Publicação

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases

Detalhes bibliográficos
Resumo:	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.
Autores principais:	Izadi, Zara
Outros Autores:	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; Maria Rodrigues, Ana; Robinson, Philip C.; Machado, Pedro M.; Sirotich, Emily; Liew, Jean W.; Hausmann, Jonathan S.
Assunto:	Rheumatology
Ano:	2022
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	acesso aberto
Instituição associada:	Universidade Nova de Lisboa
Idioma:	inglês
Origem:	Repositório Institucional da UNL

Descrição
Resumo:	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.