| Literature DB >> 30486836 |
Ted A Skolarus1,2, Sarah T Hawley3, Daniela A Wittmann4, Jane Forman3, Tabitha Metreger3, Jordan B Sparks3, Kevin Zhu3, Megan E V Caram3,5, Brent K Hollenbeck4, Danil V Makarov6,7, John T Leppert8,9, Jeremy B Shelton10, Vahakn Shahinian11, Sriram Srinivasaraghavan12, Anne E Sales3,13.
Abstract
BACKGROUND: Men with prostate cancer are often castrated with long-acting injectable drugs termed androgen deprivation therapy (ADT). Although many benefit, ADT is also used in patients with little or nothing to gain. The best ways to stop this practice are unknown, and range from blunt pharmacy restrictions to informed decision-making. This study will refine and pilot two different de-implementation strategies for reducing ADT use among those unlikely to benefit in preparation for a comparative effectiveness trial. METHODS/Entities:
Keywords: Androgen deprivation therapy (ADT); Behavior change; Castration; Choosing wisely; De-implementation; Decision-making; Discrete choice; Formulary restriction; Implementation science; Intervention; Low value care; Stakeholder
Mesh:
Substances:
Year: 2018 PMID: 30486836 PMCID: PMC6262964 DOI: 10.1186/s13012-018-0833-7
Source DB: PubMed Journal: Implement Sci ISSN: 1748-5908 Impact factor: 7.327
Fig. 1Conceptual model for de-implementation of low value prostate cancer care
Data sources and variables
| Information | Source | Variables |
|---|---|---|
| Aim 1 | ||
| Barriers and facilitators to stopping low value chemical castration | 12–16 urologists, selected patients from high and a few low ADT facilities | Semi-structured interview data coded into themes and TDF domains for use in Aim 2 |
| Aim 2 | ||
| Themes, barriers, attributes for discrete choice scenarios | TDF domains and themes related to treating localized prostate cancer with ADT from Aim 1 | 4–6 attributes with varying levels (e.g., |
| Discrete choice experiment (DCE) | Mixed multinomial logit analysis of DCE survey results among national sample of urologists | Choice sets, attributes, model outcomes (barrier attribute weights) |
| Behavior change techniques for most relevant attributes for Aim 3 | Michie et al. | Relevant TDF domains from the DCE model output for use in tailoring strategies |
Examples of potential pilot de-implementation interventions
| Formulary restriction | |
| Prior authorization | Used in infectious disease |
| Criteria for use | Currently used for restricted drugs |
| Medication safety (VAMedSAFE) | Evaluate, educate, and prevent adverse events |
| Decision-making | |
| Decision aid using a brief in-office pro/con (e.g., Option Grid™) | Commercialized shared decision-making for prostate cancer |
| Provider training in communication and values elicitation | Evidence-based practice though difficult to implement/sustain |
| Informed consent for ADT | VA iMed consent |
Methodological issues requiring Aim 3 pilot evaluation prior to a full-scale de-implementation trial
| Issue | Assessment | Potential outcome |
|---|---|---|
| Recruitment randomization scalability | Monitor proposed recruitment strategy at each facility; check practicality of cluster randomization of facilities; identify issues of participation refusal or withdrawal; acceptability of randomization; number of eligible participants per month; compare clinic flow across recruitment strategies | Select most effective recruitment and randomization strategy; trial messaging to sites; discern patient, provider and cluster sample sizes; refining eligibility screening |
| Acceptability of intervention | Check acceptability of interventions with urologists and clinic staff at pilot sites; settings for each intervention; consent and documentation practices; tailoring strategies are acceptable; timing of intervention relative to visit | Identify acceptable components of each intervention in clinical practice; consent processes; efficient documentation practices |
| Feasibility in clinical practice | Assess burden on clinic staff and providers to participate; monitor clinical time and workflow; assess adherence to intervention; technical performance of EMR-based intervention(s); participants representative of those expected in full-scale trial; intervention fidelity | Time and resources needed to roll out in randomized sites; learn research and clinic administrative staff roles for trial; standardization; scheduling practices |
| Data collection and outcome assessment | Monitor follow up practices for patients on ADT; monitor for asymmetric attrition/retention across intervention sites; missing data; review choice of primary outcome, study design; effect variability | Willingness to participate by intervention preference; effect size; consider hybrid study; duration; full-scale protocol |