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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Objective

Despite the importance of achieving tight control, many patients with rheumatoid arthritis (RA) are not effectively treated with disease-modifying antirheumatic drugs. The objective of this study was to develop a decision support tool to inform RA patients with ongoing active disease about the risks and benefits related to biologic therapy.

Methods

We developed a balanced, web-based, decision support tool. Options, values, and probabilistic information were described using theoretically supported formulations. We conducted a pre-/posttest study to assess preliminary evidence of the tool's efficacy in improving knowledge related to biologics, clarity of values, willingness to take a biologic, and informed choice.

Results

We interviewed 104 subjects (mean age 62 years, 84% women, 87% white, and median duration of RA 8 years). Knowledge (coded on a 0–20 scale) and willingness to take a biologic (coded on a 0–10 scale) significantly increased after viewing the tool (mean differences 2.3 and 1.4, respectively; P < 0.0001 for both). Perceived knowledge and values clarity (coded on 0–100 scales) also significantly improved (mean differences 20.4 and 20.8, respectively; P < 0.0001 for both). The proportion of subjects making an informed value-concordant choice increased substantially from 35% to 64%.

Conclusion

A tool designed to effectively communicate the risks and benefits associated with biologic therapy increased knowledge, patient willingness to escalate care, and the likelihood of making an informed choice. The results of this study support the need for a clinical trial to examine the impact of the tool in clinical practice.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

A targeted strategy to minimize disease activity has been shown to significantly improve both short- and long-term outcomes in rheumatoid arthritis (RA), and guidelines strongly recommend that physicians monitor and escalate treatment to achieve this goal (1, 2). Yet, despite the widespread endorsement of this approach, many patients are not effectively treated with disease-modifying antirheumatic drugs (DMARDs) (3, 4). While access (both geographic and financial) and logistics (time) are frequently cited barriers, this gap in care persists even among insured patients under the care of a rheumatologist, indicating that patient factors over and above access to services may adversely affect quality of care in RA (5). A study by Wolfe and Michaud (6) highlighted the potential effect of such factors in clinical practice. In this study, the authors found that 71% of patients with RA were reluctant to escalate care despite objective findings of active disease. Concerns regarding the risks of side effects and losing control of their disease were important factors influencing patients' preferences to remain with the status quo. Van Hulst et al (7) recently found that patients and physicians differ substantially in how they approach the decision of whether or not to escalate care and suggested that better patient–physician communication is needed to improve decision making. These studies indicate that decision support tools that effectively inform RA patients and improve patient–physician communication may lead to improved decision making and ultimately adherence to evidence-based guidelines.

While escalation of care in RA can involve many different treatment decisions, one of the more difficult decisions patients face is whether or not to initiate biologic therapy after failing nonbiologic DMARDs. Risk communication is particularly challenging in this situation because of the sheer number of risks to disclose, the difficulty explaining the risks of extremely rare, but dreaded, adverse events (AEs), and the tendency for people to discount (or underweight) future benefits (8). A traditional view of decision making, founded in expected utility theory, is based on the premise that people think quantitatively about risks and benefits, and process numbers resulting in a choice reflecting overall utility (value). However, numerous studies have demonstrated that this theory falls short in predicting how people make decisions in the real world (9–12). Fuzzy trace theory (FTT) builds directly on the advances of earlier research in risk perception and medical decision making to provide a more useful framework to guide the development of intuitive decision aids for patients (9, 13–15). FTT contends that people code and retrieve information using gist and verbatim representations. In this context, gist refers to the overall picture or the general meaning that people attach to a specific medication characteristic (9). Gist is qualitative, subjective, and dependent on individual factors (e.g., education, culture, and experience) that affect meaning. For example, the gist representation of the extremely rare risk of progressive multifocal leukoencephalopathy for one patient might be “I could get something like mad cow disease and die” and for another patient, “bad things can happen with all medications, but this is really rare and unlikely to happen to me.” In contrast, verbatim representations refer to the literal risk. A large body of evidence based on studies using controlled experiments, mathematical models, and neuroimaging supports the conclusion that people preferentially rely on gist, and not verbatim, representations when making decisions (9, 14). For example, knowing one's precise risk of developing breast cancer (verbatim representation) does not increase the rate of screening. In contrast, perceived risk (i.e., “my risk is high,” a gist representation) is a much stronger predictor of health-related behaviors (16, 17).

Currently, no proven mechanisms exist to effectively inform patients with RA and enable them to process the complex information related to escalating care after failing traditional DMARDs. The objective of this study was to develop a theory-based decision tool to effectively inform patients and promote high-quality decision making in RA patients who are candidates for biologic DMARDs. Informed choice requires that patients accurately understand salient differences between available treatment options. More important than being able to recall precise “verbatim” risk estimates is the ability to attach accurate meaning to this information (9, 14).

