Structured Abstract

Background

Systemic lupus erythematosus (SLE), or lupus, is a rare chronic disease that primarily affects young women, particularly during peak reproductive years.^1,2 In the United States, 161 000 individuals have SLE, making it a rare disease, and if not treated appropriately, SLE has devastating consequences.³ Approximately 35% of SLE patients present initially with nephritis and 50% to 60% develop nephritis during the first 10 years.^4,5 Among racial/ethnic minorities lupus nephritis accounts for 2% of all end-stage renal disease in the United States.⁶ Lupus nephritis is significantly more prevalent and has worse outcomes (eg, >3 times higher mortality) in African American and Hispanic people than in White people.^{7,84,4,5,9-15} Thus, racial disparities in outcomes and high mortality make lupus an optimal disease for patient-centered outcomes research. While the 2012 American College of Rheumatology (ACR) treatment guidelines for lupus nephritis incorporated comparative effectiveness research (CER) data,¹⁶ a detailed analysis of comparative toxicity was not completed. Strong evidence exists that the use of immunosuppressive medications improves lupus outcomes when combined with glucocorticoids,¹⁶ and may reduce the cumulative glucocorticoid dose and associated side effects.^17-19 However, immunosuppressive drugs used to treat lupus nephritis (mycophenolate mofetil, cyclophosphamide, azathioprine, etc)¹⁶ differ from each other significantly in their toxicity (eg, cancer risk, infections), effects on fertility, safety during pregnancy, administration route, frequency of dosing, and cost. For example, mycophenolate is contraindicated for use in pregnancy, whereas clinicians consider azathioprine (a less expensive medication) a relatively safe option.²⁰ Given the differences in toxicity and cost across treatment options, we believed that it was critical to generate both patient perspectives^21-23 (aim 1) and CER data^24-26 (aim 2) related to immunosuppressive drugs to enable informed decision-making. In the current study, patients were asked to weigh risks vs benefits differently depending on their values, preferences, and knowledge of and aversion to risk, to enable development of a patient-centered decision aid.

Patients are often faced with difficult decisions about how to treat newly diagnosed and chronic lupus nephritis. Medication choice(s) depend not only on efficacy and drug-specific risk profiles, but also on cost. In addition, efficacy may differ by race and ethnicity. For example, a subgroup analysis of an open-label randomized controlled trial (RCT)—the Aspreva Lupus Management Study (ALMS)—found that, compared with White and Asian people, patients of other races and ethnicities with lupus nephritis responded less favorably to cyclophosphamide as induction therapy²⁷; and while not differing by race, the response to mycophenolate was superior to azathioprine as maintenance therapy for lupus nephritis.²⁸ If the existing CER data can be expanded (aim 1) and simplified using a decision aid, this may help patients with lupus nephritis through the complicated decision-making process regarding immunosuppressive drugs (aim 3). It should be noted that the example study above, ALMS, was limited by its being an open-label design and inclusion of relatively few African Americans.²⁷

Many minority patients do not receive quality health care, due in part to lower health literacy and numeracy,²⁹ poor physician-patient communication,^30-33 high medication nonadherence,³⁴ low socioeconomic status,^35,36 more barriers to health care access,^37-39 and more risk aversion to therapies.^40-45 Since health literacy is the ability to understand, engage in, and actively apply health information to improve health,⁴⁶ poor health literacy can contribute to poor health outcomes. In the United States, 41% of Hispanic, 24% of African American, and 9% of White people have inadequate health literacy skills, highlighting the disproportionate impact of low health literacy on racial minorities.⁴⁶ Numeracy is the ability to understand and use numbers in daily life and, when inadequate, is associated with poor health outcomes.^47,48 Compared with White people, studies have shown that African American people have lower numeracy, which may explain poor health outcomes in diseases such as diabetes,⁴⁹ and poor management of medications, as demonstrated in a study using a simulated HIV medication regimen.⁵⁰ Previous studies have shown poor medication adherence is common in patients with lupus nephritis, with racial minorities indicating less willingness to receive treatment for worsening lupus.^51-53 In qualitative studies, we described both facilitators²² and barriers²³ to medication decision-making by patients with lupus nephritis, and that a decision aid could overcome these challenges and present information tailored to health literacy and numeracy. A decision aid is 1 potential solution for patients with low literacy and numeracy but is currently lacking for lupus. A Cochrane systematic review⁵⁴ that included studies in patients with low literacy showed that a decision aid helped patients comprehend accurate expectations of possible benefits and harms, make choices that are more consistent with their informed values, and participate more in decision-making.⁵⁴ Patients who were involved in making medical decisions had better outcomes^55,56 and were more satisfied than patients who were not involved.^57-59 Many treatment decisions (for example, mycophenolate mofetil vs cyclophosphamide for induction) in lupus nephritis have no single “best” choice and are preference sensitive due to insufficient evidence about outcomes and/or the need to trade off known benefits and harms. Similarly, there are preference-sensitive decisions for maintenance therapy for lupus nephritis, including the choice between mycophenolate mofetil vs cyclophosphamide (following azathioprine failure), calcineurin inhibitors such as cyclosporine/tacrolimus vs cyclophosphamide (following azathioprine and mycophenolate mofetil failure), or cyclophosphamide vs azathioprine (following mycophenolate mofetil failure). For others, the benefits of a drug far outweigh its risks. For example, the use of glucocorticoids combined with immunosuppressives is superior to glucocorticoids alone for induction—that is, it is the dominant choice (not preference sensitive), yet the rate of immunosuppressive use by patients with lupus nephritis is low in the United States.⁶⁰ A patient decision aid could facilitate preference-sensitive decisions, such as the choice between immunosuppressives for induction or maintenance therapy for lupus nephritis. Decision aids have been used successfully for diabetes and osteoporosis,^61,62 rheumatoid arthritis, and hepatitis C.^63-66 Decision aids have succeeded in improving outcomes in other inflammatory arthritis conditions⁶⁷ when developed for the target population.⁶⁸ To our knowledge, no decision aid exists to assist patients with lupus nephritis in treatment decision-making. As part of aims 3 and 4, we tested the hypothesis that a decision aid that can help patients overcome literacy/numeracy challenges can improve patient decision-making and can lead to more informed choices by patients.

We conducted network meta-analyses (NMAs)^69-71 to assess the efficacy and harms/toxicity of lupus nephritis treatments to address the PCOR question “What are my options and what are the potential benefits and harms of those options?” Second, we aimed to develop an individualized decision aid for African American and Hispanic women with lupus nephritis, for whom the disease is often more severe and outcomes are worse, by focusing on culture-specific barriers. Our central research question was whether the use of an individualized, culturally tailored, patient-centered decision aid would reduce the conflict in patient decision-making and lead to more informed patient choice regarding immunosuppressives for the treatment of lupus nephritis. Our study aims were the following:

• Aim 1: To perform an evidence synthesis by systematic review and NMA. To assess comparative effectiveness of various immunosuppressives compared with each other and with glucocorticoids, corresponding to main induction and maintenance treatment decision points (published^24-26), using rigorous systematic review and NMA methods based on Agency for Healthcare Research and Quality (AHRQ) recommendations⁷² and the Cochrane handbook,⁷³ and building on the systematic review performed for the 2012 ACR lupus nephritis treatment recommendations.¹⁶ After assessing heterogeneity across trials⁷⁴ in patient characteristics, trial methodologies, and treatment protocols, we conducted a bayesian NMA^75-77 for prespecified outcomes.
• Aim 2: To elicit patients' perceptions of barriers to effective decision-making for lupus nephritis treatments, and their concerns regarding the risks and benefits of various immunosuppressives compared with each other and with glucocorticoids through the nominal group technique (NGT; results published previously^21-23).
• Aim 3: To develop and pilot test an interactive computerized decision aid based on CER data and formative work with patients—largely racial/ethnic minorities of low socioeconomic status—using an iterative process (a detailed protocol has been published⁷⁸).
• Aim 4: To conduct a multicenter, parallel arm, randomized trial comparing the usual care for decision-making against an individualized, culturally tailored patient decision aid in women—largely racial/ethnic minorities with low socioeconomic status—making lupus nephritis treatment decisions regarding immunosuppressives.

Participation of Patients and Other Stakeholders in Research Design, Conduct, and Dissemination of Findings

We identified our key patients, patient advocacy organization, physicians, researcher, and institutional stakeholders from various racial and socioeconomic backgrounds to assist with the development and execution of this project. Our patient stakeholders included Ms A (anonymized as per instructions; CEO of Company A; master's degree in business management; trained in psychometrics and outcomes); Ms B (anonymized; Master of Public Administration; past director of patient programs and community programs at a patient advocacy organization; bilingual). Our patient advocacy organization leaders included Sandra Raymond (president and CEO of research, Lupus Foundation of America [LFA]), Leslie Hanrahan (vice president of education and research, LFA), and Laura Marrow (director, partnerships liaison at the Arthritis Foundation [AF]). Physician and researcher stakeholders included experts in lupus treatment (Drs Chatham, Yazdany, Alarcón) and those with expertise in decision aid development and/or performing CER including NMA (Drs Fraenkel, Winthrop, Wells, Suarez-Almazor, Barton, Grossman, Saag, Singh, and Street). Institutions and organizations that served as key stakeholders and assisted with the study development and conduct included the University of Alabama (UAB) Department of Communication, University of California at San Francisco (UCSF), AHRQ-supported UAB CERTs, and The Eisenberg Center.

