Program

In-person AND virtual! – We are pioneering a new conference format that will connect in-person and virtual audiences to create a unique experience. Matching the innovation that comes through our members’ work, ISPOR is pushing the boundaries of innovation to design an event that works in today’s quickly changing environment. 

In-person registration included the full virtual experience, and virtual-only attendees will be able to tune into live in-person sessions and/or watch captured in-person sessions on-demand in addition to having a variety of virtual-only sessions to attend.

Prediction of Non-Response to First-Line Methotrexate Treatment in Rheumatoid Arthritis: A Real-World Data Analysis Using Machine Learning

Speaker(s)

Icten Z1, Starzyk K2, Friedman M3, Menzin J1
1Panalgo, Boston, MA, USA, 2OM1, Boston, MA, USA, 3Panalgo LLC, Boston, MA, USA

OBJECTIVES: This study aims to identify predictors of non-response to methotrexate (MTX), the first choice among disease-modifying drugs (DMARDs) for most rheumatoid arthritis (RA) patients, using a longitudinal clinical cohort of RA patients and machine learning (ML).

METHODS: Adult RA patients initiating MTX between 01/01/2014 and 08/01/2020 without previous bDMARD/tsDMARD utilization were identified in the OM1 RA Registry (OM1, Inc; Boston, MA). First MTX date was the index date, with ≥1 Clinical Disease Activity Index (CDAI) and ≥2 healthcare encounters in the preceding 12 months. Patients initiating bDMARD/tsDMARDs within 30 days of index date or with other indication(s) for bDMARD/tsDMARDs were excluded. MTX non-response was defined as failing to attain remission or low disease activity (CDAI>10) within 4-8 months and/or the initiation of a bDMARD/tsDMARD within 8 months following index date. Features included demographics, comorbidities, baseline CDAI, body mass index, medications, procedures and healthcare resource utilization. Three-fold cross-validation was used to tune and evaluate traditional and regularized logistic regression, XGBoost, support vector machine, random forest and feed-forward neural network models. Best model was selected using the area under the ROC curve (AUC) and accuracy, recall, precision and specificity were assessed.

RESULTS: Among 6,648 eligible RA patients, 3,748 (56.4%) were classified as non-responders (mean age=59.8 years; females=79.1%). A total of 250 features were included; XGBoost model had the best AUC (AUC=76.4%; accuracy=70.9%; recall=75.1%; precision=73.9%; specificity=65.5%) with improvement over logistic regression (AUC=72.3%). Top predictors included increasing baseline CDAI, use of GABA analogs, analgesics, glucocorticoids, anxiolytics or sedatives, history of mood disorders, younger age and being female. Regardless of model type, inclusion of CDAI greatly improved model prediction.

CONCLUSIONS: Our study identified predictors of MTX non-response with strong predictive accuracy using ML. Further research is needed to better understand the potential role of comorbidities, like mood disorders, and their management on treatment success.

Code

RWD88

Topic

Clinical Outcomes, Methodological & Statistical Research, Real World Data & Information Systems

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Clinical Outcomes Assessment, Health & Insurance Records Systems

Disease

Drugs, Musculoskeletal Disorders, Personalized and Precision Medicine, Systemic Disorders/Conditions