Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry.
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Izadi Z, Gianfrancesco MA, Aguirre A, Strangfeld A, Mateus EF, Hyrich KL, Gossec L, Carmona L, Lawson-Tovey S, Kearsley-Fleet L, Schaefer M, Seet AM, Schmajuk G, Jacobsohn L, Katz P, Rush S, Al-Emadi S, Sparks JA, Hsu TY, Patel NJ, Wise L, Gilbert E, Duarte-García A, Valenzuela-Almada MO, Ugarte-Gil MF, Ribeiro SLE, de Oliveira Marinho A, de Azevedo Valadares LD, Giuseppe DD, Hasseli R, Richter JG, Pfeil A, Schmeiser T, Isnardi CA, Reyes Torres AA, Alle G, Saurit V, Zanetti A, Carrara G, Labreuche J, Barnetche T, Herasse M, Plassart S, Santos MJ, Rodrigues AM, Robinson PC, Machado PM, Sirotich E, Liew JW, Hausmann JS, Sufka P, Grainger R, Bhana S, Costello W, Wallace ZS, Yazdany J, Global Rheumatology Alliance Registry
ACR Open Rheumatol - (-) - [2022-07-22; online 2022-07-22]
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. 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. 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. 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.
PubMed 35869686
DOI 10.1002/acr2.11481
Crossref 10.1002/acr2.11481