We recruited our 2 patient stakeholders (Ms A, Ms B) based on their expertise, disease experience, and experience serving as partners in patient-centered research. Ms A and Ms B represent a key group of people for whom the study results are particularly relevant. Ms A and Ms B played key roles in developing our study questions before submission, and throughout the funding period. Patient and other key stakeholder engagement had a significant impact on all aspects of the study quality and contributed heavily to the development of the decision aid. We held stakeholder and investigator teleconference meetings monthly on Thursdays to discuss key aspects of the project, from project inception through its conclusion. The stakeholder committee was charged with finalizing the risks and benefits that were incorporated into the decision aid, and all patient and clinician stakeholders engaged in a variety of activities, including formulation of the study design, the NMA, pilot testing of the intervention (ie, individualized decision aids in English and Spanish), and patient recruitment and retention in our trial. Previous experience of key stakeholders helped us meet new challenges during the study conduct. The monthly meetings allowed interactions of investigators with other key stakeholders. For example, stakeholders and investigators collaboratively reviewed and provided feedback about plans for focus group techniques and discussed the results of patient focus groups. We did not face any significant logistic or budgetary challenges in engaging patients and stakeholders. Specific examples of key stakeholder involvement are as follows:

• Both researchers and patient partners were involved in creating the manuscript related to facilitators of medication adherence and cognitive mapping (published²¹). Researchers presented a preliminary document for review at one of the recent stakeholder/investigator calls. All partners provided their feedback on the study design as a group and later independently grouped concepts/statements as part of the cognitive mapping. This iterative process was highly effective, allowing researchers/clinicians and patient representatives to hear the differing opinions of their peers. The resulting manuscript of cognitive mapping of medication decision-making facilitators addressed the different views of all 3 groups.²¹
• Brennda Caro led and Drs Suarez-Almazor, Alarcón, and Barton helped with the Spanish translations that led to the development of the Spanish version of the decision aid. Dr Fraenkel helped the researchers develop the content of our lupus nephritis decision aid for minorities based on the NGT results, using the rheumatoid arthritis decision aid as a template. We eliminated from the decision aid several side effects important only to physicians, but not patients (eg, low white cell counts and bone density values related to glucocorticoids).
• Patient research partners (Ms A and Ms B) helped us design the study and participated heavily in the development of the decision aid. Both participated in all of the activities of our study by regular teleconferences and email exchanges.

Additionally, we were able to convene a group of key stakeholders and investigators at the 2016 annual ACR meeting, at which panel discussions were held about further ideas on patient-centered research for lupus. The theme of the discussion was how to improve lupus care. Discussion involved key PCORI investigators, stakeholders, and attending rheumatologists from sites such as Ohio State University (OSU), and stakeholders shared ideas on initiatives started by their respective organizations for better lupus care. The panel concluded that a multicomponent intervention for the management of lupus was important from the patient perspective. Investigators have actively engaged and enrolled their patients in this research project. It has helped study team investigators understand how patient-centered research helps and empowers patients to make informed decisions about their care for lupus nephritis. Investigators were motivated to become a part of future outcomes research involving implementation of multifaceted interventions for management of this condition. We discussed the methods to disseminate this decision aid with the help of our stakeholder partners, LFA and AF, using one of the PCORI dissemination grants.

Overall, patients enrolled in this trial liked the decision aid tool and found it very informative. Most patients did not mind spending the extra time during their visits to participate in the study, noting it helped them understand the treatment options as well as the risks and benefits of those options. Patients shared positive experiences about the study participation, the research study process and conduct, and the technology used (iPad self-administered).

Methods

Study Intervention Development

The study intervention in this 2-arm trial was a decision aid in English or Spanish targeting patient knowledge and opinions about immunosuppressive drugs. The decision aid content was based on information generated through qualitative work with our target population,^21,22,79 and further pilot tested in the patient population of interest. The decision aid included information about benefits and harms that are relevant to patients with lupus nephritis. A published study protocol provides further details about the decision aid.⁷⁸ We designed the decision aid educational tool to improve understanding of a large amount of information and decision quality.

We achieved the lupus nephritis decision aid development in 3 steps: (1) nominal group technique in the target population to understand barriers and facilitators to treatment decision-making related to lupus; (2) performance of systematic review and network meta-analysis to derive the estimates of comparative effectiveness and harms of each treatment option for preference-sensitive decisions; and (3) pilot testing, iterative modification, and finalization of the decision aid in English and Spanish for the target population.

The following sections briefly describe the methods for the NGT^21-23 and the NMA and systematic reviews,^24-26 which we have reproduced from our peer reviewed publications with permission from the respective journals.^22,24 Detailed methods of the NGT and the NMA and systematic reviews for other outcomes are available in listed publications.

NGT to Define the Barriers and Facilitators of Medication Decision-Making in Lupus Nephritis

We used the NGT with patients with lupus to assess the barriers and facilitators to medication decision-making for the treatment of lupus.^22,79 The NGT is a structured process to elicit ideas from participants for a formative assessment. We conducted 8 NGT meetings in English at 2 medical centers, UAB and UCSF, moderated by an expert NGT researcher. Participants responded separately to 2 questions: (1) Barriers: “What sorts of things make it hard for people to decide to take the medicines that doctors prescribe for treating their lupus kidney disease?”⁷⁹ (2) Facilitators: “What sorts of things make it easier for people to decide to take the medicines that doctors prescribe for treating their lupus kidney disease?”²² In response to each of the 2 questions, patients nominated, discussed, and prioritized (1) barriers and (2) facilitators to medication decisional processes for lupus. In the section below, methods from our previous publication²² have been replicated (with permission from the publisher).

We recruited patients from the lupus clinics at the University of Alabama at Birmingham and the University of California at San Francisco. All patients met American College of Rheumatology classification criteria for systemic lupus erythematosus and had a clinical diagnosis of lupus nephritis (based on renal biopsy and/or laboratory tests).

We convened 8 NGT meetings including lupus nephritis patients who had received treatment at UAB or UCSF lupus clinics. An expert NGT researcher (R.S.) conducted and moderated all NGT meetings in English between February and April 2014. The Institutional Review Boards at UAB and UCSF approved this study. All patients provided written, informed consent.

The NGT meeting is a facilitated data collection activity, structured to promote even and equal subject participation by minimizing the loss of information. Evidence shows that the NGT, when used correctly, elicits a greater volume of novel and higher-quality responses to a carefully articulated question than less structured group data collection approaches such as focus groups and brainstorming.^80,81 Moreover, by using the verbatim responses that are concisely documented on a flipchart as participants present them to the group, the NGT eliminates a potential source of investigator-induced interpretive bias resulting from transcribing and coding audio or video recordings.

The purpose of the NGT meetings was to tap into patients' unique insights, knowledge, and lived experiences to identify different factors that facilitated their decision-making process regarding prescribed lupus medications. The NGT leader (R.S.), along with a team member (H.Q.), started the sessions with a brief explanation of the purpose and the NGT process. Patients then worked independently for about 5 minutes to develop their own lists of brief statements/phrases in response to the NGT question.

Patients were encouraged to think broadly about the types of factors that enhanced the likelihood of deciding to take the medications prescribed for their condition. This ensured that each panel generated a wide array of responses. After 5 minutes of working on their own, patients were invited to present their responses to the group. To promote open disclosure, increase response volume, and ensure all patients had an equal opportunity to present responses, we used a “round-robin” participation format. This format involved having each patient, in turn, articulate a single response without providing any rationale, justification, or explanation for his or her response and without discussion or debate from other members in the group. All responses were immediately recorded verbatim on a flipchart to help participants recollect previously nominated responses. We continued until no further responses could be generated. All responses were then discussed in a nonevaluative fashion to ensure that they were understood from a common perspective and, potentially, to obtain additional insights.⁸²

Patients were asked to silently review the full list of responses generated during the meeting and to independently select 3 facilitators that they perceived as the most influential to their decision-making regarding lupus nephritis medication. Patients recorded their selected responses on index cards and prioritized the influence of each of their selections from 1 (least influential) to 3 (most influential). The votes reflecting these priorities were tabulated across patients in each NGT panel to determine the perceived relative influence of medication decision-making facilitators and the level of agreement among patients regarding these perceptions.

A brief questionnaire was administered to the patients at the conclusion of each NGT meeting to obtain basic demographic data, education level, and disease duration, and whether the patient needed assistance in reading materials. Data from this questionnaire were analyzed at the group level and not linked with individual responses generated during the NGT meetings.

NMA and Systematic Review of Medications Used for the Treatment of Lupus Nephritis

We performed the NMA and systematic review for several benefit and harm outcomes, which we identified from NGT with patients with lupus nephritis. We published the results in 3 peer reviewed manuscripts focused on the following outcomes: (1) serious infections²⁵; (2) malignancy, herpes zoster, gastrointestinal side effects (gastrointestinal upset, diarrhea, etc), nausea, alopecia, mycobacterial infections, hyperglycemia/diabetes, avascular necrosis/osteonecrosis, mortality, amenorrhea, cytopenia, and urinary bladder toxicity (including hemorrhagic cystitis and hematuria)²⁶; and (3) renal remission/response, renal relapse/flare, amenorrhea/ovarian failure, and cytopenia.²⁴ In the section below, we describe the methods from our previous publication,²⁴ which have been replicated from the publication (with permission from the publisher). We chose to describe methods for only a select few outcomes, rather than all outcomes, since (1) methods were similar (or in some cases the same) for other outcomes with minor exceptions, as noted below; and (2) most estimates for the outcomes described in the paper²⁴ were used in the patient decision aid. In the other 2 papers describing outcomes from the systematic reviews,^25,26 rankograms were not presented.

We used rigorous methods for the systematic review and NMA based on the Agency for Healthcare Research and Quality (AHRQ) recommendations,⁷² the Cochrane handbook,⁸³ and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Institutional Review Board at the University of Alabama at Birmingham approved the study. The need for informed consent was waived for this systematic review, since no human subjects were involved. The study protocol was registered in PROSPERO, CRD42016032965 (http://www.crd.york.ac.uk/PROSPERO/).

This systematic review included randomized controlled trials or controlled clinical trials of immunosuppressive drugs or corticosteroids for lupus nephritis, published in English, that reported any safety or efficacy outcome. Included medications were corticosteroids ([PRED], cyclophosphamide [CYC], mycophenolate mofetil [MMF], azathioprine [AZA], cyclosporine, tacrolimus, or rituximab). Belimumab studies could not be included in this systematic review since these studies included patients with lupus, and only a small proportion had active lupus nephritis. A Cochrane systematic review of belimumab for lupus is underway.⁸⁴ There were no restrictions regarding medication dose or the duration of medication use.

