperature can fairly accurately determine whether a suspected patient is infected with COVID-19. Our model can improve the diagnostic accuracy of COVID-19 and reduce the spread. Funding: This work is supported by grants from the National Key Research and Development Program of China under Grant 2017YFE0123600, the Natural Science Foundation of China (81873931, 81974382 and 81773104), the Frontier Exploration Program of Huazhong University of Science and Technology (2015TS153), and the Major Scientific and Technological Innovation Projects in Hubei Province (2018ACA136).Declaration of Interests: All the authors stated that the paper had never been published elsewhere, and that there were no competing economic interests.Ethics Approval Statement: The collection, use, and retrospective analysis of chest CT images, CFs and SARS-CoV-2 nucleic acid PCR results of patients were approved by the institutional ethical committees of HUST-UH (IRB ID: [2020] IEC(A001)).

Patient-reported Outcomes of Patients With Breast Cancer During the COVID-19 Outbreak in the Epicenter of China: A Cross-sectional Survey Study.

INTRODUCTION: We aimed to analyze the psychological status in patients with breast cancer (BC) in the epicenter of the coronavirus disease 2019 (COVID-19) pandemic. PATIENTS AND METHODS: A total of 658 individuals were recruited from multiple BC centers in Hubei Province. Online questionnaires were conducted, and these included demographic information, clinical features, and 4 patient-reported outcome scales (Generalized Anxiety Disorder Questionnaire [GAD-7], Patient Health Questionnaire [PHQ-9], Insomnia Severity Index [ISI], and Impact of Events Scale-Revised [IES-R]). Multivariable logistic regression analysis was designed to identify potential factors on mental health outcomes. RESULTS: Questionnaires were collected from February 16, 2020 to February 19, 2020, the peak time point of the COVID-19 outbreak in China. Of patients with BC, 46.2% had to modify planned necessary anti-cancer treatment during the outbreak. Severe anxiety and severe depression were reported by 8.9% and 9.3% of patients, respectively. Severe distress and insomnia were reported by 20.8% and 4.0% of patients, respectively. Multivariable logistic regression analysis demonstrated poor general condition, shorter duration after BC diagnosis, aggressive BC molecular subtypes, and close contact with patients with COVID-19 as independent factors associated with anxiety. Poor general condition and central venous catheter flushing delay were factors that were independently associated with depression. In terms of insomnia, poor generation condition was the only associated independent factor. Poor physical condition and treatment discontinuation were underlying risk factors for distress based on multivariable analysis. CONCLUSION: High rates of anxiety, depression, distress, and insomnia were observed in patients with BC during the COVID-19 outbreak. Special attention should be paid to the psychological status of patients with BC, especially those with poor general condition, treatment discontinuation, aggressive molecular subtypes, and metastatic BC.