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Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data
Hai Wang1, Haibo Ai2, Yunong Fu1
1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Insights
A new COVID-19 death risk model uses age, SpO2, temperature, and MAP for early identification. This tool helps predict mortality in hospitalized patients upon admission.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Public Health
Background:
- COVID-19 has strained healthcare systems globally.
- A need exists for simple tools to identify high-risk COVID-19 patients early.
- Current methods may not adequately identify patients requiring immediate intervention.
Purpose of the Study:
- To develop and validate an early warning model for predicting COVID-19 mortality risk.
- To identify key clinical parameters available on admission for risk stratification.
- To provide a tool for timely clinical decision-making in COVID-19 patient management.
Main Methods:
- Retrospective cohort study of 4,711 COVID-19 patients.
- Model development using 75% of data, validation on remaining 25%.
- Selection of predictors (age, SpO2, temperature, MAP) via statistical analysis and literature review.
Main Results:
- The final prediction model included age, SpO2, body temperature, and MAP.
- The full model demonstrated good performance with an AUC of 0.798 in the training cohort and 0.783 in the validation cohort.
- Visualization tools, including a dynamic nomogram, were developed for practical application.
Conclusions:
- An early warning model for COVID-19 mortality risk has been developed.
- The model aids in identifying high-risk patients upon admission.
- Further research is needed to assess its utility in outpatient or home-based settings.
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.
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