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

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.

Multimodal Multidisciplinary Management of Patients with Moderate to Severe Pain in Knee Osteoarthritis: A Need to Meet Patient Expectations

“…The OARSI guidelines fully supported this indication [ 4 ]. It must be remembered that continuous NSAID use should be never ‘chronic’, a recommendation that is supported by safety concerns, a lack of long-term trials, and the recent finding that a large number of patients are taking NSAIDs inappropriately (including those with multimorbidity) [ 21 ]. Finally, despite no significant difference in efficacy between cyclooxygenase-2 (COX-2) selective, partially selective, or non-selective NSAIDs, the most recent literature regarding NSAIDs suggests that celecoxib may reduce pain more effectively than other medications in knee OA [ 22 ], with the potential added benefit of the suppression of inflammation [ 23 , 24 ].…”

Analgesic Effectiveness of Physical Therapy Combining the Use of Electromagnetic Fields with Light Radiation Emitted by LEDs along with the Use of Topical Herbal Ointment in Patients with Gonarthrosis

“…Many authors have confirmed the effectiveness and good tolerability of the use of Traumeel S ointment in their works [ 16 , 23 , 24 ]. Non-steroidal anti-inflammatory drugs (NSAIDs) have become the primary drugs used to treat pain, trauma and inflammation [ 25 , 26 ]. A therapeutic alternative to NSAIDs in the treatment of OA is Traumeel S. Non-steroidal anti-inflammatory drugs (NSAIDs) and Traumeel S ointment have shown comparable analgesic efficacy in patients with OA [ 27 , 28 ].…”

A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease

“…Similar to this study, Patel et al (2020) built prediction models using cross-validated logistic regression (CVLR) and XGBoost to screen predictors of potentially inappropriate osteoarthritis in the elderly with NSAIDs. The machine learning algorithms used in this study included two machine learning algorithms used by Patel et al (2020) . Compared to this study, Patel et al (2020) reported a better predictive performance with an AUC of 0.8341 and an accuracy of 0.7160.…”

“…The machine learning algorithms used in this study included two machine learning algorithms used by Patel et al (2020) . Compared to this study, Patel et al (2020) reported a better predictive performance with an AUC of 0.8341 and an accuracy of 0.7160. However, Patel’s study did not perform external validation and had poor generalization ability.…”

“…Multiple studies reported that machine learning algorithms could predict severe hypoglycemia in hospitals, identify genetic risk factors for the progression and survival of colorectal cancer, etc. Patel et al used machine learning algorithms to develop predictive models to identify predictors of inappropriate use of non-steroidal anti-inflammatory drugs (NSAIDs) of PIP in elderly patients with osteoarthritis ( Patel et al, 2020 ).…”