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

dc.creatorPatel, Jayeshkumar
dc.creatorLadani, Amit
dc.creatorSambamoorthi, Nethra
dc.creatorLeMasters, Traci
dc.creatorDwibedi, Nilanjana
dc.creatorSambamoorthi, Usha
dc.creator.orcid0000-0001-8311-1360 (Sambamoorthi, Usha)
dc.date.accessioned2022-11-09T15:00:03Z
dc.date.available2022-11-09T15:00:03Z
dc.date.issued2020-12-28
dc.description.abstractEvidence 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((R)) 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.
dc.description.sponsorshipThis research received no external funding.
dc.identifier.citationPatel, J., Ladani, A., Sambamoorthi, N., LeMasters, T., Dwibedi, N., & Sambamoorthi, U. (2020). A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis. International journal of environmental research and public health, 18(1), 155. https://doi.org/10.3390/ijerph18010155
dc.identifier.issn1660-4601
dc.identifier.issue1
dc.identifier.urihttps://hdl.handle.net/20.500.12503/31899
dc.identifier.volume18
dc.publisherMDPI
dc.relation.urihttps://doi.org/10.3390/ijerph18010155
dc.rights.holder© 2020 by the authors.
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceInternational Journal of Environmental Research and Public Health
dc.subjectadverse drug event
dc.subjectaged
dc.subjectanti-inflammatory agents
dc.subjectcohort studies
dc.subjectnon-steroidal
dc.subjectosteoarthritis
dc.subject.meshAged
dc.subject.meshAged, 80 and over
dc.subject.meshAnti-Inflammatory Agents, Non-Steroidal / adverse effects
dc.subject.meshCardiovascular Diseases
dc.subject.meshFemale
dc.subject.meshGastrointestinal Diseases
dc.subject.meshHumans
dc.subject.meshInappropriate Prescribing / prevention & control
dc.subject.meshMachine Learning
dc.subject.meshMale
dc.subject.meshMedicare
dc.subject.meshOsteoarthritis / drug therapy
dc.subject.meshRetrospective Studies
dc.subject.meshUnited States / epidemiology
dc.titleA Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis
dc.typeArticle
dc.type.materialtext

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
full text article
Size:
1.41 MB
Format:
Adobe Portable Document Format
Description: