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Background Most patients with atrial fibrillation ( AF ) require rate control; however, the optimal target heart rate remains under debate. We aimed to assess rate control and subsequent outcomes among patients with permanent AF . Methods and Results We studied 2812 US outpatients with permanent AF in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation. Resting heart rate was measured longitudinally and used as a time‐dependent covariate in multivariable Cox models of all‐cause and cause‐specific mortality during a median follow‐up of 24 months. At baseline, 7.4% (n=207) had resting heart rate <60 beats per minute (bpm), 62% (n=1755) 60 to 79 bpm, 29% (n=817) 80 to 109 bpm, and 1.2% (n=33) ≥110 bpm. Groups did not differ by age, previous cerebrovascular disease, heart failure status, CHA 2 DS 2 ‐ VAS c scores, renal function, or left ventricular function. There were significant differences in race ( P =0.001), sinus node dysfunction ( P =0.004), and treatment with calcium‐channel blockers ( P =0.006) and anticoagulation ( P =0.009). In analyses of continuous heart rates, lower heart rate ≤65 bpm was associated with higher all‐cause mortality (adjusted hazard ratio [ HR ], 1.15 per 5‐bpm decrease; 95% CI , 1.01 to 1.32; P =0.04). Similarly, increasing heart rate >65 bpm was associated with higher all‐cause mortality (adjusted HR , 1.10 per 5‐bpm increase; 95% CI , 1.05 to 1.15; P <0.0001). This relationship was consistent across endpoints and in a broader sensitivity analysis of permanent and nonpermanent AF patients. Conclusions Among patients with permanent AF , there is a J‐shaped relationship between heart rate and mortality. These data support current guideline recommendations, and clinical trials are warranted to determine optimal rate control. Clinical Trial Registration URL : http://clinicaltrials.gov/ . Unique identifier: NCT01165710.

Abstract Therapy for polymyalgia rheumatica (PMR) varies widely in clinical practice as international recommendations for PMR treatment are not currently available. In this paper, we report the 2015 European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) recommendations for the management of PMR. We used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology as a framework for the project. Accordingly, the direction and strength of the recommendations are based on the quality of evidence, the balance between desirable and undesirable effects, patients’ and clinicians’ values and preferences, and resource use. Eight overarching principles and nine specific recommendations were developed covering several aspects of PMR, including basic and follow‐up investigations of patients under treatment, risk factor assessment, medical access for patients and specialist referral, treatment strategies such as initial glucocorticoid (GC) doses and subsequent tapering regimens, use of intramuscular GCs and disease modifying anti‐rheumatic drugs (DMARDs), as well as the roles of non‐steroidal anti‐rheumatic drugs and non‐pharmacological interventions. These recommendations will inform primary, secondary and tertiary care physicians about an international consensus on the management of PMR. These recommendations should serve to inform clinicians about best practices in the care of patients with PMR.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

ObjectiveTo evaluate the incidence and prevalence of juvenile idiopathic arthritis (JIA) in Olmsted County, Minnesota in 1994–2013 and trends in juvenile rheumatoid arthritis (JRA) in 1960–2013.MethodsCases of arthritis in 1994–2013 were identified by diagnosis code with medical chart review to confirm diagnosis separately for JIA and JRA. Overall incidence rates with 95% confidence intervals (95% CIs) were age and sex adjusted to the 2010 US white population. Comparisons were made with an earlier (1960–1993) cohort from this same population.ResultsSeventy‐one incident cases of JIA in 1994–2013 were identified, with an overall age‐ and sex‐adjusted incidence rate of 10.3 per 100,000 (95% CI 7.9–12.7). Forty‐two (59%) were female, with an incidence of 12.4 per 100,000 (95% CI 8.6–16.2), as compared to 8.3 per 100,000 (95% CI 5.2–11.3) in males. The most common subtype was oligoarthritis (63%). The mean ± SD age at diagnosis was 8.2 ± 5.3 years. The prevalence of JIA on January 1, 2000 and January 1, 2010 was 51.0 per 100,000 (95% CI 25.2–76.8) and 57.6 per 100,000 (95% CI 31.0–94.5), respectively. When the annual incidence of JRA was compared over time from 1960 to 2013, there was no significant change in incidence overall; however, the incidence decreased among females (P = 0.003). A cyclic pattern of incidence was observed, with peaks approximately every 10 years. Similar to the findings with regard to incidence, prevalence did not change overall, but decreased among females (P = 0.048). There were 4 deaths in the cohort of JRA patients diagnosed in 1960–2013; the standardized mortality ratio was 1.50 (95% CI 0.41–3.83).ConclusionIncidence of juvenile arthritis overall in Olmsted County, Minnesota has not changed significantly in the past 53 years. A consistent cyclic pattern was noted.