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ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison
Milena S Marcolino1, Magda C Pires2, Lucas Emanuel F Ramos3
1Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507, Porto Alegre, Brazil.
Insights
A new COVID-19 mortality risk score was developed and validated for patients at hospital admission. This easy-to-use tool aids in early risk stratification for coronavirus disease 2019 patients.
Area of Science:
- Medical research
- Clinical epidemiology
- Public health
Background:
- Existing mortality risk scores for COVID-19 patients in emergency departments often carry a high risk of bias.
- Accurate prediction of in-hospital mortality is crucial for effective patient management.
Purpose of the Study:
- To develop and validate a novel risk score for predicting in-hospital mortality in COVID-19 patients upon hospital admission.
- To compare the performance of the new score against existing mortality prediction tools.
Main Methods:
- A logistic regression model was developed using data from 3978 COVID-19 patients admitted between March-July 2020.
- The model was validated internally using 1054 patients admitted August-September 2020 and externally with 474 Spanish patients.
- Seven significant variables were identified: age, blood urea nitrogen, comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate.
Main Results:
- The developed risk score demonstrated high discriminatory value with an AUROC of 0.844 in the derivation cohort.
- Validation in Brazilian and Spanish cohorts confirmed strong performance (AUROC 0.859 and 0.894, respectively).
- The new score outperformed existing models in predicting in-hospital mortality for COVID-19 patients.
Conclusions:
- An accessible, rapid scoring system was created and validated for early in-hospital mortality risk stratification in COVID-19 patients.
- The score utilizes readily available patient characteristics at hospital presentation.
- An online risk calculator is available for practical application.
Objectives:
The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.
Methods:
Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.
Results:
Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/).
Conclusions:
An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.
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