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