LAS VEGAS—Diabetes and previous joint pain, along with a patient’s overall physical health status, may predict arthritis pain with nearly 100% accuracy, according to new research presented at the 2015 Annual Meeting of the American Academy of Orthopaedic Surgeons (AAOS).
In this study, researchers created an algorithm, based on data from the 2011-2012 Medical Expenditure Panel Survey, to determine factors and patterns that contribute to pain for a national representative sample of 5,721 American adults with arthritis. The sample’s mean age was 60.14 years and the average household income was $52,275. The study authors looked at more than 1,000 variables pertaining to demographics, medical claims, laboratory tests, patient-reported outcomes, employment history, health insurance, medical expenditures, and socio-behavioral characteristics. Patient health status was determined through use of the SF-12 Health Component Survey.
Patients were asked whether or not their pain limited normal work. Responses were divided into a “no effect” group, for those who responded “not at all” or “a little bit;” and an “effect” group for respondents who stated that they experienced pain “moderately,” “quite a bit,” or “extremely.”
The study found that specific combinations of physical health, mental health, and general health status, as well as diabetes, previous joint pain, and a patient’s education level, predicted pain for individuals diagnosed with arthritis, with physical health status the greatest predictor of pain that limited work. The research did not find a link between arthritis pain and a body mass index (BMI) above 30 kg/m² (the threshold for obesity). One of the several algorithms that the researchers developed is able to predict pain at an accuracy rate of 98.6%.
“Our results indicate that physical health along with a number of conditions can significantly distinguish individuals with and without pain,” said Man Hung, PhD, Assistant Professor in the Department of Orthopaedic Surgery Operations at the University of Utah School of Medicine. “The algorithms generated in the study offer new insights into pain and should help in the development of cost-effective care management programs for those experiencing arthritis.”