Experienced librarians (J.J. and T.R.) updated 2 systematic reviews^16,85 from their search end dates (August 2010 and April 2012, respectively) to September 2013 using the PubMed database. The search used the following terms: (Lupus[text word] OR “Lupus Vulgaris”[MeSH] OR “Lupus Erythematosus, Cutaneous”[MeSH] OR “Lupus Erythematosus, Systemic”[Mesh]) AND (“Kidney Diseases”[MeSH] OR nephropath*[text word] OR Transplants[MeSH] OR Transplantation[MesH] OR transplantation[subheading] OR transplant*[text word] OR “Kidney”[Mesh] OR Kidney*[text word] OR Renal*[text word] OR “End Stage Renal Disease”[text word] OR ESRD[text word] OR Glomerulonephr*[text word] OR “GN”[text word] OR “crescentic GN”[text word]) NOT (“animals”[MeSH] NOT “humans”[MeSH]).

Raw data abstracted for the ACR lupus nephritis guidelines systematic review¹⁶ were obtained (courtesy Dr Jennifer Grossman [J.G.], see acknowledgment section), or were abstracted from the Revman tables of the Cochrane Systematic Review.⁸⁵ A librarian (C.H.) also performed a search for all lupus trials for harms (for conditions other than lupus nephritis) in PubMed and SCOPUS from inception to February 2014, based on an a priori assumption that treatment-related harms may not depend on whether kidney is involved. Examination of data from this search revealed little additive data for harms, but added clinical heterogeneity related to differences in patient population. Therefore, after careful consideration of pros and cons, we decided not to use these data in analyses.

We defined the PICO (patient, intervention, comparator, outcome) for our systematic review and NMA as follows:

• P: Adults 18 years or older, meeting the 1987 American College of Rheumatology Classification criteria for systemic lupus erythematosus,⁸⁶ who have lupus nephritis.
• I: Immunosuppressant drug alone or in combination with other immunosuppressant drugs or biologics (such as rituximab) or corticosteroids. We categorized medication doses as low dose (LD), standard dose (SD), or high dose (HD).
• C: Placebo or another immunosuppressive with or without biologic.
• O: Benefit and harm outcomes (renal remission/response, renal relapse/flare, fertility, bone marrow suppression), defined as follows.

We assessed benefits based on 2 composite outcomes: (1) renal remission/response (indicating success of therapy), including complete renal remission,²⁷ partial renal remission,^87,88 and renal response; and (2) renal relapse/flare (indicating failure of therapy), including renal relapse⁸⁹ and renal flare. We assessed harms based on 2 composite outcomes: ovarian failure/amenorrhea, including ovarian failure and amenorrhea; and (2) bone marrow toxicity (cytopenia), including leucopenia.

Two trained abstractors (A.O., A.B.) independently reviewed abstracts and titles, abstracted data in duplicate directly into Microsoft Excel sheets, and assessed the risk of bias according to the Cochrane risk of bias tool.⁹⁰ We examined the following domains as low or high risk of bias or unclear risk (lack of information or uncertainty about potential for bias): randomization sequence generation, allocation sequence concealment, blinding of participants, personnel and outcome assessors, incomplete outcome data (primary outcome data reporting, dropout rates and reasons for withdrawal, appropriate imputation of missing data, an overall completion rate ≥80%), and selective outcome reporting and other potential threats to validity (considering external validity, eg, relevant use of cointerventions, bias due to funding source). An adjudicator (J.S.) resolved any disagreements not resolved by consensus. An expert rheumatologist (J.S.) and an expert in lupus (J.G.) examined for similarity of studies before performing evidence synthesis by the examination of similarity of study population and interventions.

We designated doses as follows: (1) CYC: SD: 0.5 through 1.0 g/m² intravenously (IV) q month for 6 to 12 months or 2 through 2.5 mg/kg orally (PO) daily over 3 to 6 months; HD: dose higher or duration longer than SD; LD: dose lower or duration shorter than SD, including the EURO-lupus dose, 500 mg IV q 14 days for 6 doses (mean 3 g); (2) AZA: SD: 1 to 3 mg/kg PO daily; HD: >3 mg/kg PO daily; (3) LEF: 1 mg/kg PO qd for 3 d then 30 mg PO qd for 6 months; and (4) PRED: SD: prednisone/methylprednisolone 1 g/m² IV q month for 6 months or prednisone 60 mg PO qd 1 to 3 months then tapered over 3 to 12 months as tolerated; HD: prednisone/methylprednisolone 1 g/m² qd IV 3 times, then 1 dose q month for 1 year or prednisone 1 mg/kg daily for 4 to 8 weeks (or unspecified period). When dose is not specified, medication dose is the standard dose.

We used bayesian mixed treatment comparison (MTC) meta-analyses^75-77 to assess the comparative effectiveness of one immunosuppressive drug vs another and immunosuppressive drugs vs corticosteroids. We conducted a bayesian MTC meta-analysis using a binomial likelihood model using WinBUGS software (MRC Biostatistics Unit), which allows inclusion of data from multiarm trials.^91,92 We conducted random-effects NMA and assessed model fit and the choice of model (random vs fixed effects) based on the assessment of the deviance information criterion and the comparison of residual deviance to the number of unconstrained data points.^91,93

We assigned vague priors, such as N (0, 100²) for basic parameters throughout⁹¹ and informative priors for the variance parameter based on Turner et al.⁹⁴ We evaluated the model diagnostics including trace plots and the Brooks-Gelman-Rubin statistic to ensure model convergence.^91,95 We fit 3 chains in WinBUGS for each analysis, with 40 000 iterations, and a burn-in of 40 000 iterations.^95,96 Both MTC and traditional meta-analysis require studies to be sufficiently similar in order to pool their results. We investigated heterogeneity, where warranted, with subgroup analyses and meta-regressions.^92,97 We examined consistency-inconsistency plots for evidence of inconsistency, and chose the appropriate model for our analyses. We obtained point estimates using odds ratios (ORs) and 95% credible intervals (CrI) using Markov Chain Monte Carlo methods. We conducted transformation of the OR to relative risk and risk difference to allow ease of interpretation for clinicians and patients. We assessed the quality of evidence as recommended in a recent study.⁹⁸

We performed sensitivity analysis by limiting analyses to partial/complete remission rather than combining this with renal response for the composite renal remission/response. We constructed staircase diagrams, another pictorial way to visualize comparisons of various treatments against each other. We constructed rankograms to model the probabilities of the treatment rankings, representing the best to the worst ranks.

Study Intervention Content Finalization and Pilot Testing

We performed iterative testing of the decision aid content. Examples from various sections of the decision aid are provided in Figures 1 to 6. The decision aid for each scenario provided information about lupus in general and how lupus affects kidneys, general information about the medication (medication formulation, route of administration, costs of medication, dosage), information about comparative risks and benefits about the 2 immunosuppressive drugs being compared, a final summary of the information provided, and information on patient resources and patient support groups. The decision aid also provided 3 optional modules: benefits and side effects of using glucocorticoids, lupus and pregnancy, and lupus and breast-feeding. We ensured the cultural sensitivity of the tool by developing the content and the structure of the tool based on our qualitative work with predominantly African American and Hispanic women (English- and Spanish-speaking) with lupus nephritis (but also White and Asian women)^21,22,79; pilot-testing both English- and Spanish-language versions in this target group; making the decision aid understandable to people with low literacy, numeracy, and graphical literacy; and keeping the decision aid at a sixth- to eight-grade reading level. The control intervention consisted of the American College of Rheumatology pamphlet with information about lupus, lupus nephritis, and its treatment.

Figure 1

Screenshot of Lupus Nephritis Decision Aid: Introduction Section.

Figure 2

Screenshot of Lupus Nephritis Decision Aid: Example of Comparison of a Benefit (Prevention of Kidney Failure) of Treatment Choices.

Figure 3

Screenshot of Lupus Nephritis Decision Aid: Example of Comparison of a Harm/Toxicity (Shingles) Related to the Treatment Choices for Lupus Nephritis.

Figure 4

Screenshot of Lupus Nephritis Decision Aid: Example of Potential Concerns Related to the Use of Immunosuppressive Drugs During Pregnancy.

Figure 5

Screenshot of Lupus Nephritis Decision Aid: Example of Other Concerns Related to Medications Based on Qualitative Work With Patients With Lupus Nephritis.

Figure 6

Screenshot of Lupus Nephritis Decision Aid: An Example of a Summary Slide.

Outcome Measures

Coprimary Outcome Measures

Decisional Conflict Scale, low-literacy version

The Decisional Conflict Scale (DCS) is a patient self-administered, validated measure of decisional conflict, most commonly used as the primary outcome in RCTs of decision aids.^110,111 The low literacy version consists of 10 items with 3 response categories (yes, unsure, no) with overall scores ranging from 0 (no decisional conflict) to 100 (extreme decisional conflict), available in English and Spanish.¹¹² We scored responses as yes = 0; unsure = 2; no = 4. We summed 10 items and multiplied them by 2.5 to provide a score ranging from 0 to 100. Decisional conflict represents a state of uncertainty about a choice or course of action and is more likely in situations involving high-stakes choices with important potential gains and losses, value tradeoffs in choosing one selection or one course of action (vs the alternative), or uncertain outcomes. We chose the DCS scale based on our intervention and the formative work showing significant doubts and decisional conflict regarding immunosuppressive drugs in lupus patients.

Informed choice

We assessed informed choice by using a validated multidimensional model of informed choice^104,105 that individually assesses and then combines 3 constructs: values regarding immunosuppressive drugs, knowledge about immunosuppressive drugs, and treatment choices. We assessed informed choice after each patient had viewed the decision aid or the ACR pamphlet at the baseline study visit before any treatment decision-making. We assessed values with a list consisting of patients' views regarding immunosuppressive drugs as a treatment option and their side effects generated by our study team based on patient concerns regarding immunosuppressive drugs. The values statements consisted of both positive and negative values about immunosuppressive drugs, mixed in a random order, with responses ranging from strongly disagree to strongly agree. We scored positive and negative value statements with appropriate signs (+ or −) and aggregated this into a total score. A higher total score indicated more positive values regarding using immunosuppressive drugs, and we used a median score to classify values as positive vs negative regarding using immunosuppressive drugs. We assessed knowledge related to immunosuppressive drugs for lupus nephritis using 20 questions. We considered patients to have adequate knowledge if they answered at least 75% of questions correctly. We assessed choice based on response to a single item on a nominal scale with anchors of “start vs don't start immunosuppressives” and “uncertain” in the middle and 15 circles, asking each patient's choice in response to the question, “If your doctor asked you right now to make a choice about immunosuppressives, please show where you would be on the scale below by choosing a circle below.” Informed choice referred to a choice that is based on accurate knowledge and is concordant with one's values. A higher proportion of patients with an informed choice is optimal. Table 1 provides details for this outcome. We performed a sensitivity analysis for informed choice by reclassifying subjects according to net score (positive or negative) on value statements, comparing value statements favorable toward immunosuppressives with those not favorable, rather than the median score; we classified those with a net positive score as favoring immunosuppressives and those with a net negative score as against immunosuppressives.

Table 1

Classification Criteria for Informed Choice.

Secondary Outcome Measures

Control preferences scale

This validated measure assessed patient participation in decision-making for only those patients with a current flare. The scale assessed how much decision-making control a patient would like to have vs control actually experienced by each patient. It distinguishes between those who feel involved in the decision vs those who do not.^106-108 The measure included 5 responses corresponding to 5 control options: active, active shared, collaborative, passive shared, and passive. We categorized these roles as active (combining active and active shared), collaborative, or passive (combining passive and passive shared).^113-115 We examined the concordance between the desired and the actual role played by each patient with a current lupus nephritis flare.

Patient-physician communication and care processes

We used the IPC-SF, an 18-item validated patient-reported measure of patient-physician communication and care processes, available in English and Spanish.^109,116-119 The IPC-SF score ranges from 18 (worst) to 90 (best); higher scores represent better patient-physician communication and care processes.

Analysis of an audio-taped physician-patient interaction

For the current flare patients only, we recorded the patient-physician discussion about immunosuppressives and used the Active Patient Participation Coding Scheme, a validated instrument, to assess indicators and facilitators of patient participation.¹²⁰ We coded 3 types of speech acts (question asking, assertive responses, and expressions of concern) as active patient participation, because they may influence a doctor's behavior as well as the content and structure of the consultation.^121-124 We coded physician communication using speech acts such as supportive talk and partnership building. We summed these units (range 0 to infinity) for each interaction to create a frequency for the degree of active participation.

Acceptability and feasibility of the decision aid

We assessed information quality and quantity, presentation style, and usefulness of the decision aid using a validated acceptability survey⁶² on a 4-point scale ranging from excellent to poor. We assessed feasibility of the decision aid vs the pamphlet and the study procedures with a self-administered questionnaire.¹²⁵ Patients rated the feasibility of the decision aid vs the pamphlet by responding to the statement “the education guide was easy to use” on a 5-point Likert scale (strongly agree to strongly disagree).

Clinically meaningful difference

We considered a 10% difference between study arms in the proportion of patients achieving a favorable or unfavorable outcome as clinically meaningful. We designated a 5% difference in proportions as “possibly” clinically meaningful.

Results

We conducted a systematic review, meta-analysis, and NMA to assess the comparative efficacy and harms of immunosuppressive drugs (aim 1); performed a nominal group technique with primarily minority women with lupus nephritis to prioritize barriers and facilitators to immunosuppressive drug decision-making (aim 2); and iteratively modified and finalized our decision aid by testing it in women with lupus nephritis (aim 3).

Detailed results of the NMA and systematic review have been published.^24-26 This study provided comparative estimates for benefits and harms of immunosuppressive drugs in lupus nephritis for our decision aid. We provide a brief summary below. We included 65 RCTs that met the inclusion and exclusion criteria. Significantly lower risk of end-stage renal disease (ESRD; 17 studies) was seen with cyclophosphamide (CYC; odds ratio 0.49, 95% credible interval 0.25-0.92) or CYC + azathioprine (AZA; OR 0.18, 95% CrI 0.05-0.57) compared with standard-dose glucocorticoids, and with high-dose (HD) CYC (OR 0.16, 95% CrI 0.03-0.61) or CYC + AZA (OR 0.10, 95% CrI 0.03-0.34) compared with high-dose glucocorticoids. High-dose glucocorticoids were associated with higher risk of ESRD compared with CYC (OR 3.59, 95% CrI 1.30-9.86), AZA (OR 2.93, 95% CrI 1.08-8.10), or mycophenolate mofetil (MMF; OR 7.05, 95% CrI 1.66-31.91).²⁶ No differences were noted between medications for the risk of malignancy (15 studies). The risk of herpes zoster vs glucocorticoids (17 studies) was as follows, OR (95% CrI): MMF, 4.38 (1.02-23.87); CYC, 6.64 (1.97-25.71); tacrolimus [TAC], 9.11 (1.13-70.99); and CYC + AZA, 8.46 (1.99-43.61). We concluded that renal benefits and the risk of herpes zoster were higher for immunosuppressive drugs vs glucocorticoids. In another systematic review, we compared the risk of serious infections with various drugs in lupus nephritis (32 RCTs with 2611 patients provided data). Tacrolimus was associated with significantly lower risk of serious infections compared with glucocorticoids, CYC, MMF, and AZA, with odds ratios (95% CrI) of 0.33 (0.12-0.88), 0.37 (0.15-0.87), 0.34 (0.18-0.81), and 0.32 (0.12-0.81), respectively.²⁵ We also found that MMF treatment followed by AZA (MMF-AZA sequential treatment) was associated with significantly lower risk of serious infections compared with CYC LD, CYC HD, CYC-AZA, or HD glucocorticoids, with odds ratios (95% CrI) of 0.09 (0.01-0.76), 0.07 (0.01-0.54), 0.14 (0.02-0.71), and 0.03 (0.00-0.56), respectively. Sensitivity analyses confirmed these findings. We concluded that tacrolimus and MMF-AZA combination each were associated with a lower risk of serious infections compared with other immunosuppressive drugs or glucocorticoids for lupus nephritis. In another analysis, we assessed the rates of renal remission/response (37 trials; 2697 patients), renal relapse/flare (13 studies; 1108 patients), amenorrhea/ovarian failure (8 trials; 839 patients), and cytopenia with immunosuppressive drugs (16 trials; 2257 patients). CYC LD and CYC HD were less likely than MMF-AZA, CYC LD, CYC HD, and plasmapheresis less likely than cyclosporine to achieve renal remission/response.²⁴ TAC was more likely than CYC LD to achieve renal remission/response. MMF and CYC were associated with lower odds of renal relapse/flare compared with glucocorticoids and MMF was associated with a lower rate of renal relapse/flare than AZA. CYC was more likely than MMF and glucocorticoids to be associated with amenorrhea/ovarian failure. Compared with MMF, CYC, AZA, CYC LD, and CYC HD were associated with a higher risk of cytopenia. We used these estimates in the decision aid tool to depict the comparative benefits and harms of various immunosuppressive medications, as applicable to a given comparison/scenario for induction or maintenance therapy.

We incorporated themes generated from NGTs into our decision aid. Since we recruited a similar target patient population for the NGT as we aimed for in the trial (racially/ethnically diverse with diverse socioeconomic status and literacy level), themes and content generated were culturally appropriate. An NGT expert moderated 8 patient group meetings at Birmingham and San Francisco, in which 52 women with lupus nephritis participated (27 African American, 13 Hispanic, and 12 Caucasian). The average (SD) age was 40.6 (13.3) years, and the mean (SD) disease duration was 11.8 (8.3) years; 36.5% had at least some college education, and 55.8% had difficulty in reading health materials (aim 1).

Detailed results of the NGT have been published.^21-23 Briefly, participants (n = 52) responded to the question “What sorts of things make it easier for people to decide to take the medicines that doctors prescribe for treating their lupus kidney disease?” and generated 280 decision-making facilitators.²² Of these, 102 (36%) facilitators were perceived by patients as having relatively more influence in decision-making processes than others. Prioritized facilitators included effective patient-physician communication regarding benefits and harms, patient desire to live a normal life and improve quality of life, patient concern for their dependents, experiencing benefits and few/infrequent/no harms with lupus medications, and medication affordability (aim 2). In a similar NGT (n = 51), participants with lupus nephritis responded to the question “What sorts of things make it hard for people to decide to take the medicines that doctors prescribe for treating their lupus kidney disease?”²³ The most salient perceived barriers were known/anticipated side effects (15.6%), medication expense/ability to afford medications (8.2%), and the fear that the medication could cause other diseases (7.8%). We discussed these barriers in detail and incorporated them into the development of the decision aid. These barriers and facilitators guided the content of the decision aid. For example, we included slides on medication cost in the decision aid and focused the content on the key side effects of these medications identified by patients during the NGT to be most relevant to women with lupus nephritis. We also focused benefits on relevant aspects (dialysis, patient-relevant benefits) identified by patients in the NGT, rather than benefits and harms that are relevant to providers only, such as decreased white cell counts and improvement in laboratory measures of proteinuria. We also included 3 optional sections, 1 each on glucocorticoids, pregnancy, and breast-feeding, based on the additional concerns identified from the NGT. After finalization of our decision aid content, we tested it iteratively in 19 subjects with lupus (mean age 39 years). We made edits, word substitutions, and corrections as suggested by patients. Three patients reviewed a pre-pilot test version of the decision aid website for color and the ease of use. They suggested a change in the background color and the exclusion of a navigation bar; we made both changes before the finalization of the electronic version of the decision aid (aim 3).

We performed a randomized trial of the decision aid vs the pamphlet (aim 4). In the section below, we provide results using tables adapted from clinicaltrials.gov as per instructions including the CONSORT flow chart (Figure 7) and a study flow diagram showing the study procedures (Figure 8).

Figure 7

Study Flow Chart CONSORT diagram.

Figure 8

Study Row Diagram Showing Each State of the Randomized Trial.

