Nurse-led advance care planning with older people who have end-stage kidney disease: feasibility of a deferred entry randomised controlled trial incorporating an economic evaluation and mixed methods process evaluation (ACReDiT)
Bibliographic record
Abstract
BACKGROUND: Advance Care Planning is recommended for people with end-stage kidney disease but evidence is limited. Robust clinical trials are needed to investigate the impact of advance care planning in this population. There is little available data on cost-effectiveness to guide decision makers in allocating resources for advance care planning. Therefore we sought to determine the feasibility of a randomised controlled trial and to test methods for assessing cost-effectiveness. METHODS: A deferred entry, randomised controlled feasibility trial, incorporating economic and process evaluations, with people with end-stage kidney disease, aged 65 years or older, receiving haemodialysis, in two renal haemodialysis units in Northern Ireland, UK. A nurse facilitator helped the patient make an advance care plan identifying: a surrogate decision-maker; what the participant would like to happen in the future; any advance decision to refuse treatment; preferred place of care at end-of-life. RESULTS: Recruitment lasted 189 days; intervention and data collection 443 days. Of the 67 patients invited to participate 30 (45%) declined and 36 were randomised to immediate or deferred advance care plan groups. Twenty-two (61%) made an advance care plan and completed data collection at 12 weeks; 17 (47.2%) were able to identify a surrogate willing to be named in the advance care plan document. The intervention was well-received and encouraged end-of-life conversations, but did not succeed in helping patients to fully clarify their values or consider specific treatment choices. There was no significant difference in health system costs between the immediate and deferred groups. CONCLUSIONS: A trial of advance care planning with participants receiving haemodialysis is feasible and acceptable to patients, but challenging. A full trial would require a pool of potential participants five times larger than the number required to complete data collection at 3 months. Widening eligibility criteria to include younger (under 65 years of age) and less frail patients, together with special efforts to engage and retain surrogates may improve recruitment and retention. Traditional advance care planning outcomes may need to be supplemented with those that are defined by patients, helping them to participate with clinicians in making medical decisions. TRIAL REGISTRATION: Registered December 16, 2015. ClinicalTrials.gov Identifier: NCT02631200 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".