Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission.

Hai Wang,Haibo Ai,Ruixia Cui,Jingyao Zhang,Qinglin Li,Kai Qu,Zi Wang,Chang Liu,Yunxiang Long,Tong Liu,Xiaohua Ma,Yan-Fen Ma,Yunong Fu

doi:10.3389/fmed.2021.699243

Abstract

Introduction: COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. However, there is still a lack of a simple and effective tool to identify these patients early.Methods: A retrospective cohort study was conducted to develop an early warning model for predicting the death risk of COVID-19. Seventy-five percent of the cases were used to construct the prediction model, and the remaining 25% were used to verify the prediction model based on data immediately available on admission.Results: From March 1, 2020, to April 16, 2020, a total of 4,711 COVID-19 patients were included in our study. The average age was 63.37 ± 16.70 years, of which 1,148 (24.37%) died. Finally, age, SpO2, body temperature (T), and mean arterial pressure (MAP) were selected for constructing the model by univariate analysis, multivariate analysis, and a review of the literature. We used five common methods for constructing the model and finally found that the full model had the best specificity and higher accuracy. The area under the ROC curve (AUC), specificity, sensitivity, and accuracy of full model in train cohort were, respectively, 0.798 (0.779, 0.816), 0.804, 0.656, and 0.768, and in the validation cohort were, respectively, 0.783 (0.751, 0.815), 0.800, 0.616, and 0.755. Visualization tools of the prediction model included a nomogram and an online dynamic nomogram (https://wanghai.shinyapps.io/dynnomapp/).Conclusion: We developed a prediction model that might aid in the early identification of COVID-19 patients with a high probability of mortality on admission. However, further research is required to determine whether this tool can be applied for outpatient or home-based COVID-19 patients.

Highlights

COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment
Univariate analysis showed that age, SpO2, mean artistic pressure (MAP), White and chronic obstructive pulmonary disease (COPD) were shown to be associated with patient prognosis, with OR values of 1.051 (1.045, 1.056), 0.946 (0.938, 0.954), 0.947 (0.943, 0.952), 1.286 (1.040, 1.591), and1.368 (1.043, 1.794) (See Table 2)
Multivariate logistic regression analysis revealed that age, SpO2, MAP, T, and Asian were associated with the prognosis of patients with COVID-19, and their OR values were, respectively, 1.052 (1.046, 1.058), 0.954 (0.945, 0.963), 0.952 (0.947, 0.957), 1.109 (1.015, 1.211), and 1.788 (1.109, 2.883) (See Table 2)

Summary

Introduction

COVID-19 has overloaded worldwide medical facilities, leaving some potentially high-risk patients trapped in outpatient clinics without sufficient treatment. A substantial proportion of COVID-19 patients were trapped in outpatient clinics or at home, unable to receive proper therapy [4]; among these there were some patients with a potentially high risk of death. How to early and effectively identify a COVID-19 patient with a high risk of death is a major challenge we face. NEWS2 had a higher warning value for the severity of patients with COVID-19 [9,10,11] These studies about NEWS2 were with minimal sample size, and the score contains eight variables, which made it more difficult to use and affected its clinical application value

Results

Discussion

Conclusion