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A risk scoring model of COVID-19 at hospital admission
João José Ferreira Gomes1, António Ferreira2, Afonso Alves1
1Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
Insights
COVID-19 mortality risk is predictable using patient age and comorbidities. Logistic regression models identified key factors including advanced age, pre-existing conditions, and hospital occupancy, aiding in risk stratification for better patient outcomes.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The COVID-19 pandemic presented a significant global health challenge, necessitating rapid decision-making amidst incomplete disease understanding.
- Accurate risk assessment for hospitalized COVID-19 patients is crucial for effective resource allocation and patient management.
Purpose of the Study:
- To develop and validate a predictive model for COVID-19 patient mortality risk.
- To identify key demographic and clinical factors associated with increased mortality risk in hospitalized COVID-19 patients.
Main Methods:
- Logistic regression analysis was employed due to its simplicity and interpretability.
- A scoring technique was utilized to integrate multiple patient comorbidities into a single continuous variable.
Main Results:
- The model demonstrated good discriminatory capacity (ROC AUC = 0.8) in predicting mortality risk.
- Key predictors of higher mortality risk include advanced age, pre-existing conditions (diabetes, hypertension), pneumonia, male gender, and high healthcare unit occupancy.
Conclusions:
- Age and comorbidities jointly contribute approximately 75% to explaining COVID-19 mortality.
- The developed model effectively differentiates mortality risk using readily available clinical and demographic data, applicable to Portuguese public hospitals.
Background:
The COVID-19 pandemic has been the most serious public health crisis in recent times, a pandemic whose impact was felt across the globe in various groups and populations. Confronted with an urgent problem, people and governments were forced to make decisions without fully understanding the disease. The present work aims to reinforce our ever-growing knowledge of the illness, particularly in modelling the risk of death of a patient admitted to a hospital with a positive COVID-19 test.
Methods:
Given the simplicity of using and programming logistic regression in any national healthcare unit and the ease of interpreting the results, we chose to use this technique over several other. Using scoring techniques, it is possible to associate the various diagnoses with a numerical value (score), making it possible therefore to integrate the patient's multiple medical conditions as a single continuous variable in the model.
Results:
It is possible to establish with good discriminatory capacity (ROC AUC Test = 0.8) which COVID patients are at higher risk when admitted to the healthcare unit-people of advanced age with pre-existing conditions, such as diabetes and high blood pressure, or newly acquired conditions, such as pneumonia. Moreover, males and clinical episodes occurring in healthcare units with few available beds (high healthcare unit occupancy) are also at higher risk. The importance of each variable in predicting the target is: age (47%), sum of comorbidity scores (28%), healthcare unit score (12.0%), gender score (7%) and healthcare unit occupancy (6%).
Conclusions:
Using a dataset with more than 52000 people, it was possible to successfully differentiate likelihood of death by COVID using age, comorbidity information, healthcare unit, healthcare unit occupancy and gender. The age and the comorbidities associated with each patient had a joint contribution of about 75% in explaining the COVID related mortality in Portuguese public hospitals in the period between March 2020 and May 2021.
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