Purpose: The purpose of this study was to develop and evaluate an Internet-based patients’ decision aid (PtDA) for surgical versus nonsurgical management of knee pain due to chronic osteoarthritis.
Method: We created an Internet-based PtDA that provided a) up-to-date, balanced clinical information, and b) decision support in four theory-based deliberative steps: 1) information comprehension; 2) values clarification; 3) consideration of personal resources; and 4) formation of an action plan. Clinical information was abstracted from original sources cited in existing paper- and video-based PtDAs for knee osteoarthritis, and updated to reflect current literature. Information was presented in lay language with optional audio narration. After pilot testing, patients were recruited who were eligible for and actively considering knee surgery. Participants were offered a computer in a private room at the clinic to complete and evaluate the PtDA in terms of: a) usability (5 items); b) post-PtDA Information Comprehension (5 items), Preparation for Decision Making, and Decision Self-efficacy; and c) pre/post-PtDA Decisional Conflict and treatment preferences.
Result: 126 patients participated. Usability: Participants reported that: the PtDA was easy to use (98%), the information was clear (90%), the length was appropriate (100%), it was appropriately detailed (90%), and it held their interest (97%). 100% of participants preferred using the PtDA on a home or public computer rather than at the clinic. Post-PtDA Information Comprehension, Preparation for Decision Making, Decision Self-Efficacy: Participants scored an average of 75% (min. 60%; max. 100%) correct responses. The median Preparation for Decision Making score was 74 (interquartile range = 30). The median Decision Self-efficacy score was 100 (interquartile range = 13.6). Pre/post-PtDA Decisional Conflict, Treatment Preferences: Viewing the PtDA reduced Decisional Conflict scores from 31.1 to 19.53 (p < 0.01). At baseline, 63.5% preferred nonsurgical therapies, 15.1% were unsure/no preference, and 21.4% preferred surgery; of those with a stated preference, 67.5% held that preference strongly, and 11.8% held it weakly. After viewing the PtDA, similar percentages of those who had been “unsure/no preference” shifted to the nonsurgical (42%) and the surgical (47%) preference sub-groups, and their strength of preference scores increased.
Conclusion: An Internet-based PtDA is usable and effective for patients considering surgical versus nonsurgical management of knee pain due to osteoarthritis.
Purpose: We aimed to elicit women’s responses to information about the nature and extent of overdiagnosis in screening mammography (detecting disease that would not present clinically during the woman’s lifetime) and explore how awareness of this largely unfamiliar issue may influence screening attitudes and intentions.
Methods: Fifty women aged 40-79 years with no personal history of breast cancer, varying in screening participation and educational background, participated in eight age-stratified focus groups. Each session included a consumer-friendly audiovisual presentation to explain overdiagnosis in screening mammography, incorporating different published estimates of its rate of occurrence (1-10%, 30%, and 50% of cancers diagnosed among regularly screened women), as well as evidence-based information on the mortality benefit of screening. Participants engaged in group discussions, guided by a pair of moderators, exploring their attitudes towards overdiagnosis, reactions to the overdiagnosis estimates, the influence of this information on screening intentions, and views about different strategies for communicating about screening. Discussions were audio-recorded, transcribed, and analysed thematically.
Results: As expected, prior awareness of overdiagnosis was limited. However, after questions were addressed and clarifications offered, most participants gained an understanding of this complex issue. Learning about overdiagnosis made some women perceive a need for more careful personal decision-making about screening, particularly if further research were to confirm the highest estimate (around 50%). In contrast, the estimates of 1-10% and 30% overdiagnosis had limited impact. Many women felt strongly committed to screening, regardless of the level of overdiagnosis. For some women, the information raised concerns not about whether to screen but rather whether to treat a screen-detected cancer or consider alternative approaches (e.g., ‘watchful waiting’). Most participants felt that the information presented was important and should be available to enable women to make informed choices, although many also wanted to be encouraged to screen.
