Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission - Wang, Hai; Ai, Haibo; Fu, Yunong; Li, Qinglin; Cui, Ruixia; Ma, Xiaohua; Ma, Yan-fen; Wang, Zi; Liu, Tong; Long, Yunxiang; Qu, Kai; Liu, Chang; et al. (Frontiers in Medicine, 2021)
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. 75% 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 4711 COVID-19 patients were included in our study. The average age of them was 63.37 ± 16.70 years, of which 1148 (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 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.