학술논문
Development of a Prediction Model for COVID‐19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry
Document Type
article
Author
Izadi, Zara; Gianfrancesco, Milena A; Aguirre, Alfredo; Strangfeld, Anja; Mateus, Elsa F; Hyrich, Kimme L; Gossec, Laure; Carmona, Loreto; Lawson‐Tovey, Saskia; Kearsley‐Fleet, Lianne; Schaefer, Martin; Seet, Andrea M; Schmajuk, Gabriela; Jacobsohn, Lindsay; Katz, Patricia; Rush, Stephanie; Al‐Emadi, Samar; Sparks, Jeffrey A; Hsu, Tiffany Y‐T; Patel, Naomi J; Wise, Leanna; Gilbert, Emily; Duarte‐García, Alí; Valenzuela‐Almada, Maria O; Ugarte‐Gil, Manuel F; Ribeiro, Sandra Lúcia Euzébio; de Oliveira Marinho, Adriana; de Azevedo Valadares, Lilian David; Di Giuseppe, Daniela; Hasseli, Rebecca; Richter, Jutta G; Pfeil, Alexander; Schmeiser, Tim; Isnardi, Carolina A; Torres, Alvaro A Reyes; Alle, Gelsomina; Saurit, Verónica; Zanetti, Anna; Carrara, Greta; Labreuche, Julien; Barnetche, Thomas; Herasse, Muriel; Plassart, Samira; Santos, Maria José; Rodrigues, Ana Maria; Robinson, Philip C; Machado, Pedro M; Sirotich, Emily; Liew, Jean W; Hausmann, Jonathan S; Sufka, Paul; Grainger, Rebecca; Bhana, Suleman; Costello, Wendy; Wallace, Zachary S; Yazdany, Jinoos; Registry, Global Rheumatology Alliance
Source
ACR Open Rheumatology. 4(10)
Subject
Language
Abstract
ObjectiveSome 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.MethodsData 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.ResultsThe 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.ConclusionWe 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.