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The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
Juan Torres-Macho1,2, Pablo Ryan1,2,3, Jorge Valencia1
1University Hospital Infanta Leonor, 28031 Madrid, Spain.
Insights
A new prognostic model predicts mortality risk in hospitalized coronavirus 19 (COVID-19) patients using readily available admission data. This tool aids early identification of high-risk individuals for timely intervention.
Area of Science:
- Medical Informatics
- Epidemiology
- Clinical Prediction Models
Background:
- Coronavirus 19 (COVID-19) poses a significant mortality risk to hospitalized patients.
- Accurate early risk stratification is crucial for optimizing patient management and resource allocation.
Purpose of the Study:
- To develop an accessible prognostic model for predicting in-hospital mortality in COVID-19 patients.
- To utilize routine clinical, radiological, and laboratory data available at admission for model development.
Main Methods:
- Retrospective analysis of clinical data from 1968 hospitalized COVID-19 patients.
- Logistic regression model with classification trees to identify optimal predictive variables.
- Development of an interpretable scoring system to estimate mortality probability.
Main Results:
- The final model incorporated nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium.
- The model demonstrated excellent predictive discrimination (AUC: 0.865 in training, 0.808 in validation, 0.883 in test sets).
- A prognostic scale was created to quantify the probability of death based on the derived score.
Conclusions:
- An easily applicable predictive model was successfully designed for early risk identification in COVID-19 patients.
- The model facilitates the early identification of hospitalized patients at high risk of mortality.
Abstract:
This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients.
Methods:
We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret.
Results:
Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score.
Conclusions:
We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.
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