A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis

Jayeshkumar Patel, Amit Ladani, Nethra Sambamoorthi, Traci LeMasters, Nilanjana Dwibedi and Usha Sambamoorthi
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Jayeshkumar Patel: Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV 26506, USA
Amit Ladani: Rheumatology, West Virginia University Medicine, Morgantown, WV 26506, USA
Nethra Sambamoorthi: Masters in Data Science Program, School of Professional Studies, Northwestern University, Chicago, IL 60201, USA
Traci LeMasters: Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV 26506, USA
Nilanjana Dwibedi: Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV 26506, USA
Usha Sambamoorthi: Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV 26506, USA

IJERPH, 2020, vol. 18, issue 1, 1-16

Abstract: Evidence from some studies suggest that osteoarthritis (OA) patients are often prescribed non-steroidal anti-inflammatory drugs (NSAIDs) that are not in accordance with their cardiovascular (CV) or gastrointestinal (GI) risk profiles. However, no such study has been carried out in the United States. Therefore, we sought to examine the prevalence and predictors of potentially inappropriate NSAIDs use in older adults (age > 65) with OA using machine learning with real-world data from Optum De-identified Clinformatics ® Data Mart. We identified a retrospective cohort of eligible individuals using data from 2015 (baseline) and 2016 (follow-up). Potentially inappropriate NSAIDs use was identified using the type (COX-2 selective vs. non-selective) and length of NSAIDs use and an individual’s CV and GI risk. Predictors of potentially inappropriate NSAIDs use were identified using eXtreme Gradient Boosting. Our study cohort comprised of 44,990 individuals (mean age 75.9 years). We found that 12.8% individuals had potentially inappropriate NSAIDs use, but the rate was disproportionately higher (44.5%) in individuals at low CV/high GI risk. Longer duration of NSAIDs use during baseline (AOR 1.02; 95% CI:1.02–1.02 for both non-selective and selective NSAIDs) was associated with a higher risk of potentially inappropriate NSAIDs use. Additionally, individuals with low CV/high GI (AOR 1.34; 95% CI:1.20–1.50) and high CV/low GI risk (AOR 1.61; 95% CI:1.34–1.93) were also more likely to have potentially inappropriate NSAIDs use. Heightened surveillance of older adults with OA requiring NSAIDs is warranted.

Keywords: osteoarthritis; anti-inflammatory agents; non-steroidal; machine learning; cohort studies; aged; adverse drug event (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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