Significance & Innovations

  • Currently, no proven mechanisms exist to effectively inform patients with rheumatoid arthritis (RA) and enable them to process the complex information related to escalating care after failing traditional disease-modifying antirheumatic drugs.

  • In this study we developed a web-based decision support tool to effectively inform patients and promote high-quality decision making in RA patients who are candidates for biologic disease-modifying antirheumatic drugs.

  • Viewing the tool resulted in improved knowledge, willingness to escalate care, and the likelihood of making an informed value-concordant choice.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Tool design.

The tool is an interactive, web-based, computerized educational module with voiceovers that subjects navigate through using a menu bar. Information is provided for all tumor necrosis factor inhibitors, abatacept, rituximab, and tocilizumab. To promote accurate gist representations, the tool begins with an educational segment describing the natural history of RA and why biologics are frequently recommended for patients with persistent disease activity despite the use of traditional DMARDs. The introduction's objective is to ensure that subjects have accurate illness perceptions regarding the consequences of chronic inflammation and the role of biologics.

Because the amount of information can influence risk perceptions (18, 19), the same amount of attention was devoted to benefits as to risks. Benefits included improvements in pain, joint swelling, fatigue, progression of erosions, chance of remission, sleep disturbance, cardiovascular outcomes, work, and overall quality of life. Links were provided to view bar graphs demonstrating the benefit of adding the specific biologic to a traditional DMARD (20–38).

We surveyed a panel of 13 internationally renowned RA experts and, based on their ratings, stratified AEs into those that: 1) must be disclosed to all patients considering biologics, 2) should be provided as supplemental information via links for patients desiring additional information, and 3) need not be included at all. This flexible approach addresses the needs of patients desiring additional information without overwhelming others.

The expert panel was presented with 3 groups of AEs: not serious and easily reversible, moderately serious and requiring treatment, and those associated with significant morbidity. Experts rated the AEs from “extremely important” (1) to “not important at all” (7). AEs were treated per the following rules: if more than 75% of the panel rated the AE between 5 and 7, the AE was excluded from the tool. The remaining AEs were included if 75% or more of the panel rated the AE between 1 and 4; otherwise, they were included as a link. Graphics were used to facilitate understanding of probabilistic information. Pie charts (for AEs with a risk of 1% or greater) and pictographs (for AEs occurring in less than 1%) were used to describe AEs and to specifically prevent denominator neglect (39). We also inserted questions to test patient knowledge and provide feedback. Knowledge questions emphasized accurate gist representations and not recall of specific “verbatim” data. After subjects responded, feedback highlighted the correct response. Illustrative screen shots are provided in Supplementary Appendix A (available in the online version of this article at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1529-0131a).

Pre-/posttest.

Subjects were recruited from community-based rheumatology practices. Potential subjects were referred by their treating physician and screened for eligibility. Subjects who were ages ≥18 years, able to speak and read English, and taking 1 or more traditional DMARDs and/or a biologic (including rituximab in the last 12 months) were eligible to participate.

We did not exclude patients currently receiving a biologic because, except for optional links that demonstrate graphs comparing biologics plus methotrexate to methotrexate alone, the tool content is also relevant for patients considering switching to a new biologic. For example, while the tool defaults to first presenting tumor necrosis factor inhibitors, a navigation bar allows subjects to choose tabs linking to a different biologic. In view of the literature demonstrating that patients are frequently underinformed about their medications, we expected the information included in the tool to also be relevant to subjects currently receiving a biologic (40).

Given the timeline and budget for this study, it was not possible to recruit patients at the time of an actual treatment decision. Ideally, because the tool was designed to complement patient–physician communication, physicians would refer patients to access the tool after discussing the need to consider escalating care. Implementing the tool in this setting, however, requires significant resources and is better justified once preliminary data support its potential value.

Exclusion criteria included relative or absolute contraindications to any approved biologic (New York Heart Association class III or IV congestive heart failure, open skin wound, active infection, history of demyelinating disease, and untreated latent tuberculosis) and comorbidities that may overwhelm RA treatment decisions (e.g., history of malignancy within the past 5 years, excluding basal cell carcinoma, end-stage renal disease on dialysis, end-stage chronic obstructive pulmonary disease, or hearing or visual impairment).

Data collection.

Patients with RA participated in a single face-to-face interview, during which they completed questionnaires before and after viewing the tool. We measured change in knowledge, perceived knowledge, values clarification (41), willingness to try biologics (42), and value-concordant choices. Values were assessed as simple gist principles (9, 14, 15). Because no questionnaires existed to measure patient knowledge related to biologics, we developed 20 true/false statements (10 each; see Supplementary Appendix A, available in the online version of this article at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1529-0131a). Items were pilot tested to ensure comprehension. Item order was determined using a random-numbers generator. Perceived knowledge and clarity of values were measured using 2 subscales from the well-validated decisional conflict scale (41). These 6 items are coded on 5-point scales ranging from “strongly agree” to “strongly disagree.” The perceived knowledge items are: “I know which options are available to me; I know the benefits of each option; I know the risks and side effects of each option.” The clarity of values items are: “I am clear about which benefits matter most to me; I am clear about which risks and side effects matter most to me; I am clear about which is more important to me (the benefits or the risk and side effects).” Patients' willingness to take a (new) biologic was measured using the choice predisposition scale (42): “If your doctor recommended that you consider taking a (new) biologic, would you be willing to take one?” This item is coded on an 11-point scale anchored by “not willing at all” and “extremely willing,” with “unsure” at the midpoint (42).

Although no currently available instruments exist to measure informed choice, there is agreement that such choice is based on accurate knowledge that is concordant with one's values (43). Therefore, we classified subjects as having made an informed choice based on an a priori criteria set before enrollment: 1) they answered 75% of the knowledge items correctly, were willing to try a (new) biologic as indicated by a choice predisposition score of 8 or greater, and had values that favored the use of medications to control disease activity; or 2) they answered 75% of the knowledge items correctly, were less willing to try a (new) biologic as indicated by a choice predisposition score of less than 8, and had values that demonstrated reluctance to take medications to control disease activity. Participants rated their values (core beliefs) on 4-point scales (where 1 = “strongly agree” and 4 = “strongly disagree”) for 10 statements developed by investigators (see Supplementary Appendix A, available in the online version of this article at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1529-0131a). Value statements were solicited from patients and framed based on input from experts in risk communication (EP) and FTT (VR). Statement order was determined using a random-numbers generator. Subjects were classified as having values that favored biologics if the sum of the scores of statements favoring medication use to minimize disease activity was less than the sum of the scores for statements consistent with reluctance to escalate care. Sums were computed in real time and those favoring biologics are shown the following statement: “Your responses show that you may be interested in changing your medications to better control your arthritis. This tool will help you learn about biologics and will give you the opportunity to think about specific questions you may want to discuss with your rheumatologist.” Subjects with values demonstrating reluctance toward escalating care see: “Your responses show that you may be concerned about changing your medications to better control your arthritis. This tool will help you learn about biologics and will give you the opportunity to think about specific questions you may want to discuss with your rheumatologist.”

Disease activity was measured using Routine Assessment of Patient Index Data 4 (RAPID 4), which includes 4 components of the Multidimensional Health Assessment Questionnaire: physical functional assessment, arthritis-related pain numerical rating scale, patient global assessment of disease activity, and the Rheumatoid Arthritis Disease Activity Index self-report joint count (44–46). Acceptability of the tool was assessed by asking the subjects to rate the quality, quantity, and balance of the information presented (on a 5-point scale ranging from “excellent” to “very poor”), whether they thought the tool was helpful for learning about biologics (“very helpful,” “somewhat,” or “not helpful”), and whether they would recommend the tool for other patients (“yes” or “no”).

Statistical analysis.

Statistical analyses were conducted with SAS software, version 9.22. Pre-/posttest differences for continuous variables were performed using paired t-tests. The proportion of subjects having a value-concordant choice before and after completing the tool was compared using McNemar's test. Pre-/posttest differences, including baseline scores, were also examined using general linear models to adjust for age, education, disease activity (RAPID 4 score), and current biologic use (47).

The study was powered to detect a difference in the pre–/post–perceived knowledge scores because it has been shown to discriminate between patients who reject or accept medical interventions (41). Based on previous studies, 90 subjects were needed to detect a Cohen's d effect size of at least 0.3 (41), assuming a correlation between ratings r = 0.5, α = 0.05, 80% power, and 2-tailed test.

This protocol was determined to be exempt from continuing review by the Human Investigations Committee at our institution. Verbal consent was obtained from all participants.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

We interviewed 104 subjects (48 eligible subjects refused to participate) with a mean ± SD age of 62 ± 12 years. The majority of subjects were white (87%) and women (84%), and had some college education (72%). Thirty-nine percent were employed, 40% were retired, 15% were receiving disability benefits, and 6% were unemployed. The median duration of RA was 8 years (range 1–61 years) and the median RAPID 4 score was 16.1 (range 4.3–31.2). Thirty-eight percent reported an overall health status of very good or excellent, and 40% were receiving a biologic. Most subjects' values were consistent with a tight control approach (Table 1).

Table 1. Subjects' value ratings before performing the tool*
Value directionStatementMean ± SD
  • *

    Statements are coded on a 1–4 scale, where 1 = strongly agree and 4 = strongly disagree.

