Current medical research and opinion · 2026
Predicting the risk of venous thromboembolism events in older adults with rheumatoid arthritis after initiating targeted disease-modifying antirheumatic drugs: a comparison of the random survival forest and regularized cox regression models.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Huang Yinan, Bazzazzadehgan Shadi, Maharjan Shishir, Lin Ying, Bentley John P, Agarwal Sandeep K, Yang Yi
Аннотация
OBJECTIVES: To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthritis (RA) patients initiating biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) and compare its model performance with a regularized Cox regression (RegCox) model.
METHODS: This retrospective cohort study using the 5% Medicare data (2012-2020) identified older RA patients (≥ 65 years) initiating b/tsDMARDs (index date), including tumor necrosis factor inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, and tsDMARDs between January 1, 2013, through December 31, 2019. Study cohort was followed until an incident composite VTE event or censoring. Data were divided into training (75%) and testing (25%) sets. The RSF model was trained to predict VTE events during the follow-up period in the training set, with the RegCox model as the reference model. The performance of these models was evaluated in the testing data using the C-index. Variable importance of the predictors was assessed.
RESULTS: Of 3,648 RA patients, 360 (9.87%) experienced any VTE event. The RSF model had better performance (C-index [95% CI] = 0.609[0.602-0.617]) than the RegCox model (C-index [95% CI] = 0.599[0.597-0.602], p = 0.0021). Variables commonly identified as the top influential variables were varicose veins, inpatient visits, Elixhauser score, emergency room visits, and outpatient visits.
CONCLUSIONS: The RSF model performed slightly better in identifying VTE in RA patients after b/tsDMARDs initiation than RegCox. Incorporating additional clinical and contextual information beyond claims data may further enhance predictive accuracy in future studies.
METHODS: This retrospective cohort study using the 5% Medicare data (2012-2020) identified older RA patients (≥ 65 years) initiating b/tsDMARDs (index date), including tumor necrosis factor inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, and tsDMARDs between January 1, 2013, through December 31, 2019. Study cohort was followed until an incident composite VTE event or censoring. Data were divided into training (75%) and testing (25%) sets. The RSF model was trained to predict VTE events during the follow-up period in the training set, with the RegCox model as the reference model. The performance of these models was evaluated in the testing data using the C-index. Variable importance of the predictors was assessed.
RESULTS: Of 3,648 RA patients, 360 (9.87%) experienced any VTE event. The RSF model had better performance (C-index [95% CI] = 0.609[0.602-0.617]) than the RegCox model (C-index [95% CI] = 0.599[0.597-0.602], p = 0.0021). Variables commonly identified as the top influential variables were varicose veins, inpatient visits, Elixhauser score, emergency room visits, and outpatient visits.
CONCLUSIONS: The RSF model performed slightly better in identifying VTE in RA patients after b/tsDMARDs initiation than RegCox. Incorporating additional clinical and contextual information beyond claims data may further enhance predictive accuracy in future studies.