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
University of California, San Francisco.
Deutsches Rheuma-Forschungszentrum Berlin, Berlin, Germany.
ACR Open Rheumatol. 2022 Oct;4(10):872-882. doi: 10.1002/acr2.11481. Epub 2022 Jul 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.
一些风湿性疾病患者可能患2019冠状病毒病（COVID-19）急性呼吸窘迫综合征（ARDS）的风险更高。我们旨在为该人群开发一种COVID-19 ARDS预测模型，并创建一个用于临床环境的简单风险评分计算器。
数据来自2020年3月24日至2021年5月12日的COVID-19全球风湿病联盟登记处。使用在COVID-19诊断时获得的83个变量，对7种机器学习分类器进行ARDS结局训练。在美国测试集中评估预测性能，并使用曲线下面积（AUC）、准确性、敏感性和特异性在来自四个拥有独立登记处国家的患者中进行验证。使用包含来自表现最佳分类器的最具影响力预测因子的回归模型开发了一个简单的风险评分计算器。
该研究纳入了来自74个国家的8633名患者，其中523名（6%）患有ARDS。梯度提升的平均AUC最高（0.78；95%置信区间[CI]：0.67 - 0.88），被认为是表现最佳的分类器。确定了10个预测因子为关键风险因素，并纳入回归模型。该回归模型在测试集中预测ARDS的敏感性为71%（95% CI：61% - 83%），在拥有独立登记处的国家中敏感性范围为61%至80%，用于开发风险评分计算器。
我们能够利用COVID-19诊断时 readily available 的信息以良好的敏感性预测ARDS。所提出的风险评分计算器有可能指导对单克隆抗体等有潜力降低COVID-19疾病进展的治疗进行风险分层。
