Abstract
This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients' admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients' ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients' conditions and providing early intervention for those with risk factors.
Highlights
A multicenter study conducted in China [9] was developed and internally verified a nomogram predicting COVID-19 based on the symptoms, vital signs, and comorbidities of 366 laboratory-confirmed COVID-19 patients in the emergency departments. e Harrel concordance index (C-Index) of this model was 0.863
In this study, according to the clinical data of 1046 patients with COVID-19, we developed a risk prediction model based on the age, clinical symptoms, and underlying diseases of newly admitted patients with COVID-19 to predict their development into severe conditions and further programmed a predictor on the Internet. e study screened 25 clinical factors and established a receiver operating characteristic (ROC) regression model with
Multivariate analysis showed that age was a risk factor for patients with the common type of COVID-19 for developing severe conditions. is may be related to the
Summary
Erefore, it is very important to know the risk factors for COVID-19 that develops from mild to severe, identify those potentially severe cases early, and give active intervention, in order to improve the survival rate of patients. The early warning model for severe/critical type of COVID-19 is mainly based on laboratory test indicators, and the predictive value of clinical symptoms for warning signs of severe COVID-19 is less researched. Is study was performed in a designated hospital for COVID-19 in one city It retrospectively collected the clinical data of all the 1,046 cases of confirmed and asymptomatic COVID-19 patients in this hospital from July to September in 2020, analyzed the related risk factors of developing severe illnesses, and explored the predictive value of clinical symptoms in the change from mild COVID-19 to severe conditions.
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