Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

Zara Izadi, Milena A. Gianfrancesco, Alfredo Aguirre, Anja Strangfeld, Elsa F. Mateus, Kimme L. Hyrich, Laure Gossec, Loreto Carmona, Saskia Lawson-Tovey, Lianne Kearsley-Fleet, Martin Schaefer, Andrea M. Seet, Gabriela Schmajuk, Lindsay Jacobsohn, Patricia Katz, Stephanie Rush, Samar Al-Emadi, Jeffrey A. Sparks, Tiffany Y.T. Hsu, Naomi J. PatelLeanna Wise, Emily Gilbert, Alí Duarte-García, Maria O. Valenzuela-Almada, Manuel F. Ugarte-Gil, Sandra Lúcia Euzébio Ribeiro, Adriana de Oliveira Marinho, Lilian David de Azevedo Valadares, Daniela Di Giuseppe, Rebecca Hasseli, Jutta G. Richter, Alexander Pfeil, Tim Schmeiser, Carolina A. Isnardi, Alvaro A. Reyes Torres, Gelsomina Alle, Verónica Saurit, Anna Zanetti, Greta Carrara, Julien Labreuche, Thomas Barnetche, Muriel Herasse, Samira Plassart, Maria José Santos, Ana Maria Rodrigues, Philip C. Robinson, Pedro M. Machado, Emily Sirotich, Jean W. Liew, Jonathan S. Hausmann

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Objective: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.

Original languageEnglish
Pages (from-to)872 - 882
JournalACR Open Rheumatology
Issue number10
Early online date22 Jul 2022
Publication statusPublished - Oct 2022


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