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

Izadi Z.; Gianfrancesco M.A.; Aguirre A.; Strangfeld A.; Mateus E.F.; Hyrich K.L.; Gossec L.; Carmona L.; Lawson-Tovey S.; Kearsley-Fleet L.; Schaefer M.; Seet A.M.; Schmajuk G.; Jacobsohn L.; Katz P.; Rush S.; Al-Emadi S.; Sparks J.A.; Hsu T.Y.T.; Patel N.J.; Wise L.; Gilbert E.; Duarte-García A.; Valenzuela-Almada M.O.; Ugarte-Gil M.F.; Ribeiro S.L.E.; de Oliveira Marinho A.; de Azevedo Valadares L.D.; Giuseppe D.D.; Hasseli R.; Richter J.G.; Pfeil A.; Schmeiser T.; Isnardi C.A.; Reyes Torres A.A.; Alle G.; Saurit V.; Zanetti A.; Carrara G.; Labreuche J.; Barnetche T.; Herasse M.; Plassart S.; Santos M.J.; Rodrigues A.M.; Robinson P.C.; Machado P.M.; Sirotich E.; Liew J.W.; Hausmann J.S.; Sufka P.; Grainger R.; Bhana S.; Costello W.; Wallace Z.S.; Yazdany J. · ACR Open Rheumatology (2022) · DOI: 10.1002/acr2.11481

Resumen

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.