Primary Outcome

View in own window

Title:	Change From Baseline in Decisional Conflict Scale Scores (Reduction)
▼ Description	Patient self-administered, validated measure of decisional conflict, most commonly used as the primary outcome in RCTs of decision aids (change score). The score ranges from 0 (no decisional conflict) to 100 (extreme decisional conflict). Decisional conflict represents a state of uncertainty about a choice or course of action and is more likely in situations involving high-stakes choices with important potential gains and losses, value tradeoffs in selecting a choice or a course of action (vs the alternative), or uncertain outcomes.
Time Frame	Baseline and after viewing the decision aid or the standard pamphlet on the same visit as the intervention (preferred) but before treatment decision-making (usually within 1 week)

• Outcome Measure Data ✓
• Analysis Population Description

All participants who received either the decision aid or pamphlet

View in own window

Arm/group title	Decision aid	Pamphlet
▼ Arm/group description	Participants received decision aid tool	Participants received the standard ACR pamphlet
Overall No. of participants analyzed	151	147
Mean reduction (SD) Unit of measure: units on a scale	21.80 (30.89)	12.69 (24.41)

• Statistical Analysis 1 ✓

View in own window

Statistical analysis overview	Comparison group selection	Decision aid, pamphlet
	Comments	[Not specified]
	Type of statistical test	Superiority or other (legacy)
	Comments	[Not specified]
Statistical test of hypothesis	P value	.005
	Comments	[Not specified]
	Method	t test, 2 sided
	Comments	[Not specified]

The effect size (Cohen d) for change in decisional conflict was 0.33. The proportion of patients with unresolved clinically significant decisional conflict (score ≥25) postintervention was greater in the pamphlet than in the decision aid group—44.2% vs 22.5% (P < .001). Median (IQR) was 10 (45) for the decision aid group and 10 (25) for the pamphlet group.

Sensitivity Analysis: Change in DCS scores was almost normally distributed (see Figure 9). For sensitivity analysis, we used the Wilcoxon rank sum test, a nonparametric test, to compare change in DCS (in case of deviation from a normal distribution). We found that the decision aid group had a significantly larger change in DCS compared with the pamphlet group using the Wilcoxon rank sum test (P = .04).

Figure 9

Distribution of Change in DCS Scores.

Discussion

Conclusions

This multicenter randomized study tested an individualized, culturally tailored, computerized patient decision aid that addressed treatment induction and the failure of maintenance therapy in patients with a flare of lupus nephritis. The decision aid was superior to the standard ACR lupus pamphlet for the primary outcome: reducing decisional conflict about immunosuppressive drugs in women from diverse racial/ethnic and socioeconomic backgrounds. The lupus nephritis decision aid had higher acceptability and was easier to understand than the standard ACR lupus pamphlet for patients.

The major strengths of our study were a randomized design as well as the negligible number of patients without outcome data. The shortcoming was the lack of evidence about how long the effect of the decision aid persisted. Based on these findings, we conclude that the decision aid is ready for testing in additional settings and with further follow-up to measure the persistence of the knowledge gained. This tool, available in English and Spanish, can facilitate and improve shared decision-making for lupus nephritis treatments in clinical practice and should lead to higher patient satisfaction and engagement. Whether it might improve disease outcomes remains to be seen.

References

1.

Weckerle CE, Niewold TB. The unexplained female predominance of systemic lupus erythematosus: clues from genetic and cytokine studies. Clin Rev Allergy Immunol. 2011;40(1):42-49. doi:10.1007/s12016-009-8192-4. [PMC free article: PMC2891868] [PubMed: 20063186] [CrossRef]

2.

Lopez P, Mozo L, Gutierrez C, Suarez A. Epidemiology of systemic lupus erythematosus in a northern Spanish population: gender and age influence on immunological features. Lupus. 2003;12(11):860-865. doi:10.1191/0961203303lu469xx. [PubMed: 14667105] [CrossRef]

3.

Helmick CG, Felson DT, Lawrence RC, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part I. Arthritis Rheum. 2008;58(1):15-25. doi:10.1002/art.23177. [PubMed: 18163481] [CrossRef]

4.

Alarcon GS, McGwin G Jr, Petri M, et al. Baseline characteristics of a multiethnic lupus cohort: PROFILE. Lupus. 2002;11(2):95-101. doi:10.1191/0961203302lu155oa. [PubMed: 11958584] [CrossRef]

5.

Kasitanon N, Magder LS, Petri M. Predictors of survival in systemic lupus erythematosus. Medicine. 2006;85(3):147-156. doi:10.1097/01.md.0000224709.70133.f7. [PubMed: 16721257] [CrossRef]

6.

Maisonneuve P, Agodoa L, Gellert R, et al. Distribution of primary renal diseases leading to end-stage renal failure in the United States, Europe, and Australia/New Zealand: results from an international comparative study. Am J Kidney Dis. 2000;35(1):157-165. doi:10.1016/S0272-6386(00)70316-7. [PubMed: 10620560] [CrossRef]

7.

Odutola J, Ward MM. Ethnic and socioeconomic disparities in health among patients with rheumatic disease. Curr Opin Rheumatol. 2005;17(2):147-152. PubMed [PubMed: 15711226]

8.

Alarcon GS, Friedman AW, Straaton KV, et al. Systemic lupus erythematosus in three ethnic groups: III. a comparison of characteristics early in the natural history of the LUMINA cohort. LUpus in MInority populations: NAture vs. Nurture. Lupus. 1999;8(3):197-209. [PubMed: 10342712]

9.

Dooley MA, Aranow C, Ginzler EM. Review of ACR renal criteria in systemic lupus erythematosus. Lupus. 2004;13(11):857-860. PMID:15580982. [PubMed: 15580982]

10.

Ward MM, Pyun E, Studenski S. Mortality risks associated with specific clinical manifestations of systemic lupus erythematosus. Arch Intern Med. 1996;156(12):1337-1344. [PubMed: 8651844]

11.

Costenbader KH, Desai A, Alarcon GS, et al. Trends in the incidence, demographics, and outcomes of end-stage renal disease due to lupus nephritis in the US from 1995 to 2006. Arthritis Rheum. 2011;63(6):1681-1688. doi:10.1002/art.30293 [PMC free article: PMC3106117] [PubMed: 21445962] [CrossRef]

12.

Bernatsky S, Boivin JF, Joseph L, et al. Race/ethnicity and cancer occurrence in systemic lupus erythematosus. Arthritis Rheum. 2005;53(5):781-784. doi:10.1002/art.21458. [PubMed: 16208671] [CrossRef]

13.

Cervera R, Khamashta MA, Font J, et al. Morbidity and mortality in systemic lupus erythematosus during a 10-year period: a comparison of early and late manifestations in a cohort of 1,000 patients. Medicine (Baltimore). 2003;82(5):299-308. doi:10.1097/01.md.0000091181.93122.55. [PubMed: 14530779] [CrossRef]

14.

Krishnan E, Hubert HB. Ethnicity and mortality from systemic lupus erythematosus in the US. Ann Rheum Dis. 2006;65(11):1500-1505. doi:10.1136/ard.2005.040907. [PMC free article: PMC1798334] [PubMed: 16627544] [CrossRef]

15.

Barr RG, Seliger S, Appel GB, et al. Prognosis in proliferative lupus nephritis: the role of socio-economic status and race/ethnicity. Nephrol Dial Transplant. 2003;18(10):2039-2046. doi:10.1093/ndt/gfg345. [PubMed: 13679478] [CrossRef]

16.

Hahn BH, McMahon MA, Wilkinson A, et al. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res (Hoboken). 2012;64(6):797-808. doi:10.1002/acr.21664. PMID:22556106. [PMC free article: PMC3437757] [PubMed: 22556106] [CrossRef]

17.

van Staa TP, Leufkens HG, Abenhaim L, Zhang B, Cooper C. Oral corticosteroids and fracture risk: relationship to daily and cumulative doses. Rheumatology (Oxford). 2000;39(12):1383-1389. [PubMed: 11136882]

18.

Walsh LJ, Wong CA, Oborne J, et al. Adverse effects of oral corticosteroids in relation to dose in patients with lung disease. Thorax. 2001;56(4):279-284. [PMC free article: PMC1746020] [PubMed: 11254818]

19.

Zonana-Nacach A, Barr SG, Magder LS, Petri M. Damage in systemic lupus erythematosus and its association with corticosteroids. Arthritis Rheum. 2000;43(8):1801-1808. doi:10.1002/1529-0131(200008)43:8<1801::AID-ANR16>3.0.CO;2-O. [PubMed: 10943870] [CrossRef]

20.

Houssiau FA, D'Cruz D, Sangle S, et al. Azathioprine versus mycophenolate mofetil for long-term immunosuppression in lupus nephritis: results from the MAINTAIN Nephritis Trial. Annals Rheumatic Dis. 2010;69(12):2083-2089. doi:10.1136/ard.2010.131995. [PMC free article: PMC3002764] [PubMed: 20833738] [CrossRef]

21.

Qu H, Shewchuk RM, Alarcon G, et al. Mapping perceptions of lupus medication decision-making facilitators: the importance of patient context. Arthritis Care Res (Hoboken). 2016;68(12):1787-1794. doi:10.1002/acr.22904. [PubMed: 27059939] [CrossRef]

22.

Singh JA, Qu H, Yazdany J, Chatham W, Shewchuk R. Minorities with lupus nephritis and medications: a study of facilitators to medication decision-making. Arthritis Res Ther. 2015;(17):367. doi:10.1186/s13075-015-0883-z. [PMC free article: PMC4704543] [PubMed: 26680561] [CrossRef]

23.

Singh JA, Qu H, Yazdany J, Chatham W, Dall'era M, Shewchuk RM. Barriers to medication decision making in women with lupus nephritis: a formative study using nominal group technique. J Rheumatol. 2015. doi:10.3899/jrheum.150168. [PubMed: 26178276] [CrossRef]

24.

Singh JA, Hossain A, Kotb A, Wells GA. Comparative effectiveness of immunosuppressive drugs and corticosteroids for lupus nephritis: a systematic review and network meta-analysis. Syst Rev. 2016;5(1):155. doi:10.1186/s13643-016-0328-z. [PMC free article: PMC5020478] [PubMed: 27619512] [CrossRef]

25.

