PDF(Pubmed)
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.
摘要：
背景：预测2019年严重冠状病毒病（COVID-19）的进展风险可以促进个性化诊断和治疗方案，从而优化医疗资源的使用。方法：在这项前瞻性研究中，在2019年12月20日至2020年4月10日期间，从地区医疗机构招募了206例COVID-19患者。我们整理了一系列数据，以得出和验证COVID-19进展的预测模型，包括人口统计，临床特征,实验室发现,和细胞因子水平。变异分析,以及最小绝对收缩和选择算子(LASSO)和Boruta算法，用于建模。通过特异性评估衍生模型的性能，灵敏度,接收器工作特征(ROC)曲线(AUC)下面积，Akaike信息准则(AIC)，校准图,决策曲线分析(DCA)，还有Hosmer-Lemeshow测试.结果：我们使用LASSO算法和逻辑回归建立了一个模型，可以准确预测严重COVID-19的进展风险。该模型掺入了丙氨酸氨基转移酶（ALT），白细胞介素（IL）-6，咳痰，疲劳,淋巴细胞比率（LYMR），天冬氨酸转氨酶（AST），肌酐（CREA）。该模型在推导和验证队列中产生了令人满意的预测性能，AUC为0.9104和0.8792，分别。然后将最终模型用于创建列线图，将其包装到开源和预测性计算器中以供临床使用。该模型可在https://severconid-19predction在线免费获得。shinyapps.io/SHINY/.结论：在这项研究中，我们开发了一个基于ALT的开源和免费的COVID-19进展预测计算器，IL-6,咳痰,疲劳,LYMR,AST,和CREA。经验证的模型可以有效预测严重COVID-19的进展，从而为早期和个性化管理以及分配适当的医疗资源提供了有效的选择。
