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

作者：Zara Izadi, Milena 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 Hsu, Naomi J. Patel, Leanna Wise, Emily Gilbert, Alí Duarte‐García, Maria O Valenzuela-Almada, Manuel F. Ugarte‐Gil, Sandra Lúcia Euzébio Ribeiro, Adriana de Oliveira Marinho, Lílian David de Azevedo Valadares, Daniela Di Giuseppe, Rebecca Hasseli, Jutta Richter, Alexander Pfeil, Tim Schmeiser, C. A. Isnardi, Alvaro Andres Reyes Torres, Gelsomina Alle, Verónica Saurit, Anna Zanetti, Greta Carrara, Julien Labreuche, Thomas Barnetche, Muriel Hérasse, Samira Plassart, María José Santos, Ana Maria Rodrigues, Philip C. Robinson, Pedro Machado, Emily Sirotich, Jean W. Liew, Jonathan S. Hausmann, Paul Sufka, Rebecca Grainger, Suleman Bhana, Wendy Costello, Zachary S. Wallace, Jinoos Yazdany, Global Rheumatology Alliance Registry · 发表于：ACR Open Rheumatology · 年份：2022 · DOI：10.1002/acr2.11481 · 被引用次数：10 · 研究领域：COVID-19 Clinical Research Studies、Long-Term Effects of COVID-19、SARS-CoV-2 and COVID-19 Research

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 wi...