Singh JA, Hossain A, Kotb A, Wells G. Risk of serious infections with immunosuppressive drugs and glucocorticoids for lupus nephritis: a systematic review and network meta-analysis. BMC Med. 2016;14(1):137. doi:10.1186/s12916-016-0673-8. [PMC free article: PMC5022202] [PubMed: 27623861] [CrossRef]

26.

Singh JA, Hossain A, Kotb A, et al. Treatments for lupus nephritis: a systematic review and network metaanalysis. J Rheumatol. 2016;43(10):1801-1815. doi:10.3899/jrheum.160041. [PubMed: 27585688] [CrossRef]

27.

Appel GB, Contreras G, Dooley MA, et al. Mycophenolate mofetil versus cyclophosphamide for induction treatment of lupus nephritis. J Am Soc Nephrol. 2009;20(5):1103-1112. doi:10.1681/ASN.2008101028. PMID:19369404. [PMC free article: PMC2678035] [PubMed: 19369404] [CrossRef]

28.

Dooley MA, Jayne D, Ginzler EM, et al. Mycophenolate versus azathioprine as maintenance therapy for lupus nephritis. N Engl J Med. 2011;365(20):1886-1895. doi:10.1056/NEJMoa1014460. [PubMed: 22087680] [CrossRef]

29.

Langford AT, Resnicow K, Roberts JS, Zikmund-Fisher BJ. Racial and ethnic differences in direct-to-consumer genetic tests awareness in HINTS 2007: sociodemographic and numeracy correlates. J Genet Couns. 2012;21(3):440-447. doi:10.1007/s10897-011-9478-2. [PubMed: 22271378] [CrossRef]

30.

Cene CW, Roter D, Carson KA, Miller ER III, Cooper LA. The effect of patient race and blood pressure control on patient-physician communication. J Gen Intern Med. 2009;24(9):1057-1064. doi:10.1007/s11606-009-1051-4. [PMC free article: PMC2726885] [PubMed: 19575270] [CrossRef]

31.

Gordon HS, Street RL Jr, Kelly PA, Souchek J, Wray NP. Physician-patient communication following invasive procedures: an analysis of post-angiogram consultations. Soc Sci Med. 2005;61(5):1015-1025. doi:10.1016/j.socscimed.2004.12.021. [PubMed: 15955403] [CrossRef]

32.

Johnson RL, Roter D, Powe NR, Cooper LA. Patient race/ethnicity and quality of patient-physician communication during medical visits. Am J Public Health. 2004;94(12):2084-2090. [PMC free article: PMC1448596] [PubMed: 15569958]

33.

Manfredi C, Kaiser K, Matthews AK, Johnson TP. Are racial differences in patient-physician cancer communication and information explained by background, predisposing, and enabling factors? J Health Commun. 2010;15(3):272-292. doi:10.1080/10810731003686598. [PMC free article: PMC2862581] [PubMed: 20432108] [CrossRef]

34.

Jeon-Slaughter H. Economic factors in of patients' nonadherence to antidepressant treatment. Soc Psychiatry Psychiatr Epidemiol. 2012. doi:10.1007/s00127-012-0497-6. [PubMed: 22415606] [CrossRef]

35.

Cox ED, Nackers KA, Young HN, Moreno MA, Levy JF, Mangione-Smith RM. Influence of race and socioeconomic status on engagement in pediatric primary care. Patient Educ Couns. 2012;87(3):319-326. doi:10.1016/j.pec.2011.09.012. [PMC free article: PMC3359403] [PubMed: 22070902] [CrossRef]

36.

Yao L, Robert SA. Examining the racial crossover in mortality between African American and White older adults: a multilevel survival analysis of race, individual socioeconomic status, and neighborhood socioeconomic context. J Aging Res. 2011;2011:132073 doi:10.4061/2011/132073. [PMC free article: PMC3139872] [PubMed: 21792390] [CrossRef]

37.

Chu DI, Moreira DM, Gerber L, et al. Effect of race and socioeconomic status on surgical margins and biochemical outcomes in an equal-access health care setting: results from the Shared Equal Access Regional Cancer Hospital (SEARCH) database. Cancer. 2012. doi:10.1002/cncr.27456. [PMC free article: PMC3376692] [PubMed: 22415377] [CrossRef]

38.

Trinh QD, Sun M, Sammon J, et al. Disparities in access to care at high-volume institutions for uro-oncologic procedures. Cancer. 2012. doi:10.1002/cncr.27440. [PubMed: 22298310] [CrossRef]

39.

FitzGerald JD, Soohoo NF, Losina E, Katz JN. Potential impact on patient residence to hospital travel distance and access to care under a policy of preferential referral to high-volume knee replacement hospitals. Arthritis Care Res (Hoboken). 2012;64(6):890-897. doi:10.1002/acr.21611. [PMC free article: PMC3869386] [PubMed: 22238250] [CrossRef]

40.

Byrne MM, Souchek J, Richardson M, Suarez-Almazor M. Racial/ethnic differences in preferences for total knee replacement surgery. J Clin Epidemiol. 2006;59(10):1078-1086. doi:10.1016/j.jclinepi.2006.01.010. [PubMed: 16980148] [CrossRef]

41.

Rosen AB, Tsai JS, Downs SM. Variations in risk attitude across race, gender, and education. Med Decis Making. 2003;23(6):511-517. doi:10.1177/0272989X03258431 [PubMed: 14672111] [CrossRef]

42.

Ibrahim SA, Siminoff LA, Burant CJ, Kwoh CK. Differences in expectations of outcome mediate African American/White patient differences in “willingness” to consider joint replacement. Arthritis Rheum. 2002;46(9):2429-2435. doi:10.1002/art.10494. [PubMed: 12355491] [CrossRef]

43.

Hofmann JC, Wenger NS, Davis RB, et al. Patient preferences for communication with physicians about end-of-life decisions. SUPPORT investigators. study to understand prognoses and preference for outcomes and risks of treatment. Ann Intern Med. 1997;127(1):1-12. [PubMed: 9214246]

44.

Whittle J, Conigliaro J, Good CB, Lofgren RP. Racial differences in the use of invasive cardiovascular procedures in the Department of Veterans Affairs medical system. N Engl J Med. 1993;329(9):621-627. doi:10.1056/NEJM199308263290907. [PubMed: 8341338] [CrossRef]

45.

Constantinescu F, Goucher S, Weinstein A, Fraenkel L. Racial disparities in treatment preferences for rheumatoid arthritis. Med Care. 2009;47(3):350-355. doi:10.1097/MLR.0b013e31818af829. [PMC free article: PMC3682220] [PubMed: 19165120] [CrossRef]

46.

Institute of Medicine. Health Literacy: A Prescription to End Confusion. National Academies Press; 2004. [PubMed: 25009856]

47.

Rothman RL, Montori VM, Cherrington A, Pignone MP. Perspective: the role of numeracy in health care. J Health Commun. 2008;13(6):583-595. doi:10.1080/10810730802281791. [PMC free article: PMC2767457] [PubMed: 18726814] [CrossRef]

48.

Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Aff (Millwood). 2007;26(3):741-748. doi:10.1377/hlthaff.26.3.741. [PubMed: 17485752] [CrossRef]

49.

Osborn CY, Cavanaugh K, Wallston KA, White RO, Rothman RL. Diabetes numeracy: an overlooked factor in understanding racial disparities in glycemic control. Diabetes Care. 2009;32(9):1614-1619. doi:10.2337/dc09-0425. [PMC free article: PMC2732142] [PubMed: 19401443] [CrossRef]

50.

Waldrop-Valverde D, Osborn CY, Rodriguez A, Rothman RL, Kumar M, Jones DL. Numeracy skills explain racial differences in HIV medication management. AIDS Behav. 2010;14(4):799-806. doi:10.1007/s10461-009-9604-4. PMCID:2891293. [PMC free article: PMC2891293] [PubMed: 19669403] [CrossRef]

51.

Julian LJ, Yelin E, Yazdany J, et al. Depression, medication adherence, and service utilization in systemic lupus erythematosus. Arthritis Rheum. 2009;61(2):240-246. doi:10.1002/art.24236. [PMC free article: PMC2875189] [PubMed: 19177526] [CrossRef]

52.

Costedoat-Chalumeau N, Pouchot J, Guettrot-Imbert G, et al. Adherence to treatment in systemic lupus erythematosus patients. Best Pract Res Clin Rheumatol. 2013;27(3):329-340. doi:10.1016/j.berh.2013.07.001. [PubMed: 24238690] [CrossRef]

53.

Vina ER, Masi CM, Green SL, Utset TO. A study of racial/ethnic differences in treatment preferences among lupus patients. Rheumatology (Oxford). 2012;51(9):1697-1706. doi:10.1093/rheumatology/kes128. [PMC free article: PMC3418647] [PubMed: 22653381] [CrossRef]

54.

Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;(10):CD001431. doi:10.1002/14651858.CD001431.pub3. [PubMed: 21975733] [CrossRef]

55.

Petrella RJ, Petrella M. A prospective, randomized, double-blind, placebo controlled study to evaluate the efficacy of intraarticular hyaluronic acid for osteoarthritis of the knee. J Rheumatol. 2006;33(5):951-956. doi:0315162X-33-951 [PubMed: 16652426]

56.

Greenfield S, Kaplan S, Ware JE Jr. Expanding patient involvement in care. effects on patient outcomes. Ann Intern Med. 1985;102(4):520-528. [PubMed: 3977198]

57.

Gattellari M, Butow PN, Tattersall MH. Sharing decisions in cancer care. Soc Sci Med. 2001;52(12):1865-1878. doi:S0277953600003038 [PubMed: 11352412]

58.

Keating NL, Guadagnoli E, Landrum MB, Borbas C, Weeks JC. Treatment decision making in early-stage breast cancer: should surgeons match patients' desired level of involvement? J Clin Oncol. 2002;20(6):1473-1479. doi:10.1200/jco.2002.20.6.1473. [PubMed: 11896094] [CrossRef]

59.

Davidson JR, Brundage MD, Feldman-Stewart D. Lung cancer treatment decisions: patients' desires for participation and information. Psychooncology. 1999;8(6):511-520. doi:10.1002/(SICI)1099-1611(199911/12)8:6<511::AID-PON415>3.0.CO;2-T [PubMed: 10607984] [CrossRef]

60.