Conclusions: Women had diverse responses to overdiagnosis and the different estimates of its magnitude. Some women would rethink their screening intentions at the 50% estimate but few at the lower or intermediate estimates. We found that lay women from a range of socioeconomic backgrounds can be informed about overdiagnosis, and that women valued the information. Providing information about overdiagnosis would facilitate better informed decisions about mammography screening. Future research should quantify any impact such information may have on screening participation.
Purpose: To compare quality-adjusted survival between three treatment strategies for advanced bladder cancer that differ in side effects and survival. There exists considerable controversy over which factors should direct shared decision making for these patients.
Method: We evaluated three treatment strategies for advanced bladder cancer using a decision-analytic Markov model based on a formal literature review. The base case was assumed to be a 65-year-old person with newly diagnosed MIBC. The model used a patient perspective a lifetime time horizon, and one month cycle-length. Three strategies were evaluated: (1) immediate radical cystectomy followed by adjuvant chemotherapy for high risk (>=T3) findings on pathology (RC); 2. immediate neoadjuvant chemotherapy followed by radical cystectomy (NC&RC); 3. trimodal therapy consisting of immediate pelvic and nodal radiation therapy with concurrent systemic chemotherapy followed by cystectomy for patients who do not enter remission (TMT). Outcomes were life expectancy (LE) and quality-adjusted life expectance (QALE).
Result: LE of 11.9 year was optimized with TMT treatment, while the discounted QALE of 8.3 years was maximized with NC&RC treatment. RC had the lowest LE (10.7 years) and QALE (7.6 years) compared to both other treatments, a difference that was sensitive to changes in both perioperative death from radical cystectomy and long term surgical complications. When we adjusted for effectiveness of BCG, remission rate post-TMT, and metastatic potential of the tumour, TMT maximized QALE over NC&RC.
Conclusion: For patients with newly diagnosed invasive bladder cancer, management with either neoadjuvant chemotherapy with radical cystectomy or radical radiation therapy with concurrent systemic chemotherapy with or without cystectomy offers improved life expectancy and quality-adjusted life expectancy compared to radical cystectomy alone. Thus, patients with localized, aggressive bladder cancer benefit from the use of systemic chemotherapy in addition to either radiotherapy or radical surgery early in their treatment. Deciding between surgical-based and radiation-based interventions is very sensitive to patient preferences.
Purpose: Effective patients’ decision aids (PtDAs) help patients understand clinical information and reduce decisional conflict. This study’s purpose was to test whether PtDAs that also explicitly provide guidance through four “deliberative steps” yield additional decision-making gains, and whether sub-groups of patients engage differently with the information and deliberative steps.
Method: We created two versions of a web-based PtDA regarding the surgical/nonsurgical management of chronic knee osteoarthritis. The Information-Provision version provided clinical information at an overview level (with optional “More Information” links to detail) and implicit deliberative guidance. The Information+Deliberation version provided the same clinical information and links, as well as explicit guidance through four deliberative steps: 1) information comprehension; 2) values clarification; 3) consideration of social resources; and 4) formation of an action plan. Each step offered an optional deliberative activity. In both versions, the program tracked selection of the information links; in the Information+Deliberation version, the program tracked engagement with the deliberative activities. Eligible participants (N = 126) were randomly assigned to one of the versions. Across-version analyses compared scores on self-reported post-PtDA Preparation for Decision Making, Decision Self-efficacy, and Decisional Conflict scales. Sub-groups using the “More Information” links and the deliberative activities were characterized.
Result: Across-Group Differences: There were no statistically significant across-version-group differences in mean Preparation for Decision Making, Decision Self-efficacy, or Decisional Conflict scores. In both groups (N = 126), 46% of participants engaged with the “More Information” links; they were primarily female, Caucasian, college-degreed, reported higher decisional conflict, and had viewed the Information+Deliberation version. In the Information+Deliberation group (n = 64), 43% engaged with the interactive deliberative activities. This sub-group was primarily female, Caucasian, college-educated, and reported higher levels of pain, higher decisional conflict scores, and greater familiarity with the decision. Across-Sub-groups: Increased engagement was significantly associated with increased self-efficacy (b = -9.08, p = 0.01) and decreased decisional conflict (b= -13.29, p = 0.007).
