Singh, Jasvinder A.

ObjectiveNumerous factors can impede or facilitate patients’ medication decision‐making and adherence to physicians’ recommendations. Little is known about how patients and physicians jointly view issues that affect the decision‐making process. Our objective was to derive an empirical framework of patient‐identified facilitators to lupus medication decision‐making from key stakeholders (including 15 physicians, 5 patients/patient advocates, and 8 medical professionals) using a patient‐centered cognitive mapping approach.MethodsWe used nominal group patient panels to identify facilitators to lupus treatment decision‐making. Stakeholders independently sorted the identified facilitators (n = 98) based on their similarities and rated the importance of each facilitator in patient decision‐making. Data were analyzed using multidimensional scaling and hierarchical cluster analysis.ResultsA cognitive map was derived that represents an empirical framework of facilitators for lupus treatment decisions from multiple stakeholders’ perspectives. The facilitator clusters were 1) hope for a normal/healthy life, 2) understand benefits and effectiveness of taking medications, 3) desire to minimize side effects, 4) medication‐related data, 5) medication effectiveness for “me,” 6) family focus, 7) confidence in physician, 8) medication research, 9) reassurance about medication, and 10) medication economics.ConclusionConsideration of how different stakeholders perceive the relative importance of lupus medication decision‐making clusters is an important step toward improving patient‐physician communication and effective shared decision‐making. The empirically derived framework of medication decision‐making facilitators can be used as a guide to develop a lupus decision aid that focuses on improving physician‐patient communication.

ObjectiveTo develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA).MethodsWe conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences.ResultsThe guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional.ConclusionThis RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

ObjectiveHow best to involve patients in the development of clinical practice guideline (CPG) recommendations is not known. We sought to determine the feasibility and value of developing CPG recommendations based on a voting panel composed entirely of patients, with the ultimate goal of comparing the patients’ recommendations to ones developed by a physician‐dominated voting panel on the same clinical questions.MethodsTen patients with rheumatoid arthritis completed 8 hours of training on evidence‐based medicine and guideline development. They constituted a voting panel and, with 2 American College of Rheumatology staff with expertise in CPG development and a physician facilitator, subsequently met at a face‐to‐face meeting to develop recommendations. They applied the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology to formulate recommendations on 18 questions for which there was evidence warranting moderate or high confidence.ResultsThe patient panel developed recommendations for 16 of the 18 questions; for the other 2, the panel thought there were insufficient data to support a recommendation. For 13 of the 16 questions, the patient panel recommended the same course of action as did the physician‐dominated panel. Differences were due to how the 2 panels valued the balance between benefits and harms.ConclusionPatient and physician‐dominated panels developed the same recommendations for most questions for which there was evidence warranting moderate to high confidence. Additional experiences are necessary to advance the evidence necessary to determine what panel composition is optimal to produce the best guidelines.

Objective To develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA). Methods We conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences. Results The guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional. Conclusion This RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

Objective Existing criteria for the classification of gout have suboptimal sensitivity and/or specificity, and were developed at a time when advanced imaging was not available. The current effort was undertaken to develop new classification criteria for gout. Methods An international group of investigators, supported by the American College of Rheumatology and the European League Against Rheumatism, conducted a systematic review of the literature on advanced imaging of gout, a diagnostic study in which the presence of monosodium urate monohydrate (MSU) crystals in synovial fluid or tophus was the gold standard, a ranking exercise of paper patient cases, and a multicriterion decision analysis exercise. These data formed the basis for developing the classification criteria, which were tested in an independent data set. Results The entry criterion for the new classification criteria requires the occurrence of at least 1 episode of peripheral joint or bursal swelling, pain, or tenderness. The presence of MSU crystals in a symptomatic joint/bursa (i.e., synovial fluid) or in a tophus is a sufficient criterion for classification of the subject as having gout, and does not require further scoring. The domains of the new classification criteria include clinical (pattern of joint/bursa involvement, characteristics and time course of symptomatic episodes), laboratory (serum urate, MSU‐negative synovial fluid aspirate), and imaging (double‐contour sign on ultrasound or urate on dual‐energy computed tomography, radiographic gout‐related erosion). The sensitivity and specificity of the criteria are high (92% and 89%, respectively). Conclusion The new classification criteria, developed using a data‐driven and decision analytic approach, have excellent performance characteristics and incorporate current state‐of‐the‐art evidence regarding gout.