Yazdany J, Feldman CH, Liu J, Ward MM, Fischer MA, Costenbader KH. Quality of care for incident lupus nephritis among Medicaid beneficiaries in the United States. Arthritis Care Res (Hoboken). 2014;66(4):617-624. doi:10.1002/acr.22182. [PMC free article: PMC4109800] [PubMed: 24124011] [CrossRef]

61.

Montori VM, Shah ND, Pencille LJ, et al. Use of a decision aid to improve treatment decisions in osteoporosis: the osteoporosis choice randomized trial. Am J Med. 2011;124(6):549-556. doi:10.1016/j.amjmed.2011.01.013. [PubMed: 21605732] [CrossRef]

62.

Mullan RJ, Montori VM, Shah ND, et al. The diabetes mellitus medication choice decision aid: a randomized trial. Arch Intern Med. 2009;169(17):1560-1568. doi:10.1001/archinternmed.2009.293. [PubMed: 19786674] [CrossRef]

63.

de Achaval S, Fraenkel L, Volk RJ, Cox V, Suarez-Almazor ME. Impact of educational and patient decision aids on decisional conflict associated with total knee arthroplasty. Arthritis Care Res (Hoboken). 2012;64(2):229-237. doi:10.1002/acr.20646. [PMC free article: PMC3634330] [PubMed: 21954198] [CrossRef]

64.

Fraenkel L, Street RL Jr, Fried TR. Development of a tool to improve the quality of decision making in atrial fibrillation. BMC Med Inform Decis Mak. 2011;(11):59. doi:10.1186/1472-6947-11-59. [PMC free article: PMC3207873] [PubMed: 21977943] [CrossRef]

65.

Berman L, Curry L, Goldberg C, Gusberg R, Fraenkel L. Pilot testing of a decision support tool for patients with abdominal aortic aneurysms. J Vasc Surg. 2011;53(2):285-292 doi:10.1016/j.jvs.2010.08.075. [PMC free article: PMC3058927] [PubMed: 21067886] [CrossRef]

66.

Fraenkel L, Chodkowski D, Lim J, Garcia-Tsao G. Patients' preferences for treatment of hepatitis C. Med Decis Making. 2010;30(1):45-57. doi:10.1177/0272989X09341588. [PMC free article: PMC2812601] [PubMed: 19636065] [CrossRef]

67.

Nota I, Drossaert CHC, Melissant HC, et al. Development of a web-based patient decision aid for initiating disease modifying anti-rheumatic drugs using user-centred design methods. BMC Med Inform Decis Mak. 2017;17(1):51. doi:10.1186/s12911-017-0433-5. [PMC free article: PMC5405550] [PubMed: 28441950] [CrossRef]

68.

Meade T, Dowswell E, Manolios N, Sharpe L. The motherhood choices decision aid for women with rheumatoid arthritis increases knowledge and reduces decisional conflict: a randomized controlled trial. BMC Musculoskelet Disord. 2015;(16):260. doi:10.1186/s12891-015-0713-0. [PMC free article: PMC4579637] [PubMed: 26395873] [CrossRef]

69.

Singh JA, Christensen R, Wells GA, et al. A network meta-analysis of randomized controlled trials of biologics for rheumatoid arthritis: a Cochrane overview. CMAJ. 2009;181(11):787-796. doi:10.1503/cmaj.091391. [PMC free article: PMC2780484] [PubMed: 19884297] [CrossRef]

70.

Singh JA, Christensen R, Wells GA, et al. Biologics for rheumatoid arthritis: an overview of Cochrane reviews. Cochrane Database Syst Rev. 2009;(4):CD007848. doi:10.1002/14651858.CD007848.pub2. [PMC free article: PMC10636593] [PubMed: 19821440] [CrossRef]

71.

Singh JA, Wells GA, Christensen R, et al. Adverse effects of biologics: a network meta-analysis and Cochrane overview. Cochrane Database Syst Rev. 2011;(2):CD008794. doi:10.1002/14651858.CD008794.pub2. [PMC free article: PMC7173749] [PubMed: 21328309] [CrossRef]

72.

Methods guide for effectiveness and comparative effectiveness reviews. Agency for Healthcare Research and Quality. Published 2012. Accessed July 25, 2012. [PubMed: 21433403]

73.

The Cochrane Collaboration. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.1.0. The Cochrane Collaboration; 2011. Accessed July 25, 2012. https:​//handbook-5-1.cochrane.org

74.

Deeks J, Higgins J, Altman D. Chapter 9: Analysing data and undertaking meta-analyses. In: Higgins J, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.0.0. The Cochrane Collaboration; 2008. Accessed July 25, 2012. https://training​.cochrane​.org/handbook/archive/v5.0.0/

75.

Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med. 2002;21(16):2313-2324. doi:10.1002/sim.1201 [PubMed: 12210616] [CrossRef]

76.

Psaty BM, Lumley T, Furberg CD, et al. Health outcomes associated with various antihypertensive therapies used as first-line agents: a network meta-analysis. JAMA. 2003;289(19):2534-2544. doi:10.1001/jama.289.19.2534 [PubMed: 12759325] [CrossRef]

77.

Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105-3124. doi:10.1002/sim.1875. [PubMed: 15449338] [CrossRef]

78.

Singh JA, Shah N, Green C. Individualized patient decision-aid for immunosuppressive drugs in women with lupus nephritis: study protocol of a randomized, controlled trial. BMC Musculoskelet Disord. 2017;18(1):53. doi:10.1186/s12891-017-1408-5. [PMC free article: PMC5282664] [PubMed: 28143529] [CrossRef]

79.

Singh JA, Qu H, Yazdany J, Chatham W, Dall'era M, Shewchuk RM. Barriers to medication decision making in women with lupus mephritis: a formative study using nominal group technique. J Rheumatol. 2015;42(9):1616-1623. doi:10.3899/jrheum.150168. [PubMed: 26178276] [CrossRef]

80.

Asmus CL, James K. Nominal group technique, social loafing, and group creative project quality. Creat Res J. 2005;17(4):349-354.

81.

Ruyter KD. Focus versus nominal group interviews: a comparative analysis. Marketing Intelligence & Planning. 1996;14(6):44-50.

82.

Delbecq AL, Van de Ven AH, Gustafson DH. Group Techniques for Program Planning. Scott, Foresman, and Co; 1975.

83.

In: Higgins JP, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.1.0. The Cochrane Collaboration; 2011. http://handbook​.cochrane.org..

84.

Singh JA, Noorbaloochi S, Tucker MD. Belimumab for systemic lupus erythematosus (protocol). Cochrane Database Syst Rev. 2013. doi:10.1002/14651858.CD010668. [PMC free article: PMC8095005] [PubMed: 33631841] [CrossRef]

85.

Henderson L, Masson P, Craig JC, et al. Treatment for lupus nephritis. Cochrane Database Syst Rev. 2012;(12):CD002922. doi:10.1002/14651858.CD002922.pub3. PMID:23235592. [PubMed: 23235592] [CrossRef]

86.

Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997;40(9):1725. doi:10.1002/1529-0131(199709)40:9&lt;1725::AID-ART29&gt;3.0.CO;2-Y. [PubMed: 9324032] [CrossRef]

87.

El-Shafey EM, Abdou SH, Shareef MM. Is mycophenolate mofetil superior to pulse intravenous cyclophosphamide for induction therapy of proliferative lupus nephritis in Egyptian patients? Clin Exp Nephrol. 2010;14(3):214-221. [PubMed: 20169461]

88.

Mitwalli AH, Al Wakeel JS, Hurraib S, et al. Comparison of high and low dose of cyclophosphamide in lupus nephritis patients: a long-term randomized controlled trial. Saudi J Kidney Dis Transpl. 2011;22(5):935-940. [PubMed: 21912022]

89.

Houssiau FA, D'Cruz D, Sangle S, et al. Azathioprine versus mycophenolate mofetil for long-term immunosuppression in lupus nephritis: results from the MAINTAIN Nephritis Trial. Ann Rheum Dis. 2010;69(12):2083-2089. [PMC free article: PMC3002764] [PubMed: 20833738]

90.

Higgins JP, Altman DG, Sterne JA. Chapter 8: Assessing risk of bias in included studies. In: Higgins JP, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.1.0. The Cochrane Collaboration; 2011. https:​//handbook-5-1.cochrane.org

91.

Dias S, Sutton A, Welton N, et al. NICE DSU Technical support document 2: a generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. August 2011. http://nicedsu​.org.uk​/wp-content/uploads​/2017/05/TSD2-General-meta-analysis-corrected-2Sep2016v2.pdf [PubMed: 27466657]

92.

Dias S, Sutton A, Welton N, et al. NICE DSU technical support document 3: heterogeneity: subgroups, meta-regression, bias and bias-adjustment. September 2011. http://nicedsu​.org.uk​/wp-content/uploads​/2016/03/TSD3-Heterogeneity​.final-report.08.05.12.pdf

93.

Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med. 2009;28(14):1861-1881. [PubMed: 19399825]

94.

Turner RM, Thompson SG, Spiegelhalter DJ. Prior distributions for the intracluster correlation coefficient, based on multiple previous estimates, and their application in cluster randomized trials. Clin Trials. 2005;2(2):108-118. PMID:16279132. [PubMed: 16279132]

95.

Spiegelhalter D, Thomas A, Best N, Lunn D. WinBUGS User Manual. Version 1.4. 2003. https://www​.mrc-bsu.cam​.ac.uk/wp-content/uploads/manual14​.pdf

96.

Ades AE, Welton NJ, Caldwell D, Price M, Goubar A, Lu G. Multiparameter evidence synthesis in epidemiology and medical decision-making. J Health Serv Res Policy. 2008;13(suppl 3):12-22. doi:10.1258/jhsrp.2008.008020. [PubMed: 18806188] [CrossRef]

97.

Dias S, Sutton A, Welton N, et al. NICE DSU technical support document 4: inconsistency in networks of evidence based on randomised controlled trials. May 2011. http://nicedsu​.org.uk​/wp-content/uploads​/2016/03/TSD4-Inconsistency​.final_.15April2014.pdf [PubMed: 27466656]

98.