NegativeIt is better to continue with the pain I know than to change my medications.3.2 ± 0.7
PositiveIt is important to reduce my chances of becoming disabled, even if it means taking medications with a risk of serious side effects.2.2 ± 0.8
PositiveIt is okay to ignore the risk of a serious side effect if it is extremely rare.2.6 ± 0.8
PositiveIt is important to take the strongest possible medications needed to control my arthritis now to improve my chances of being able to function in the future.2.4 ± 0.8
PositiveIt is important to accept the risk of side effects now in order to improve my chances of being healthy in the future.2.2 ± 0.7
NegativeEven if my medications are not working well, it is better to stay on them than to try a new medication that could cause cancer.2.7 ± 0.8
NegativeIt is okay to delay treating my arthritis in order to take care of my family responsibilities.3.2 ± 0.8
NegativeIt is better to take natural remedies than prescription medications for my arthritis.3.1 ± 0.7
NegativeIt is wrong to take medications for my arthritis that could cause serious side effects.2.7 ± 0.8
PositiveIt is important to take care of my disease so that I can be as productive as possible.1.6 ± 0.7

Knowledge related to biologics significantly improved after viewing the tool (mean ± SD difference 2.3 ± 3.0), as did perceived knowledge and clarity of values (mean ± SD differences 20.4 ± 27.2 and 20.7 ± 26.2, respectively) (Table 2). Subjects were more willing to try a biologic after viewing the tool (mean ± SD difference 1.4 ± 2.3) (Table 2). Improvements were seen in subjects who were, and were not, currently receiving a biologic (Table 3). The proportion of subjects making an informed value-concordant choice significantly increased from 35% to 64% (P = 0.0001). The proportion of subjects making an informed value-concordant choice improved from 29% to 56% (P = 0.001) among subjects who were not currently receiving a biologic, and from 44% to 78% among those who were currently receiving a biologic (P = 0.002).

Table 2. Changes in knowledge, perceived knowledge, clarity of values, and willingness to try a biologic before and after using the tool
 Pretest, mean ± SDPosttest, mean ± SDP (paired t-test)
  • *

    Objective knowledge measured using a 20-item survey (possible range 0–20). An increased score indicates improvement.

  • Perceived knowledge measured using the informed subscale from the decisional conflict scale (possible range 0–100). A decreased score indicates improvement.

  • Clarity of values measured using a values clarity subscale from the decisional conflict scale (possible range 0–100). A decreased score indicates improvement.

  • §

    Willingness to try a biologic measured using the choice predisposition 11-point scale anchored by “not willing at all” and “extremely willing,” with “unsure” at the midpoint (possible range 0–10). An increased score indicates increased willingness.

Knowledge*15.7 ± 3.518.0 ± 1.9< 0.0001
Perceived knowledge74.7 ± 26.254.3 ± 18.3< 0.0001
Clarity of values68.2 ± 26.447.4 ± 16.7< 0.0001
Willingness to try a biologic§6.1 ± 2.87.5 ± 2.5< 0.0001
Table 3. Changes in knowledge, perceived knowledge, clarity of values, and willingness to try a biologic by biologic exposure*
 Subjects not currently receiving a biologic (n = 63)Subjects currently receiving a biologic (n = 41)
Mean difference (95% CI)t valuePMean difference (95% CI)t valueP
  • *

    95% CI = 95% confidence interval.

Knowledge2.8 (1.9–3.6)6.3< 0.00011.7 (1.0–2.3)5.3< 0.0001
Perceived knowledge26.5 (29.8–34.0)7< 0.000111.2 (5.1–17.2)3.70.0006
Clarity of values26.3 (18.8–33.8)7< 0.000112.2 (7.1–17.3)4.8< 0.0001
Willingness to try a biologic1.4 (0.8–1.9)5.0< 0.00011.5 (0.7–2.2)4.1< 0.0002

Pre-/posttest differences, measured using general linear models, including baseline scores, are shown in Table 4. Improvements in knowledge were similar in older (age ≥65 years) and younger adults (age ≤64 years). Statistically significant improvements in knowledge were seen across education levels, but those with less than a college education benefited more than those with at least some college education (mean ± SD difference 3.3 ± 4.4; P = 0.0004 versus mean ± SD difference 1.9 ± 2.2; P < 0.0001) (Table 4 and Figure 1).

Table 4. Standardized estimates of posttest outcomes after adjusting for baseline values and covariates*
CovariatePost-knowledge (R2 = 0.29)Post–perceived knowledge (R2 = 0.15)Post–clarity of values (R2 = 0.22)Post-willingness (R2 = 0.55)
Standardized estimate (SEE)PStandardized estimate (SEE)PStandardized estimate (SEE)PStandardized estimate (SEE)P
  • *

    SEE = standard error of the estimate; RAPID 4 = Routine Assessment of Patient Index Data 4.

