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

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.

Figure 1

Data set partitioning into training, test, and validation sets. 1) Seven supervised machine learning algorithms were trained on acute respiratory distress syndrome outcomes using three repeats of 10‐fold cross‐validation. 2) Predictive performance was assessed in the test set. 3) Predictive performance was further assessed in the validation sets. ^aSubset A included all patients reported from the United States, except patients reported from Mass General Brigham in Massachusetts and Mayo Clinics in Minnesota and Florida. ^bSubset B included all patients reported from Mass General Brigham in Massachusetts and Mayo Clinics in Minnesota and Florida. These health systems systematically reported all coronavirus disease 2019 (COVID‐19) diagnoses, irrespective of severity. ^cItaly had the lowest rates of unknown data (<7% in any variable and <10% of patients excluded because of incomplete data) among all validation sets and was therefore considered the primary validation set. GRA, Global Rheumatology Alliance.

Figure 2

Adjusted odds ratios (ORs) obtained from the multivariable logistic regression model. Top 10 most influential predictors identified by the gradient boosting machine. CI, confidence interval; GC, glucocorticoid.

Figure 3

The risk score calculator pocket care side 1. BMI, body mass index; COVID‐19, coronavirus disease 2019.

Figure 4

The risk score calculator pocket care side 2. CI, confidence interval; COVID‐19, coronavirus disease 2019.