Puhan MA, Schunemann HJ, Murad MH, et al. A GRADE working group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ. 2014;(349):g5630. doi:10.1136/bmj.g5630. [PubMed: 25252733] [CrossRef]

99.

American College of Rheumatology. 2016. https://www​.rheumatology.org

100.

Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi:10.1016/j.jbi.2008.08.010. [PMC free article: PMC2700030] [PubMed: 18929686] [CrossRef]

101.

Arozullah AM, Yarnold PR, Bennett CL, et al. Development and validation of a short-form, rapid estimate of adult literacy in medicine. Med Care. 2007;45(11):1026-1033. doi:10.1097/MLR.0b013e3180616c1b. [PubMed: 18049342] [CrossRef]

102.

Lee SY, Stucky BD, Lee JY, Rozier RG, Bender DE. Short assessment of health literacy-Spanish and English: a comparable test of health literacy for Spanish and English speakers. Health Serv Res. 2010;45(4):1105-1120. doi:10.1111/j.1475-6773.2010.01119.x. [PMC free article: PMC2910571] [PubMed: 20500222] [CrossRef]

103.

O'Connor AM. Validation of a decisional conflict scale. Med Decis Making. 1995;15(1):25-30. [PubMed: 7898294]

104.

Marteau TM, Dormandy E, Michie S. A measure of informed choice. Health Expect. 2001;4(2):99-108. PMID:11359540. [PMC free article: PMC5060053] [PubMed: 11359540]

105.

Michie S, Dormandy E, Marteau TM. The multi-dimensional measure of informed choice: a validation study. Patient Educ Couns. 2002;48(1):87-91. [PubMed: 12220754]

106.

Degner LF, Russell CA. Preferences for treatment control among adults with cancer. Res Nurs Health. 1988;11(6):367-374. PMID:3231738. [PubMed: 3231738]

107.

Degner LF, Kristjanson LJ, Bowman D, et al. Information needs and decisional preferences in women with breast cancer. JAMA. 1997;277(18):1485-1492. PMID:9145723. [PubMed: 9145723]

108.

Adams JR, Drake RE, Wolford GL. Shared decision-making preferences of people with severe mental illness. Psychiatr Serv. 2007;58(9):1219-1221. doi:10.1176/appi.ps.58.9.1219 [PubMed: 17766569] [CrossRef]

109.

Stewart AL, Napoles-Springer AM, Gregorich SE, Santoyo-Olsson J. Interpersonal processes of care survey: patient-reported measures for diverse groups. Health Serv Res. 2007;42(3 Pt 1):1235-1256. doi:10.1111/j.1475-6773.2006.00637.x [PMC free article: PMC1955252] [PubMed: 17489912] [CrossRef]

110.

O'Connor AM. User manual - Decisional Conflict Scale (10 item question format). Updated 2010. Accessed March 17, 2013. http://decisionaid​.ohri​.ca/docs/develop/User_Manuals​/UM_Decisional_Conflict.pdf

111.

Siminoff LA, Ravdin P, Colabianchi N, Sturm CM. Doctor-patient communication patterns in breast cancer adjuvant therapy discussions. Health Expect. 2000;3(1):26-36. [PMC free article: PMC5081082] [PubMed: 11281909]

112.

O'Connor AM. User manual - Decisional Conflict Scale. 2003. Updated 2010. Accessed December 25, 2016. https://decisionaid​.ohri​.ca/docs/develop​/User_Manuals/UM_Decisional_Conflict.pdf

113.

Degner LF, Sloan JA, Venkatesh P. The Control Preferences Scale. Can J Nurs Res. 1997;29(3):21-43. [PubMed: 9505581]

114.

Florin J, Ehrenberg A, Ehnfors M. Clinical decision-making: predictors of patient participation in nursing care. J Clin Nurs. 2008;17(21):2935-2944. [PubMed: 19034992]

115.

Singh JA, Sloan JA, Atherton PJ, et al. Preferred roles in treatment decision making among patients with cancer: a pooled analysis of studies using the Control Preferences Scale. Am J Manag Care. 2010;16(9):688-696. [PMC free article: PMC3020073] [PubMed: 20873956]

116.

Stewart AL, Napoles-Springer A, Perez-Stable EJ. Interpersonal processes of care in diverse populations. Milbank Q. 1999;77(3):305-339, 274. [PMC free article: PMC2751132] [PubMed: 10526547]

117.

Schillinger D, Handley M, Wang F, Hammer H. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: a three-arm practical clinical trial. Diabetes Care. 2009;32(4):559-566. doi:10.2337/dc08-0787. [PMC free article: PMC2660485] [PubMed: 19131469] [CrossRef]

118.

Napoles AM, Gregorich SE, Santoyo-Olsson J, O'Brien H, Stewart AL. Interpersonal processes of care and patient satisfaction: do associations differ by race, ethnicity, and language? Health Serv Res. 2009;44(4):1326-1344. doi:10.1111/j.1475-6773.2009.00965.x. [PMC free article: PMC2714869] [PubMed: 19490162] [CrossRef]

119.

Schenker Y, Stewart A, Na B, Whooley MA. Depressive symptoms and perceived doctor-patient communication in the Heart and Soul study. J Gen Intern Med. 2009;24(5):550-556. doi:10.1007/s11606-009-0937-5. [PMC free article: PMC2669866] [PubMed: 19274477] [CrossRef]

120.

Street RL Jr, Millay B. Analyzing patient participation in medical encounters. Health Commun. 2001;13(1):61-73. doi:10.1207/S15327027HC1301_06 [PubMed: 11370924] [CrossRef]

121.

Cegala DJ, Street RL Jr, Clinch CR. The impact of patient participation on physicians' information provision during a primary care medical interview. Health Commun. 2007;21(2):177-185. doi:10.1080/10410230701307824. [PubMed: 17523863] [CrossRef]

122.

Kravitz RL, Epstein RM, Feldman MD, et al. Influence of patients' requests for direct-to-consumer advertised antidepressants: a randomized controlled trial. JAMA. 2005;293(16):1995-2002. doi:10.1001/jama.293.16.1995. [PMC free article: PMC3155410] [PubMed: 15855433] [CrossRef]

123.

Street RL Jr, Gordon H, Haidet P. Physicians' communication and perceptions of patients: is it how they look, how they talk, or is it just the doctor? Soc Sci Med. 2007;65(3):586-598. doi:10.1016/j.socscimed.2007.03.036 [PMC free article: PMC2811428] [PubMed: 17462801] [CrossRef]

124.

Street RL Jr, Richardson MN, Cox V, Suarez-Almazor ME. (Mis)understanding in patient-health care provider communication about total knee replacement. Arthritis Rheum. 2009;61(1):100-107. doi:10.1002/art.24371. [PubMed: 19116969] [CrossRef]

125.

Mudano AS, Gary LC, Oliveira AL, et al. Using tablet computers compared to interactive voice response to improve subject recruitment in osteoporosis pragmatic clinical trials: feasibility, satisfaction, and sample size. Patient Prefer Adherence. 2013;(7):517-523. doi:10.2147/PPA.S44551. [PMC free article: PMC3685447] [PubMed: 23807841] [CrossRef]

126.

Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Erlbaum; 1988.

127.

Cohan D, Gomez E, Greenberg M, Washington S, Charlebois ED. Patient perspectives with abbreviated versus standard pre-test HIV counseling in the prenatal setting: a randomized-controlled, non-inferiority trial. PLoS One. 2009;4(4):e5166. doi:10.1371/journal.pone.0005166. [PMC free article: PMC2666158] [PubMed: 19367335] [CrossRef]

128.

Smith SK, Trevena L, Simpson JM, Barratt A, Nutbeam D, McCaffery KJ. A decision aid to support informed choices about bowel cancer screening among adults with low education: randomised controlled trial. BMJ. 2010;341:c5370. doi:10.1136/bmj.c5370. [PMC free article: PMC2965151] [PubMed: 20978060] [CrossRef]

129.

De Abreu MM, Gafni A, Ferraz MB. Development and testing of a decision board to help clinicians present treatment options to lupus nephritis patients in Brazil. Arthritis Rheum. 2009;61(1):37-45. doi:10.1002/art.24368. [PubMed: 19116966] [CrossRef]

130.

Ganachari MS, Almas SA. Evaluation of clinical pharmacist mediated education and counselling of systemic lupus erythematosus patients in tertiary care hospital. Ind J Rheumatol. 2012;(7):7-12.

131.

Ting TV, Kudalkar D, Nelson S, et al. Usefulness of cellular text messaging for improving adherence among adolescents and young adults with systemic lupus erythematosus. J Rheumatol. 2012;39(1):174-179. doi:10.3899/jrheum.110771 [PubMed: 22089460] [CrossRef]

132.

Galo JS, Mehat P, Rai SK, Avina-Zubieta A, De Vera MA. What are the effects of medication adherence interventions in rheumatic diseases: a systematic review. Ann Rheum Dis. 2016;75(4):667-673. doi:10.1136/annrheumdis-2014-206593. [PubMed: 25667208] [CrossRef]

133.

International Patient Decision Aid Standard (IPDAS) Collaboration website. Published 2013. Accessed January 28, 2017. http://www​.ipdas.ohri.ca/

134.

Centers for Disease Control and Prevention. Trends in deaths from systemic lupus erythematosus--United States, 1979-1998. MMWR Morb Mortal Wkly Rep. 2002;51(17):371-374. [PubMed: 12018384]

Acknowledgments

We thank Jeffrey Foster, Mazin Khalil, Candace Green, and Diana Florence for proofreading this report.

Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#CE-1304-6631) Further information available at: https://www.pcori.org/research-results/2013/personalized-decision-aid-help-women-lupus-nephritis-racially-and-ethnically

Suggested citation:

Singh J, Yazdany J, Chatham W, et al. (2019). A Personalized Decision Aid to Help Women with Lupus Nephritis from Racially and Ethnically Diverse Backgrounds Make Decisions about Taking Immune-Blocking Medicines. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/10.2019.CE.13046631

Disclaimer

The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.