Predictors of Co-occurring Cardiovascular and Gastrointestinal Disorders among Elderly with Osteoarthritis

Abstract

Objective: To identify the leading predictors of co-occurring cardiovascular or gastrointestinal disorders (CV-GID) in a real-world cohort of elderly with osteoarthritis (OA). Method: An observational retrospective cohort study using data from Optum’s deidentified Clinformatics® Data Mart was conducted. Elderly with OA were identified in 2015 and were followed for two years to identify co-occurring CV-GID including ischemic heart disease, stroke, heart failure, dyspepsia, gastroesophageal reflux disorder, and peptic ulcer disease. Random Forest (RF) and Partial Dependence Plots (PDP) were used to identify the leading predictors of CV-GID and to examine their associations. Multivariable logistic regression was also used to examine the association of the leading predictors with CV-GID. Results: Our study cohort consisted of 45,385 elderly with OA (mean age 76.0 years). CV-GID were present in 59% of elderly. Using RF, age was found to be the strongest predictor of CV-GID followed by cardiac arrhythmia, duration of opioid use, number of orthopedist or physical therapy visits, number of intra-articular corticosteroid injections, polypharmacy, duration of non-selective nonsteroidal anti-inflammatory drugs or oral corticosteroids, and hypertension. The PDPs demonstrated that higher age, cardiac arrhythmia, longer durations of opioid or oral corticosteroids, higher number of physical therapy visits or intra-articular corticosteroid use, polypharmacy, and hypertension were associated with a higher risk of CV-GID. Conclusion: CV-GIDs are common among elderly with OA and can be predicted based on certain clinical factors. Machine learning methods with PDPs can be used to improve the interpretability and inform decision-making.

Keywords

• Osteoarthritis
• Cardiovascular disease
• Gastrointestinal disorders
• Machine learning
• Predictive modeling
• Random forest