Age−0.09 (0.01)0.280.23 (0.15)0.020.30 (0.12)0.002−0.25 (0.01)0.0005
Education0.21 (0.20)0.04−0.07 (1.98)0.49−0.13 (1.71)0.200.17 (0.19)0.02
Current biologic0.03 (0.35)0.78−0.04 (3.97)0.74−0.07 (3.47)0.480.12 (0.39)0.12
RAPID 4−0.003 (0.03)0.970.01 (0.30)0.89−0.01 (0.29)0.91−0.06 (0.03)0.45
Baseline0.39 (0.05)< 0.00010.22 (0.07)0.040.24 (0.06)0.020.64 (0.06)< 0.0001
thumbnail image

Figure 1. Improvements in knowledge and willingness to try a biologic among subjects with and without some college education. * = higher scores indicate improvement. http://onlinelibrary. wiley.com/journal/10.1002/(ISSN)2151-4658.

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Improvement in perceived knowledge was greater among older versus younger adults (mean ± SD difference 22.5 ± 29.9; P < 0.0001 versus mean ± SD difference 19.1 ± 25.4; P < 0.0001), as was improvement in clarity of values (mean ± SD difference 18.7 ± 29.5; P = 0.0003 versus mean ± SD difference 22.0 ± 24.0; P < 0.0001) (Table 4 and Figure 2).

thumbnail image

Figure 2. Improvements in perceived knowledge, values clarity, and willingness to try a biologic among older and younger subjects. * = lower scores indicate improvement; † = higher scores indicate improvement.

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Increased willingness to try a biologic was greater among subjects with at least some college education (mean ± SD difference 1.7 ± 2.3; P < 0.0001) compared to those without such education (mean ± SD difference 0.7 ± 1.9; P = 0.05) and among younger adults (mean ± SD difference 2.0 ± 2.4; P < 0.0001) compared to older adults (mean ± SD difference 0.6 ± 1.7; P = 0.05) (Table 4 and Figures 1 and 2).

More than 90% of the participants rated the quality and quantity of information as very good or excellent. Eighty-nine percent found the tool to be very helpful and 100% would recommend it for patients with RA.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Based on principles of FTT, a tool to support decision making for RA patients who are candidates for biologic therapy significantly increased knowledge, decreased decisional conflict (improved perceived knowledge and clarity of values), and increased patient willingness to escalate care in a pre-/posttest. Most importantly, the tool increased the proportion of patients making an informed value-concordant choice by more than 80%.

Decision aids have largely focused on supporting patient decision making in situations that include at least 2 plausible treatment options (including the possibility of refusal or deferral in some cases), and choice depends on individual patient preferences. Widely studied examples include treatment for early-stage breast and prostate cancer, screening for colorectal cancer, and the decision to undergo elective orthopedic surgery. These decisions are considered “preference sensitive” because neither option clearly dominates. In contrast, escalating care for RA patients with ongoing active disease is a decision in which the benefits clearly outweigh the risks for the majority of patients, yet biologics are nonetheless underused. Data suggest that patient- and physician-related factors are responsible. Wolfe and Michaud (6) found that fear of losing control over their disease and fear of AEs both contribute to patients' reluctance to change therapy, even when clinically indicated. Van Hulst et al (7) recently found that patients and physicians differ substantially in how they approach the decision of whether or not to escalate care and suggested that better patient–physician communication is needed to improve decision making.

In this study, we addressed barriers impeding adherence to the principles of tight control. We took several steps to overcome patients' bias toward maintaining the status quo. We devoted as much attention to the benefits associated with biologics as to the risks, and we included graphs to ensure that patients attended to denominators that include the number of patients who do not experience an AE. Our results suggest that these efforts were successful in improving knowledge and in helping patients clarify their values. Improving patient knowledge and clarity of values is necessary to prepare patients to better communicate with their physicians. While we did not measure actual behavior change in this study, we did find that willingness to try a biologic also significantly increased after viewing the tool. Most importantly, the tool significantly increased the likelihood of making an informed value-concordant choice.

Improvements were seen across demographic groups, although some differences were larger for subgroups. Specifically, subjects without a college education demonstrated greater improvements in knowledge compared to those with at least some college education. The fact that those with less education significantly improved is an encouraging finding, and suggests that the tool was constructed at a level that will benefit subjects across a wide range of backgrounds, including those most in need. Twenty-four percent of subjects without a college education scored 18 or higher (maximum score 20) on the baseline knowledge assessment compared to 44% of subjects with a college education. Therefore, the difference noted may be due in part to a ceiling effect among those with a college education. It is possible that people with a college education who were already receiving a biologic (40% in our sample) were more likely to have already sought information about biologics than those without a college education. We also found that, despite significant improvements in objective/perceived knowledge and values clarification, older adults were less likely than their younger counterparts to be willing to escalate care. This finding is consistent with some studies reporting greater risk aversion among older adults (48, 49). However, others have failed to find a significant association between age and treatment preference (50, 51), suggesting that this relationship is likely context specific.

