Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.

Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer-Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.

A precision medicine approach to managing 2019 novel coronavirus pneumonia.

In December 2019, several patients with pneumonia of an unknown cause were detected in Wuhan, China. On 7 January 2020, the causal organism was identified as a new coronavirus, later named as the 2019 novel coronavirus (2019-nCoV). Genome sequencing found the genetic sequence of 2019-nCoV homologous to that of severe acute respiratory syndrome-associated coronavirus. As of 29 January 2020, the virus had been diagnosed in more than 7000 patients in China and 77 patients in other countries. It is reported that both symptomatic and asymptomatic patients with 2019-nCoV can play a role in disease transmission via airborne and contact. This finding has caused a great concern about the prevention of illness spread. The clinical features of the infection are not specific and are often indistinguishable from those of other respiratory infections, making it difficult to diagnose. Given that the virus has a strong ability to spread between individuals, it is of top priority to identify potential or suspected patients as soon as possible-or the virus may cause a serious pandemic. Therefore, a precision medicine approach to managing this disease is urgently needed for detecting and controlling the spread of the virus. In this article, we present such an approach to managing 2019-nCoV-related pneumonia based on the unique traits of the virus recently revealed and on our experience with coronaviruses at West China Hospital in Chengdu, China.