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

Authors :
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
Giuseppe, Daniela Di
Hasseli, Rebecca
Richter, Jutta G
Pfeil, Alexander
Schmeiser, Tim
Isnardi, Carolina A
Reyes Torres, Alvaro A
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
Global Rheumatology Alliance Registry
Source :
ACR open rheumatology, vol 4, iss 10
Publication Year :
2022
Publisher :
eScholarship, University of California, 2022.

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.

Details

Database :
OpenAIRE
Journal :
ACR open rheumatology, vol 4, iss 10
Accession number :
edsair.od.......325..c6a2bfcfb0809f52ced1144f5da5bfef