ACR Open Rheumatol
. 2022 Jul 22.
doi: 10.1002/acr2.11481. Online ahead of print.
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 1 , Milena A Gianfrancesco 1 , Alfredo Aguirre 1 , Anja Strangfeld 2 , Elsa F Mateus 3 , Kimme L Hyrich 4 , Laure Gossec 5 , Loreto Carmona 6 , Saskia Lawson-Tovey 7 , Lianne Kearsley-Fleet 8 , Martin Schaefer 9 , Andrea M Seet 1 , Gabriela Schmajuk 10 , Lindsay Jacobsohn 1 , Patricia Katz 1 , Stephanie Rush 1 , Samar Al-Emadi 11 , Jeffrey A Sparks 12 , Tiffany Y-T Hsu 12 , Naomi J Patel 13 , Leanna Wise 14 , Emily Gilbert 15 , Alí Duarte-García 16 , Maria O Valenzuela-Almada 16 , Manuel F Ugarte-Gil 17 , Sandra Lúcia Euzébio Ribeiro 18 , Adriana de Oliveira Marinho 19 , Lilian David de Azevedo Valadares 20 , Daniela Di Giuseppe 21 , Rebecca Hasseli 22 , Jutta G Richter 23 , Alexander Pfeil 24 , Tim Schmeiser 25 , Carolina A Isnardi 26 , Alvaro A Reyes Torres 27 , Gelsomina Alle 27 , Verónica Saurit 28 , Anna Zanetti 29 , Greta Carrara 29 , Julien Labreuche 30 , Thomas Barnetche 31 , Muriel Herasse 32 , Samira Plassart 32 , Maria José Santos 33 , Ana Maria Rodrigues 34 , Philip C Robinson 35 , Pedro M Machado 36 , Emily Sirotich 37 , Jean W Liew 38 , Jonathan S Hausmann 39 , Paul Sufka 40 , Rebecca Grainger 41 , Suleman Bhana 42 , Wendy Costello 43 , Zachary S Wallace 13 , Jinoos Yazdany 1 , Global Rheumatology Alliance Registry
Affiliations
- PMID: 35869686
- DOI: 10.1002/acr2.11481
Abstract
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.