ORAL ABSTRACTS: PATIENT DECISION MAKING & DECISION AIDS
Method(s): A paper-based decision aid for cardiac surgery was developed and evaluated within the context of a pre-post study design. Surgeons were trained in shared decision making through a web based programme. Research team members acted as decisional coaches, going through the decision aids with the patients and their families, and remaining available for consultation. Patients (65 and over) undergoing isolated valve, Coronary Artery Bypass Graft (CABG) or CABG+Valve surgery were eligible. Participants in the pre-intervention phase (n=100) were followed through the standard course of care to establish a baseline. Participants in the interventional group (n=100) were presented with a decision aid following cardiac catheterization populated with individualized risk assessment, personal profile, and co-morbidity status. Surgeon training in shared decision making occurred just prior to instituting the post intervention phase. Decisional coaching only applied to the post intervention phase. Both groups were assessed pre-operatively on comprehension (Maritime Heart Center Comprehension Scale), decisional conflict (Decisional Conflict Scale), decisional quality (9-item Shared Decision Making Questionnaire), anxiety and depression (Hospital Anxiety and Depression Scale), Primary outcomes were comprehension and decisional quality scores.
Result(s): Patients who received decision aids through a formalized shared decision making approach scored higher in comprehension (median: 15.0; IQR: 12.0-18.0) compared to those who did not (median: 9.0; IQR: 7.0-12.0) (p < 0.001). Decisional quality was greater in the interventional group (median: 80.0; IQR: 73.0-91.0) compared to those in the pre-intervention group (median: 75.0; IQR: 62.0-82.0) (p<0.05). Anxiety and depression scores showed no significant difference between pre-intervention (median: 9.0; IQR: 4.0-12.0) and post-intervention groups (median: 7.0; IQR: 5.0-11.0) (p<0.28).
Conclusion(s): Institution of a formalized shared decision making process including individualized decision aids improve comprehension of risks, benefits and alternatives to cardiac surgery, decisional quality, and did not result in increased levels of anxiety.
Method(s): Patients were recruited from 5 centers and through advertisement. Inclusion criteria were: (i) age ≥18 years (ii) diagnosis of rheumatoid arthritis (RA), knee osteoarthritis (OA), or osteoporosis/osteopenia (OP) (iii) adequate cognitive status and, (iv) ability to communicate in English or Spanish language. Our primary outcome was disease knowledge and secondary measures included decisional conflict, self-efficacy and disease management. Assessments were conducted before and after viewing MM-PtET, at 3 and 6 months. The Control Preference Scale (CPS) was used to characterize participants according to their preferred role in decision making (passive vs shared vs active role).
Result(s): 665 participants were randomized (331=MM-PtET, 334=written booklet). Mean age was 59.8±12.1 years, 87% were female, 65% non-White, 20% had inadequate health literacy levels and 26% answered the questionnaire in Spanish. Thirty-three percent had a diagnosis of OA, 34% OP, and 33% RA; 472 (232=MM-PtET, 240=booklet) and 522 (257=MM-PtET, 265=booklet) participants returned their questionnaires at 3 and 6 months, respectively. Most patients reported an active decision-making role preference in the intervention (48% active, 36% shared, and 15% passive) and control (47% active, 36% shared, 17% passive) groups. Greater knowledge scores were observed after viewing the MM-PtET compared with reading the booklet in patients with a shared role preference (p=0.04). Compared to patients in the control group, patients in the intervention group with a passive role preference had less decisional conflict (p=0.04) and better decision management (p=0.02) at 3 months. However, at 6 months improvements from baseline were only significant for patients with an active (decisional conflict, p=0.04) and shared role preference (disease management, p=0.03). Univariate analysis showed that greater improvements in knowledge (regardless of assignment) were associated with passive role preference compared to active (p=0.001, pre&post; p=0.03, pre&6-month) and shared role (p=0.01) preference.
Conclusion(s): Our MM-PtET improved outcomes after intervention, 3 and 6 months. However, the benefits varied according to the decision-making role preference. Given the observed differences, it is important that educational interventions are tailored to the patients’ preferences about their involvement in the decision-making processes.
Method(s): A single centre, prospective, mixed-methods study, over 30 months. Fifty-eight women aged between 16-40, attending a cancer hospital with a new diagnosis of cancer were recruited. Thirty-four women, decided not to preserve their fertility in oncology (Group 1). Twenty-four women were referred to the fertility expert (Group 2). Data was collected using patient-reported outcome measures which were administered at baseline, pre-and-post fertility consultation (T1 & T2) and post cancer treatment (T3). A subsample (n=15) also took part in a qualitative interview. The interview transcripts were coded by two members of the study team using NVivo and analysed using a thematic approach.
Result(s): In Group 1, reasons for declining a referral to the fertility expert were i) already completed their family (44%), ii) worried about future survival/delaying cancer treatment (18%), iii) too old for FP treatment/subsequent pregnancy (18%), iv) never wanted children (12%), iv) had an oestrogen positive cancer (3%), vi) not told/given the option of FP (3%).
