Online Program Home
My Program

Abstract Details

Activity Number: 355 - Contributed Poster Presentations: Biopharmaceutical Section
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #304851
Title: Mining Longitudinal Real-World Data to Identify Risk Factors for Cardiovascular Events Related to Anti-Dementia Medications
Author(s): Meiqi He* and Yuting Zhang and Inmaculada Hernandez
Companies: University of Pittsburgh School of Pharmacy and University of Melbourne Institute of Applied Economic and Social Research and University of Pittsburgh School of Pharmacy
Keywords: feature selection; data mining; pharmacological therapy; Alzheimer's disease

Applying a combination of data mining techniques and statistical methods to claims data can help identify causal relationships between medications and the incidence of related side effects. Given the unclear association between use of anti-dementia medications and the risk for cardiovascular adverse events, we leveraged 2007-2013 Medicare claims data and identified 30433 patients newly diagnosed with Alzheimer’s Disease (AD) who later initiated anti-dementia therapy. We used feature selection techniques followed by step-wise logistic regressions to identify risk factors for side effects. For the pre-specified 10381 potential factors, including patient demographics, ICD-9 diagnosis codes and therapeutic classes of patient-used medications, our combined model of factor screening, ranking and step-wise logistic regression classification successfully identified 55 risk factors for any adverse cardiovascular event. We obtained a model performance with a C-statistics of 0.67 and an accuracy of 0.74. Subgroup analyses studying specific adverse cardiovascular events by implementing same model also yielded similar outcomes, providing insights into patient safety related to AD drug use.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program