| Background and objectiveThe coronavirus disease 2019(COVID-19)epidemic broke out in Wuhan at the end of 2019 and then spread rapidly throughout the world.The World Health Organization announced on March 11,2020 that COVID-19 had constituted a global pandemic.The pandemic of COVID-19 has placed a huge burden on the medical systems of countries around the world.At present,there is an urgent need to design a scoring model that could predict COVID-19 severity,which could be used to identify patients with a high risk of severe and critical disease in the early stage,so as to prevent patients with mild and moderate pneumonia from occupying the intensive care unit,at the same time,to meet the necessary intensive care-related medical needs of patients with severe and critical disease.In this way,medical resources could be allocated reasonably and used to the best of their ability,thereby reducing the medical burden of various regions and reducing the mortality rate.So far,the clinical factors and laboratory indicators that could affect the severity of COVID-19 are not very clear,and methods that could predict the risk of severe COVID-19 are also very rare.Therefore,it is very meaningful to explore the influencing factors related to the severity of COVID-19,and to further establish and verify a model that could predict disease severity among COVID-19 patients.MethodsFrom January 1 to March 18,2020,a total of 690 patients with confirmed COVID-19 were collected from hospitals in Honghu and Nanchang;After excluding patients(248 cases)who had incomplete clinical data,those who were coinfected with other respiratory viruses,and those who were discharged within 24 h after admission,442 patients were retained in the final analysis,of which 332 patients were from Honghu and 110 patients were from Nanchang.We divided Honghu patients into the Honghu training group(231 cases)and the Honghu internal verification group(101 cases)at a ratio of 7:3 for model establishment and internal verification.In addition,we defined Nanchang patients as the Nanchang external validation group(110 cases)to further externally validate the model.Finally,we compared the model we have established with some existing models that could predict the severity of COVID-19,and analyzed the advantages and disadvantages of our model.ResultsFive risk factors(including hypertension,neutrophil count,C-reactive protein,lymphocyte count and lactate dehydrogenase)related to severe COVID-19 were identified through multiple logistic regression analysis,and HNC-LL scoring model was established based on these risk factors to predict the risk of non-severe COVID-19 patients developing severe COVID-19.The HNC-LL scoring model could accurately identify severe COVID-19 patients in the Honghu training cohort(area under the curve[AUC]=0.861,95%confidence interval[CI]:0.800-0.922;P<0.001),Honghu internal validation cohort(AUC=0.871,95%CI:0.769-0.972;P<0.001)and Nanchang external validation cohort(AUC=0.826,95%CI:0.746-0.907;P<0.001).In addition,by comparing other models,it is found that the HNC-LL scoring model can effectively predict the risk of developing severe COVID-19 from non-severe COVID-19 patients,thus confirming the important clinical significance of the HNC-LL scoring model.ConclusionsWe found the influencing factors related to the severity of COVID-19 and developed an accurate tool for predicting severe COVID-19.This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions. |
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