The mean age of the women in Group 2 was 29.2 years, range (16 - 39 years). These women had breast cancer (60.9%), lymphoma (17.4%), sarcoma (4.3%) cervical (4.3%) rectal (4.3%) brain (4.3%) and tonsil cancer (4.3%). Median time of referral from oncology to the fertility expert was 7 days (range 1 – 29 days). Sixteen women preserved their fertility; six opted for oocyte freezing, seven embryo freezing and three both. The qualitative analysis revealed that the main reason hindering the FP decision was the lack of FP treatment information received at the point of diagnosis/treatment planning stage in oncology. Other reasons included age, costs, having a hormone sensitive and/or aggressive cancer, time pressure to make the decision and/or start cancer treatment, perceived risks around delaying cancer treatment, not feeling re-assured by clinical advice, fear of the FP treatment and uncertainty over IVF success rates.
Conclusion(s): Women wanted to receive some specialist FP information sooner and in the context of their cancer care, in advance of seeing the fertility expert. These findings are informing the development of a FP decision aid for use in oncology to better support and prepare women needing cancer treatment with fertility decisions.
Method(s): An environmental scan of open-access decision support resources carried out using systematic review methods (December 2015). Three data sources were searched: internet (Google): healthcare decision support repositories (Decision Aids Library Inventory; Trips; NHS Evidence; National Guidelines Clearinghouse; Clinical Trials); shared decision making experts (SHARED-L distribution list). Inclusion criteria were, information about: women receiving cancer treatment; consequences cancer treatment on fertility; fertility preservation options; statements supporting women’s choices. The International Patient Decision Aid Standards (IPDAS) criteria informed the data extraction sheet developed to elicit information about resources’ content. Data were evaluated critically against these components, assessing resources’ validity to support actively people’s decision making between options.
Result(s): Of the 116 patient decision aids and 42 clinical guidelines identified, 24 decision aids met the inclusion criteria. Resources varied in amount (2 – 90 pages) and type (pdf – App) of information. Most were rated as difficult to read (Flesch <60); few were endorsed independently (e.g. DALI, Crystal Mark). A third stated the resource’s purpose was to support women’s decision making; most aimed to inform and prepare women for fertility preservation and/or infertility procedures. Most resources provided questions for women to engage with health professionals rather than prompts and structures supporting deliberative thinking about which options fit best into their life now, and after cancer treatment (e.g. decision maps, parallel presentation options and attributes; risk figures, value clarification prompts). Most descriptions of cancer and infertility missed out information from one of the five schema people need when making sense of illness. Most resources met less than 50% of the IPDAS criteria.
Conclusion(s): Resources provided information about fertility preservation and infertility treatment options; about 20% adhered to IPDAS guidance and readability standards. Most resources were designed for women with breast cancer after referral to infertility services. A decision aid supporting women’s deliberation about fertility preservation, or not, when receiving treatment for any cancer is likely to meet UK and international service needs.
Method(s): One hundred eighty-one women aged between 30 and 45 were randomly presented with material about BRCA1 or BRCA2 after stratifying for Having children or not and Age group, and completed a questionnaire, which included questions about preferences, knowledge, risk perception, and socio-demographic information. Choice was analysed using binary logistic regression models and with a model selection approach. First, all predictor variables were included in the model, and then the model was progressively reduced, until the plausibility of the model was no longer increased by removing variables.
Result(s): Results show that intensified surveillance was the preferred option (64.6%), followed by surgery (24.3%). Seven predictors of choice were included in the model: knowing that life expectancy is longer with surgery, perceived comprehension of all the consequences of testing, previous knowledge about BRCA testing, anticipated worry about developing cancer, feelings of risk (all associated with a higher likelihood to prefer surgery over intensified surveillance), having childbearing intentions, and the extent to which childbearing intentions affected choice (both associated with a lower likelihood to prefer surgery).
Conclusion(s): Our study provides useful indications for genetic counsellors in order to promote informed uptake of preventive removal of ovaries in the context of BRCA mutation. Based on our findings, we suggest that: a) during counselling, when describing the available options, the comparative effectiveness of surgery and surveillance and their effect on life expectancy should be made clear; b) counsellors should ensure that patients correctly feel they understood all the consequences of their decision; c) since previous knowledge on BRCA testing is beneficial, information provision prior to counselling may be useful; d) affective-based risk perception drives preference for prophylactic surgery more than cognitive-based risk perception, although risk communication is an essential component of genetic counselling; e) in addition to discussing childbearing intentions, also the effect of childbearing intentions on choice should be considered.
Method(s): A random sample of 400 patients aged 40 years or older, receiving diabetes care from the Virginia Commonwealth University Health System between August 2012 – August 2013 were mailed a survey containing the PM-CGS, perceived competence, trust in the physician, and self-management behaviors. External validity was evaluated via a structural equation model (SEM) in order to assess a potential association with self-management behaviors.
Result(s): A total of 192 respondents reported engaging in a goal setting discussion with their clinician and were included in analyses. The overall fit of the unadjusted model was good (χ2 = 4827.38, p<.001; RMSEA = .07). Collaborative goal setting was significantly associated with increased self-efficacy (p<.03) as well as with self-management behaviors (p<.001). Furthermore, self-efficacy was significantly associated with an increase in a patient’s self-management behaviors (p<.001). Results (p<0.05) supported that the relationship between collaborative goal setting and self-management was partially mediated by self-efficacy.
Conclusion(s): Results here provided external validity of the PM-CGS. Further testing is needed to establish the pathways by which collaborative goal setting impacts clinical outcomes.