There are several limitations to this study. The majority of the subjects had a college education and most of the subjects were white, thereby limiting the generalizability of the results. In addition, the tool was not administered to patients at the time of an actual treatment decision. Given this limitation, these results should be interpreted as providing preliminary data to support the potential value of the tool. The results of this study support the need for a clinical trial to examine the impact of the tool in clinical practice. As recommended, we tested our pre-/posttest findings using general linear models including the baseline estimate as a covariate to reduce systematic bias and error variance (47). However, a pre-/posttest study design is only strong enough to demonstrate proof of concept. To test the impact of the tool in clinical practice, a controlled trial is now required.

The decision support tool described in this study was designed based on strong theoretical principles, and pre-/posttesting demonstrates that it successfully promotes valid gist representations, facilitates processing of tradeoffs, and enables patients to understand the salient differences between the treatment options available to them.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Fraenkel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Fraenkel, Peters, Charpentier, Olsen.

Acquisition of data. Fraenkel, Charpentier, Errante, Schoen.

Analysis and interpretation of data. Fraenkel, Schoen, Reyna.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

We greatly appreciate the time and effort of the expert panelists who made this project possible: Joan Bathon, Maarten Boers, Mark Genovese, Arthur Kavanaugh, Joel Kremer, Diane Lacaille, Ted Mikuls, Larry Mooreland, James O'Dell, Ken Saag, Robert Schoen, Vibeke Strand, and Michael Weinblatt.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information
  • 1
    Singh JA, Furst DE, Bharat A, Curtis JR, Kavanaugh AF, Kremer J, et al. 2012 update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken) 2012; 64: 62539.
  • 2
    Smolen JS, Landewe R, Breedveld FC, Dougados M, Emery P, Gaujoux-Viala C, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs. Ann Rheum Dis 2010; 69: 96475.
  • 3
    Schmajuk G, Trivedi AN, Solomon DH, Yelin E, Trupin L, Chakravarty EF, et al. Receipt of disease-modifying antirheumatic drugs among patients with rheumatoid arthritis in Medicare managed care plans. JAMA 2011; 305: 4806.
  • 4
    Verschueren P, Westhovens R. Optimal care for early RA patients: the challenge of translating scientific data into clinical practice. Rheumatology (Oxford) 2011; 50: 1194200.
  • 5
    Schmajuk G, Schneeweiss S, Katz JN, Weinblatt ME, Setoguchi S, Avorn J, et al. Treatment of older adult patients diagnosed with rheumatoid arthritis: improved but not optimal. Arthritis Rheum 2007; 57: 92834.
  • 6
    Wolfe F, Michaud K. Resistance of rheumatoid arthritis patients to changing therapy: discordance between disease activity and patients' treatment choices. Arthritis Rheum 2007; 56: 213542.
  • 7
    Van Hulst LT, Kievit W, van Bommel R, van Riel PL, Fraenkel L. Rheumatoid arthritis patients and rheumatologists approach the decision to escalate care differently: results of a maximum difference scaling experiment. Arthritis Care Res (Hoboken) 2011; 63: 140714.
  • 8
    Redelmeier DA, Heller DN. Time preference in medical decision making and cost-effectiveness analysis. Med Decis Making 1993; 13: 2127.
  • 9
    Reyna VF. A theory of medical decision making and health: fuzzy trace theory. Med Decis Making 2008; 28: 85065.
  • 10
    Badia X, Roset M, Herdman M. Inconsistent responses in three preference-elicitation methods for health states. Soc Sci Med 1999; 49: 94350.
  • 11
    Cook KF, Ashton CM, Byrne MM, Brody B, Geraci J, Giesler RB, et al. A psychometric analysis of the measurement level of the rating scale, time trade-off, and standard gamble. Soc Sci Med 2001; 53: 127585.
  • 12
    Oliver A. Testing the internal consistency of the standard gamble in ‘success’ and ‘failure’ frames. Soc Sci Med 2004; 58: 221929.
  • 13
    Fraenkel L, Bogardus S, Concato J, Felson D. Preference for disclosure of information among patients with rheumatoid arthritis. Arthritis Rheum 2001; 45: 1369.
  • 14
    Reyna VF. How people make decisions that involve risk: a dual-processes approach. Curr Dir Psychol Sci 2004; 13: 606.
    Direct Link:
