Development of a novel algorithm to determine adherence to chronic pain treatment guidelines using administrative claims.

objectiveTo develop a claims-based algorithm for identifying patients who are adherent versus nonadherent to published guidelines for chronic pain management.

methodsUsing medical and pharmacy health care claims from the MarketScan® Commercial and Medicare Supplemental Databases, patients were selected during July 1, 2010, to June 30, 2012, with the following chronic pain conditions: osteoarthritis (OA), gout (GT), painful diabetic peripheral neuropathy (pDPN), post-herpetic neuralgia (PHN), and fibromyalgia (FM). Patients newly diagnosed with 12 months of continuous medical and pharmacy benefits both before and after initial diagnosis (index date) were categorized as adherent, nonadherent, or unsure according to the guidelines-based algorithm using disease-specific pain medication classes grouped as first-line, later-line, or not recommended. Descriptive and multivariate analyses compared patient outcomes with algorithm-derived categorization endpoints.

resultsA total of 441,465 OA patients, 76,361 GT patients, 10,645 pDPN, 4,010 PHN patients, and 150,321 FM patients were included in the development of the algorithm. Patients found adherent to guidelines included 51.1% for OA, 25% for GT, 59.5% for pDPN, 54.9% for PHN, and 33.5% for FM. The majority (~90%) of patients adherent to the guidelines initiated therapy with prescriptions for first-line pain medications written for a minimum of 30 days. Patients found nonadherent to guidelines included 30.7% for OA, 6.8% for GT, 34.9% for pDPN, 23.1% for PHN, and 34.7% for FM.

conclusionThis novel algorithm used real-world pharmacotherapy treatment patterns to evaluate adherence to pain management guidelines in five chronic pain conditions. Findings suggest that one-third to one-half of patients are managed according to guidelines. This method may have valuable applications for health care payers and providers analyzing treatment guideline adherence.