Adherence and Outcomes with Urate-Lowering Therapy: A Site-Randomized Trial.

PURPOSE: The purpose of this study was to test a pharmacist-led intervention to improve gout treatment adherence and outcomes. METHODS: We conducted a site-randomized trial (n=1463 patients) comparing a 1-year, pharmacist-led intervention to usual care in patients with gout initiating allopurinol. The intervention was delivered primarily through automated telephone technology. Co-primary outcomes were the proportion of patients adherent (proportion of days covered ≥0.8) and achieving a serum urate <6.0 mg/dl at 1 year. Outcomes were reassessed at year 2. RESULTS: Patients who underwent intervention were more likely than patients of usual care to be adherent (50% vs 37%; odds ratio [OR] 1.68; 95% confidence interval [CI] 1.30, 2.17) and reach serum urate goal (30% vs 15%; OR 2.37; 95% CI 1.83, 3.05). In the second year (1 year after the intervention ended), differences were attenuated, remaining significant for urate goal but not for adherence. The intervention was associated with a 6%-16% lower gout flare rate during year 2, but the differences did not reach statistical significance. CONCLUSIONS: A pharmacist-led intervention incorporating automated telephone technology improved adherence and serum urate goal in patients with gout initiating allopurinol. Although this light-touch, low-tech intervention was efficacious, additional efforts are needed to enhance patient engagement in gout management and ultimately to improve outcomes.

Rationale and design of the randomized evaluation of an Ambulatory Care Pharmacist-Led Intervention to Optimize Urate Lowering Pathways (RAmP-UP) Study.

BACKGROUND: Despite the availability of effective therapies, most gout patients achieve suboptimal treatment outcomes. Current best practices suggest gradual dose-escalation of urate lowering therapy and serial serum urate (sUA) measurement to achieve sUA<6.0mg/dl. However, this strategy is not routinely used. Here we present the study design rationale and development for a pharmacist-led intervention to promote sUA goal attainment. METHODS: To overcome barriers in achieving optimal outcomes, we planned and implemented the Randomized Evaluation of an Ambulatory Care Pharmacist-Led Intervention to Optimize Urate Lowering Pathways (RAmP-UP) study. This is a large pragmatic cluster-randomized trial designed to assess a highly automated, pharmacist-led intervention to optimize allopurinol treatment in gout. Ambulatory clinics (n=101) from a large health system were randomized to deliver either the pharmacist-led intervention or usual care to gout patients over the age of 18years newly initiating allopurinol. All participants received educational materials and could opt-out of the study. For intervention sites, pharmacists conducted outreach primarily via an automated telephone interactive voice recognition system. The outreach, guided by a gout care algorithm developed for this study, systematically promoted adherence assessment, facilitated sUA testing, provided education, and adjusted allopurinol dosing. The primary study outcomes are achievement of sUA<6.0mg/dl and treatment adherence determined after one year. With follow-up ongoing, study results will be reported subsequently. CONCLUSION: Ambulatory care pharmacists and automated calling technology represent potentially important, underutilized resources for improving health outcomes for gout patients.

Patient and clinical characteristics associated with gout flares in an integrated healthcare system.