  • 15
    Reyna VF, Adam MB. Fuzzy-trace theory, risk communication, and product labeling in sexually transmitted diseases. Risk Anal 2003; 23: 32542.
  • 16
    Calvocoressi L, Kasl SV, Lee CH, Stolar M, Claus EB, Jones BA. A prospective study of perceived susceptibility to breast cancer and nonadherence to mammography screening guidelines in African American and white women ages 40 to 79 years. Cancer Epidemiol Biomarkers Prev 2004; 13: 2096105.
  • 17
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA. How making a risk estimate can change the feel of that risk: shifting attitudes toward breast cancer risk in a general public survey. Patient Educ Couns 2005; 57: 2949.
  • 18
    Peters E, Dieckmann N, Dixon A, Hibbard JH, Mertz CK. Less is more in presenting quality information to consumers. Med Care Res Rev 2007; 64: 16990.
  • 19
    Zikmund-Fisher BJ, Angott AM, Ubel PA. The benefits of discussing adjuvant therapies one at a time instead of all at once. Breast Cancer Res Treat 2011; 129: 7987.
  • 20
    Cohen SB, Emery P, Greenwald MW, Dougados M, Furie RA, Genovese MC, et al, for the REFLEX Trial Group. Rituximab for rheumatoid arthritis refractory to anti–tumor necrosis factor therapy: results of a multicenter, randomized, double-blind, placebo-controlled, phase III trial evaluating primary efficacy and safety at twenty-four weeks. Arthritis Rheum 2006; 54: 2793806.
  • 21
    Cole JC, Li T, Lin P, MacLean R, Wallenstein GV. Treatment impact on estimated medical expenditure and job loss likelihood in rheumatoid arthritis: re-examining quality of life outcomes from a randomized placebo-controlled clinical trial with abatacept. Rheumatology (Oxford) 2008; 47: 104450.
  • 22
    Emery P, Deodhar A, Rigby WF, Isaacs JD, Combe B, Racewicz AJ, et al. Efficacy and safety of different doses and retreatment of rituximab: a randomised, placebo-controlled trial in patients who are biological naïve with active rheumatoid arthritis and an inadequate response to methotrexate (Study Evaluating Rituximab's Efficacy in MTX iNadequate rEsponders (SERENE)). Ann Rheum Dis 2010; 69: 162935.
  • 23
    Emery P, Keystone E, Tony HP, Cantagrel A, van Vollenhoven R, Sanchez A, et al. IL-6 receptor inhibition with tocilizumab improves treatment outcomes in patients with rheumatoid arthritis refractory to anti-tumour necrosis factor biologicals: results from a 24-week multicentre randomised placebo-controlled trial. Ann Rheum Dis 2008; 67: 151623.
  • 24
    Fleischmann R, Vencovsky J, van Vollenhoven RF, Borenstein D, Box J, Coteur G, et al. Efficacy and safety of certolizumab pegol monotherapy every 4 weeks in patients with rheumatoid arthritis failing previous disease-modifying antirheumatic therapy: the FAST4WARD study. Ann Rheum Dis 2009; 68: 80511.
  • 25
    Gartlehner G, Hansen RA, Jonas BL, Thieda P, Lohr KN. The comparative efficacy and safety of biologics for the treatment of rheumatoid arthritis: a systematic review and metaanalysis. J Rheumatol 2006; 33: 2398408.
  • 26
    Genovese MC, Becker JC, Schiff M, Luggen M, Sherrer Y, Kremer J, et al. Abatacept for rheumatoid arthritis refractory to tumor necrosis factor α inhibition. N Engl J Med 2005; 353: 111423.
  • 27
    Kavanaugh A, Smolen JS, Emery P, Purcaru O, Keystone E, Richard L, et al. Effect of certolizumab pegol with methotrexate on home and work place productivity and social activities in patients with active rheumatoid arthritis. Arthritis Rheum 2009; 61: 1592600.
  • 28
    Keystone E, Emery P, Peterfy CG, Tak PP, Cohen S, Genovese MC, et al. Rituximab inhibits structural joint damage in patients with rheumatoid arthritis with an inadequate response to tumour necrosis factor inhibitor therapies. Ann Rheum Dis 2009; 68: 21621.
  • 29
    Lipsky PE, van der Heijde DM, St. Clair EW, Furst DE, Breedveld FC, Kalden JR, et al, for the Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. Infliximab and methotrexate in the treatment of rheumatoid arthritis. N Engl J Med 2000; 343: 1594602.
  • 30
    Maini RN, Taylor PC, Szechinski J, Pavelka K, Broll J, Balint G, et al, for the CHARISMA Study Group. Double-blind randomized controlled clinical trial of the interleukin-6 receptor antagonist, tocilizumab, in European patients with rheumatoid arthritis who had an incomplete response to methotrexate. Arthritis Rheum 2006; 54: 281729.
  • 31
    Mease PJ, Revicki DA, Szechinski J, Greenwald M, Kivitz A, Barile-Fabris L, et al. Improved health-related quality of life for patients with active rheumatoid arthritis receiving rituximab: results of the dose-ranging assessment. International clinical evaluation of rituximab in rheumatoid arthritis (DANCER) trial. J Rheumatol 2008; 35: 2030.
  • 32
    Smolen J, Landewe RB, Mease P, Brzezicki J, Mason D, Luijtens K, et al. Efficacy and safety of certolizumab pegol plus methotrexate in active rheumatoid arthritis: the RAPID 2 study. A randomised controlled trial. Ann Rheum Dis 2009; 68: 797804.
  • 33
    Smolen JS, Beaulieu A, Rubbert-Roth A, Ramos-Remus C, Rovensky J, Alecock E, et al, for the OPTION Investigators. Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (OPTION study): a double-blind, placebo-controlled, randomised trial. Lancet 2008; 371: 98797.
  • 34
    Strand V, Singh JA. Newer biological agents in rheumatoid arthritis: impact on health-related quality of life and productivity. Drugs 2010; 70: 12145.
  • 35
    Tobon GJ, Saraux A, Devauchelle-Pensec V. Effect of biologic agents on radiographic progression of rheumatoid arthritis. Rep Med Imaging 2010; 3: 3544.
  • 36
    Weinblatt ME, Kremer JM, Bankhurst AD, Bulpitt KJ, Fleischmann RM, Fox RI, et al. A trial of etanercept, a recombinant tumor necrosis factor receptor:Fc fusion protein, in patients with rheumatoid arthritis receiving methotrexate. N Engl J Med 1999; 340: 2539.
  • 37
    Wells G, Li T, Tugwell P. Investigation into the impact of abatacept on sleep quality in patients with rheumatoid arthritis, and the validity of the MOS-Sleep questionnaire Sleep Disturbance Scale. Ann Rheum Dis 2010; 69: 176873.
  • 38
    Westhovens R, Cole JC, Li T, Martin M, Maclean R, Lin P, et al. Improved health-related quality of life for rheumatoid arthritis patients treated with abatacept who have inadequate response to anti-TNF therapy in a double-blind, placebo-controlled, multicentre randomized clinical trial. Rheumatology (Oxford) 2006; 45: 123846.
  • 39
    Reyna VF, Brainerd CJ. Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learn Indiv Differ 2008; 18: 89107.
  • 40
    Tarn DM, Heritage J, Paterniti DA, Hays RD, Kravitz RL, Wenger NS. Physician communication when prescribing new medications. Arch Intern Med 2006; 166: 185562.
  • 41
    O'Connor AM. Validation of a decisional conflict scale. Med Decis Making 1995; 15: 2530.
  • 42
    O'Connor AM, Tugwell P, Wells GA, Elmslie T, Jolly E, Hollingworth G, et al. A decision aid for women considering hormone therapy after menopause: decision support framework and evaluation. Patient Educ Couns 1998; 33: 26779.
  • 43
    Sepucha K, Ozanne E, Silvia K, Partridge A, Mulley JA. An approach to measuring the quality of breast cancer decisions. Patient Educ Couns 2007; 65: 2619.
  • 44
    Pincus T. Routine assessment of patient index data (RAPID) scores. URL: http://mdhaq.org/Content/Forms/RAPID/RAPIDScores.pdf.
  • 45
    Sullivan MB, Iannaccone C, Cui J, Lu B, Batra K, Weinblatt M, et al. Evaluation of selected rheumatoid arthritis activity scores for office-based assessment. J Rheumatol 2010; 37: 24668.
  • 46
    Pincus T, Yazici Y, Bergman M, Maclean R, Harrington T. A proposed continuous quality improvement approach to assessment and management of patients with rheumatoid arthritis without formal joint counts, based on quantitative routine assessment of patient index data (RAPID) scores on a multidimensional health assessment questionnaire (MDHAQ). Best Pract Res Clin Rheumatol 2007; 21: 789804.
  • 47
    Barry J. Data analysis of pre-post study designs. URL: http://www.cscu.cornell.edu/news/statnews/stnews79.pdf.
  • 48
    Hamel MB, Lynn J, Teno JM, Covinsky KE, Wu AW. Age-related differences in care preferences, treatment decisions, and clinical outcomes of seriously ill hospitalized adults: lessons from SUPPORT. J Am Geriatr Soc 2000; 48: S17682.
  • 49
    Sommers BD, Beard CJ, D'Amico AV, Kaplan I, Richie JP, Zeckhauser RJ. Predictors of patient preferences and treatment choices for localized prostate cancer. Cancer 2008; 113: 205867.
  • 50
    Fraenkel L, Bogardus ST, Concato J, Felson DT, Wittink DR. Patient preferences for treatment of rheumatoid arthritis. Ann Rheum Dis 2004; 63: 13728.
  • 51
    Marshall T, Bryan S, Gill P, Greenfield S, Gutridge K. Predictors of patients' preferences for treatments to prevent heart disease. Heart 2006; 92: 16515.

Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

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