Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China
| Autor: | Rui Wu, Shourong Liu, Fei Wang, Di He, Mengyan Wang, Jinsong Huang, Huaizhong Cui, Wenjun Ma, Dagan Yang, Kexin Qi, Jianjiang Qi, Chuntao Liu, Zhijian Li, Jing Wu, Fei Ye, Yimin Zhu, Jinjian Xu |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
China Assessment model Infectious and parasitic diseases RC109-216 Logistic regression Risk Assessment Severity Lasso (statistics) Internal medicine Medicine Humans Retrospective Studies Framingham Risk Score Receiver operating characteristic Web-based assessment system business.industry SARS-CoV-2 COVID-19 Regression analysis Regression Infectious Diseases Ordered logit business Risk assessment Prediction Biomarkers Research Article |
| Zdroj: | BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-10 (2021) BMC Infectious Diseases |
| Popis: | Background The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. Methods 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. Results Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values Conclusions Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment. |
| Databáze: | OpenAIRE |
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