While elective total hip arthroplasty (THA) for end-stage osteoarthritis (OA) improves pain, mobility function, and quality of life in most cases, a large proportion of patients suffer persistent muscle atrophy, pain, and mobility impairment. Extensive skeletal muscle damage is unavoidable in these surgical procedures, and it stands to reason that poor recovery and long-term mobility impairment among some individuals after THA is linked to failed muscle regeneration and regrowth following surgery and that local muscle inflammation susceptibility (MuIS) is a major contributing factor. Here we present results of two integrated studies. In study 1, we compared muscle inflammation and protein metabolism signaling in elective THA ( n = 15) vs. hip fracture/trauma (HFX; n = 11) vs. nonsurgical controls (CON; n = 19). In study 2, we compared two subgroups of THA patients dichotomized into MuIS (+) ( n = 7) or MuIS (−) ( n = 7) based on muscle expression of TNF-like weak inducer of apoptosis (TWEAK) receptor (Fn14). As expected, HFX demonstrated overt systemic and local muscle inflammation and hypermetabolism. By contrast, no systemic inflammation was detected in elective THA patients; however, local muscle inflammation in the perioperative limb was profound in MuIS (+) and was accompanied by suppressed muscle protein synthesis compared with MuIS (−) . Muscle from the contralateral limb of MuIS (+) was unaffected, providing evidence of a true inflammation susceptibility localized to the muscle surrounding the hip with end-stage OA. We suggest MuIS status assessed at the time of surgery may be a useful prognostic index for muscle recovery potential and could therefore provide the basis for a personalized approach to postsurgery rehabilitation.

ObjectiveTo assess the association of hospital procedure volume for total shoulder arthroplasty (TSA) with patient outcomes and complications.MethodsWe used the US Nationwide Inpatient Sample from 1998–2011 to study the association of hospital annual TSA procedure volume with patient characteristics and TSA outcomes, including discharge disposition (home versus inpatient facility), length of index hospitalization, postarthroplasty periprosthetic fracture, and revision. Annual hospital TSA volume was categorized as <5, 5–9, 10–14, 15–24, and ≥25 TSA procedures annually.ResultsPatients receiving TSA at higher volume hospitals were more likely to be female (P < 0.0001) and white (P < 0.0001). Compared to low volume hospitals (<5, 5–9, or 10–14 procedures annually), patients receiving TSA at higher volume hospitals (15–24 or ≥25 procedures annually) had significantly lower likelihood of being discharged to an inpatient medical facility: 16.5%, 13.4%, 13.0%, 12.7%, and 11.5%, respectively (P < 0.0001); hospital stay above the overall median: 46.6%, 40.4%, 36.6%, 34.4%, and 29.2%, respectively (P < 0.0001); postarthroplasty fracture: 1.2%, 0.8%, 0.9%, 0.6%, and 0.8%, respectively (P = 0.0004); blood transfusion: 8%, 7.1%, 6.7%, 7.1%, and 5.5%, respectively (P = 0.006); and TSA revision: 0.5%, 0.3%, 0.2%, 0.3%, 0.3%, respectively (P = 0.045).ConclusionIn this study, we found that higher annual hospital TSA volume was associated with better TSA outcomes in the US. These findings document the impact of annual hospital TSA volume on TSA outcomes. Patients, surgeons, and policy‐makers should be aware of these findings and take them into account in decision‐making, policy decisions, and resource allocation.

ObjectiveTo examine whether an underlying diagnosis of rheumatoid arthritis (RA) or osteoarthritis (OA) impacts the 90‐day readmission rates after total hip arthroplasty (THA) or total knee arthroplasty (TKA).MethodsWe analyzed prospectively collected data from an integrated health care system, Total Joint Replacement Registry, of adults with RA or OA undergoing unilateral primary THA or TKA during 2009–2011. Adjusted logistic regression models for 90‐day readmission were fit. Odds ratios with 95% confidence intervals (95% CIs) were calculated. Study year was an effect modifier for the outcome; therefore separate analyses were conducted for each of the 3 study years.ResultsOf the 34,311 patients, 496 had RA and 33,815 had OA. Comparisons of RA and OA patients, respectively, were 73% and 61% women, 45% and 70% white, and patients had a mean age of 61 versus 67 years (P < 0.001). Crude 90‐day readmission rates for RA and OA were 8.5% and 6.7%, respectively. The adjusted odds of 90‐day readmission increased from year to year for RA compared to OA patients, from 0.89 (95% CI 0.46–1.71) in 2009 to 1.34 (95% CI 0.69–2.61) in 2010, and to 1.74 (95% CI 1.16–2.60) in 2011. The 2 most common readmission reasons were joint prosthesis infection (10.2%) and septicemia (10.2%) in RA and joint prosthesis infection (5.7%) and other postoperative infection (5.1%) in OA.ConclusionRA is a risk factor for 90‐day readmission after primary THA or TKA. An increasing risk of readmissions noted in RA in 2011 is concerning and indicates that further studies should examine the reasons for this increasing trend.