Gout flares have been challenging to identify in retrospective databases due to gout flares not being well documented by diagnosis codes, making it difficult to conduct accurate database studies. Previous studies have used different algorithms, and in this study, we used a computer-based method to identify gout flares. The objectives of this study were to identify gout flares in gout patients newly initiated on urate-lowering therapy and evaluate factors associated with a patient experiencing gout flares after starting drug treatment. This was a retrospective cohort study identifying gout patients newly initiated on a urate-lowering therapy (ULT) during the study time period of January 1, 2007-December 31, 2010. The index date was the first dispensed ULT prescription during the study time period. Patients had to be ≥18 years of age on index date, have no history of prior ULT prescription during 12 months before index date, and were required to have 12 months of continuous membership with drug benefit during pre-/post-index. Electronic chart notes were reviewed to identify gout flares; these reviews helped create a validated computer-based method to further identify patients with gout flares and were categorized into 0 gout flares, 1-2 gout flares, and ≥3 gout flares during the 12 months post-index period. Multivariable logistic regression was used to examine patient and clinical factors associated with gout flares during the 12-month follow-up period. There were 8905 patients identified as the final cohort and 68 % of these patients had one or more gout flares during the 12-month follow-up: 2797 patients (31 %) had 0 gout flares, 4836 (54 %) had 1-2 gout flares, and 1272 patients (14 %) had ≥3 gout flares. Using a multivariate regression analyses, factors independently associated with 1-2 gout flares and ≥3 gout flares versus no gout flares were similar, however, with slight differences, such as younger patients were more likely to have 1-2 gout flares and patients ≥65 years of age had ≥3 gout flares. Factors such as male gender, not attaining sUA goal, having ≥3 comorbidities, diuretics use, no changes in initial ULT dose, and not adhering to ULT all were associated with gout flares versus no gout flares. Using a new method to identify gout flares, we had the opportunity to compare our findings with the previous studies. Our study findings echo other previous studies where older patients, male, diuretics, having a greater number of comorbidities, and non-adherence are more likely to have more gout flares during the first year of newly initiating ULT. There is an unmet need for patients with gout to be educated and managed more closely, especially during the first year.

Effect of urate-lowering therapies on renal disease progression in patients with hyperuricemia.

OBJECTIVE: To evaluate the association between hyperuricemia and renal disease progression in a real-world, large observational database study. METHODS: We conducted a population-based retrospective cohort study identifying 111,992 patients with hyperuricemia (> 7 mg/dl) from a large medical group. The final cohort were ≥ 18 years old, urate-lowering therapy (ULT)-naïve, and had the following laboratory results available: at least 1 glomerular filtration rate (GFR) level before the index date and at least 1 serum uric acid (sUA) level and GFR in the followup 36-month period. The cohort was categorized into 3 groups: never treated (NoTx), ULT time receiving therapy of < 80% (< 80%), and ULT time receiving therapy of ≥ 80% (≥ 80%). Outcomes were defined as a ≥ 30% reduction in GFR from baseline, dialysis, or GFR of ≤ 15 ml/min. A subanalysis of patients with sUA < 6 mg/dl at study conclusion was performed. Cox proportional hazards regression model determined factors associated with renal function decline. RESULTS: A total of 16,186 patients met inclusion criteria. There were 11,192 NoTx patients, 3902 with < 80% time receiving ULT, and 1092 with ≥ 80% time receiving ULT. Factors associated with renal disease progression were age, sex, hypertension, diabetes, congestive heart failure, hospitalizations, rheumatoid arthritis, and higher sUA at baseline. Time receiving therapy was not associated with renal outcomes. Patients who achieved sUA < 6 mg/dl had a 37% reduction in outcome events (p < 0.0001; HR 0.63, 95% CI: 0.5-0.78). CONCLUSION: Hyperuricemia is an independent risk factor for renal function decline. Patients treated with ULT who achieved sUA < 6 mg/dl on ULT showed a 37% reduction in outcome events.

Using natural language processing and machine learning to identify gout flares from electronic clinical notes.

