Development and Preliminary Evaluation of Decision Support for Patients to Accept or Decline Implantable Cardioverter-Defibrillator Replacement at the Time of Battery Depletion
Bibliographic record
Abstract
Purpose. To systematically develop and conduct preliminary evaluation of a decision support intervention to engage patients and their families about whether to accept or decline implantable cardioverter-defibrillator (ICD) pulse generator replacement. Methods. A series of studies using multi-methods and guided by the Ottawa Decision Support Framework and the Normalization Process Theory: 1) an integrative review of patients’ perspectives on ICD decision-making; 2) a systematic review of the risks and benefits of ICD replacement; 3) an embedded mixed methods study to iteratively develop a patient decision aid (PDA) and simultaneously plan for its implementation; and 4) a feasibility pilot randomized controlled trial to evaluate ease of recruitment, decision support intervention delivery and data collection. Findings. The integrative review of 25 articles reported that ICD decision-making was difficult and the majority of patients misunderstood ICD therapy. The systematic review of 17 nonrandomized studies reported that complication rates are higher at replacement as compared to initial implant, mortality benefit post ICD replacement is unclear, and patients’ clinical profile can affect ICD’s effectiveness. Findings from both studies were used to draft a PDA. Interviews with 18 end-users (clinicians, patients, spouses) revealed that the current ICD replacement process is automated and needs to elicit patient preferences. The PDA was considered the optimal tool to initiate the discussion of options. In a feasibility trial, 30 patients were randomized to the decision support intervention (PDA + decision coaching) (n=15) or usual care (n=15). The intervention was used as intended, users found the PDA acceptable but acceptability of decision coaching was variable. Patients exposed to the intervention had better knowledge scores compared to controls. Conclusion. The Ottawa Decision Support Framework and Normalization Process Theory were complementary frameworks to ensure that the decision support intervention has the potential for implementation. To determine whether this approach was successful, future research is required to evaluate and implement the intervention in clinical practice. Findings from the feasibility study will be used to design an effectiveness trial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".