Conclusion: These results suggest that a) in chronic care, the effect of implicit versus explicit guidance may not vary, on average, b) sub-groups exist with differing “deliberative styles”, and c) some deliberative styles may benefit more from interactive features that provide personalized decision support.
Purpose:To evaluate the ability of a 6-item measure of physician numeracy (the ability to use numbers and numeric concepts in the context of taking care of patients) to predict enthusiasm for cancer screening.
Methods: We developed the content and design of the questionnaire through an iterative 8 month process supporting content validity. Our final measure consisted of 6 items which appeared to best predict accurate perceptions of the benefit of screening mammography on pilot testing: 2 items from the Medical Data Interpretation Test (MDIT) and 4 new items. To measure enthusiasm for cancer screening we modified items from a previous survey “Enthusiasm for Cancer Screening in the United States," (JAMA 2004). We distributed a paper survey to 139 internists and medicine sub-specialists attending an annual meeting. Numeracy scores were created on a scale from 0-6 based on the number of questions correct. Answers to the enthusiasm for cancer screening items were aggregated, higher scores indicating more enthusiasm for cancer screening. We calculated the Pearson correlation coefficient between the physician numeracy score and scores on the enthusiasm for screening scale. We used multiple regression to adjust for demographics.
Results: 88 participants returned completed surveys representing a 63% response rate. No question had more than one non-response. Numeracy scores ranged from 2-6 and with 63% scoring 6 out of 6 correct. Numeracy scores had a significant negative correlation with enthusiasm for cancer screening scores (r=0.26, p=0.01). This relationship remained significant after correcting for gender and year graduated from medical school.
Conclusions: We found that physician numeracy affects attitudes toward cancer screening. Different attitudes toward cancer screening could result in different styles of risk communication and medical decision-making.
|Calculate 2 absolute risk reductions from relative risk reductions and baseline risks and select the larger. (MDIT)|| |
|Calculate absolute risk reduction from 2 absolute risks. (MDIT)|| |
|Know that survival rates are a biased estimate of the benefits of cancer screening tests.|| |
|Know that all-cause mortality benefits of treating a single disease will decrease with age.|| |
|Know that a statement about relative risk reduction is not equivalent to a statement of absolute risk reduction.|| |
|Know that pre-test probability affects the positive predictive value of a test.|| |
Purpose: Regulatory agencies show a growing interest in quantitative models for risk-benefit assessments to increase decision transparency. In addition, regulators increasingly incorporate the view of patients regarding benefit-risk trade offs. Although patient perspectives are sometimes taken into account through patient panels, little is known on how to integrate elicited preferences into the decision making process. There is also little knowledge on how to integrate these preferences with clinical performance data and how to use knowledge about the uncertainty surrounding both types of parameters (preference and performance). The objective of this study was to demonstrate how patient preferences can be integrated in a Bayesian framework for quantitative benefit-risk assessment.
Method: An MCDA model was developed that integrates clinical trial data, patient preference information and the uncertainty surrounding these estimates. Stochastic characteristics of preference weights and drug performance parameters can be approximated from stated preference studies (e.g. conjoint analysis or direct rankings obtained from MCDA studies) and clinical performance data estimated from systematic reviews or RCT’s. Risk and benefit scores of drugs are then simulated using approximated distributions. All simulations of a particular drug where the weighted benefits are higher than the weighted risks are considered acceptable. Then, the acceptability is calculated. Using value of information metrics, residual uncertainty and the impact of reducing uncertainty on parameters are calculated. A ‘risk-benefit factsheet’ with acceptability graphs is provided, to facilitate decision makers in their appraisal.
Result: We applied the method in two cases, namely a case with anti-depressants and a case on colorectal cancer screening. For both cases we demonstrate the potential utility of applying the MCDA framework to the decision-making process.
Conclusion: Using Bayesian statistics it is possible to include patient preference in a quantitative risk-benefit assessment model. The model allows integration of stochastic uncertainty as well as (preference) heterogeneity. The study also demonstrates that comprehensive presentation of the data is possible. The usefulness of the approach needs to be determined in real-life case studies.