OBJECTIVE: Gout flares are not well documented by diagnosis codes, making it difficult to conduct accurate database studies. We implemented a computer-based method to automatically identify gout flares using natural language processing (NLP) and machine learning (ML) from electronic clinical notes. METHODS: Of 16,519 patients, 1,264 and 1,192 clinical notes from 2 separate sets of 100 patients were selected as the training and evaluation data sets, respectively, which were reviewed by rheumatologists. We created separate NLP searches to capture different aspects of gout flares. For each note, the NLP search outputs became the ML system inputs, which provided the final classification decisions. The note-level classifications were grouped into patient-level gout flares. Our NLP+ML results were validated using a gold standard data set and compared with the claims-based method used by prior literatures. RESULTS: For 16,519 patients with a diagnosis of gout and a prescription for a urate-lowering therapy, we identified 18,869 clinical notes as gout flare positive (sensitivity 82.1%, specificity 91.5%): 1,402 patients with ≥3 flares (sensitivity 93.5%, specificity 84.6%), 5,954 with 1 or 2 flares, and 9,163 with no flare (sensitivity 98.5%, specificity 96.4%). Our method identified more flare cases (18,869 versus 7,861) and patients with ≥3 flares (1,402 versus 516) when compared to the claims-based method. CONCLUSION: We developed a computer-based method (NLP and ML) to identify gout flares from the clinical notes. Our method was validated as an accurate tool for identifying gout flares with higher sensitivity and specificity compared to previous studies.

Direct medical expenditure associated with rheumatoid arthritis in a nationally representative sample from the medical expenditure panel survey.

OBJECTIVE: To quantify the incremental direct medical expenditure associated with rheumatoid arthritis (RA) in the US population from a payer's perspective. METHODS: A probability-weighted sample of adult respondents from the Medical Expenditure Panel Survey (2008) was used to identify a cohort of patients with RA and compared to a control cohort without RA. Annual expenditure outcomes, including total expenditure and subgroups related to pharmacy, office-based visits, emergency department visits, hospital inpatient stays, and residual expenditures were estimated. Differences between the RA and control cohort were adjusted for sociodemographic factors, employment status, insurance coverage, health behavior, and health status using a generalized linear model with log link and gamma distribution. Statistical inferences on difference in expenditures between RA and non-RA controls were based on nonparametric cluster bootstrapping using percentiles. RESULTS: The adjusted average annual total expenditure of the RA cohort in 2008 US dollars (USD) was $13,012 (95% confidence interval [95% CI] $1,737-$47,081), while that of the control cohort was $4,950 (95% CI $567-$17,425). The incremental total expenditure of the RA patients as compared to non-RA controls was $2,085 (95% CI $250-$7,822). RA patients also had a significantly higher pharmacy expenditure of $5,825 (95% CI $446-$30,998) that was on average $1,380 (95% CI $94-$7,492) higher as compared to the controls. The summated total incremental expenditure of all RA patients in the US was $22.3 billion (2008 USD). CONCLUSION: RA exerts considerable incremental economic burden on US health care, which is primarily driven by the incremental pharmacy expenditure.

Development and evaluation of a patient self-report case-finding method for rheumatoid arthritis.

OBJECTIVE: To describe the development and evaluation of a patient self-report case-finding method for rheumatoid arthritis (RA) not dependent on direct contact with the treating physicians. METHODS: The American College of Rheumatology criteria for RA diagnosis were adapted for patient self-report using a questionnaire, and alternative scoring algorithms were evaluated to balance case-finding sensitivity and specificity. Positive rheumatoid factor tests were used to identify 1053 individuals in 2 large healthcare organizations; 440 agreed to receive study materials. Case-finding results were validated by medical record review (MRR) for a random sample of 90 patients. Three scoring algorithms were compared with MRR for likelihood of RA diagnosis. Cases not classifiable by algorithm were flagged and reviewed by 2 expert physicians for likelihood of RA diagnosis. RESULTS: Pilot testing demonstrated that patients comprehended the questionnaire and were willing to answer the questions. Completed questionnaires were returned by 265 (60%) of the 440 patients contacted. Following expert physician review of 16 flagged cases in the 90-patient MRR subsample, the most accurate scoring algorithm demonstrated 80% sensitivity, 67% specificity, 74% accuracy, and 77% positive predictive value for detecting early RA. CONCLUSION: The case-finding method represents a promising tool for identifying RA patients, with potential application in research and quality-assurance activities. RELEVANCE: This case-finding method should be useful in research and quality-assurance efforts requiring identification of RA patients treated by all types of providers in healthcare organizations in which centralized laboratory data are available.