ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores
Authors: Marcolino Milena S. , Pires Magda C. , Ramos Lucas Emanuel F. , Silva Rafael T. , Oliveira Luana M. , Carvalho Rafael L.R. , Mourato Rodolfo L.S. , Sánchez-Montalvá Adrián , Raventós Berta , Anschau Fernando , Chatkin José Miguel , Nogueira Matheus C. A. , Guimarães Milton H. , Vietta Giovanna G. , Duani Helena , Ponce Daniela , Ziegelmann Patricia K. , Castro Luís C. , Ruschel Karen B. , Cimini Christiane C. R. , Francisco Saionara C. , Floriani Maiara A. , Nascimento Guilherme F. , Farace Bárbara L. , Monteiro Luanna S. , Souza-Silva Maira V. R. , Sales Thais L. S. , Martins Karina Paula M. P. , Nascimento Israel J. Borges do , Fereguetti Tatiani O. , Ferrara Daniel T. M. O. , Botoni Fernando A. , Etges Ana Paula Beck da Silva , Boersma Eric , Polanczyk Carisi A. ,   Brazilian COVID-19 Registry Investigators
Published: February 2021

Objective

To develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones.

Design

Cohort study

Setting

The Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.

Participants

Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay.

Main outcome measures

In-hospital mortality

Results

Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/).

Conclusions

We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Summary boxes

What is already known on this topic?

Rapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department.

The majority of available scores have high risk of bias and lack benefit to clinical decision making.

Derivation and validation studies in low- and middle-income countries, including Latin America, are scarce.

What this study adds

ABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate.

This easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores.

A free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation.