Abstract
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.
Highlights
The current outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly and widely across the world, causing panic and major public health challenges in the international community [1]
We recruited 206 patients with a confirmed diagnosis of COVID19; of these, 44 patients progressed to severe COVID-19, and 162 patients were classified as having non-severe COVID-19
We analyzed a range of indicators associated with severe COVID-19 and developed a novel predictive model that included ALT, IL-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and CREA
Summary
The current outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly and widely across the world, causing panic and major public health challenges in the international community [1]. A small proportion of the total number of cases progress to a severe condition (∼15–20%); ∼40% of patients with severe disease die [2,3,4,5]. Some research has shown that initial therapy with remdesivir or non-invasive positive pressure ventilation (NIPPV) is very efficient for severe cases, there is currently a lack of accepted recommendations for severe patients with regard to individualized treatment [6,7,8]. There is an urgent need to develop options for the personalized diagnosis and treatment of such patients, with regard to protecting the relative shortage of medical resources. Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, optimizing the use of medical resources
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