Preferences of Patients With Chronic Kidney Disease for Invasive Versus Conservative Treatment of Acute Coronary Syndrome: A Discrete Choice Experiment
Bibliographic record
Abstract
Background Patients with chronic kidney disease (CKD) can experience acute coronary syndromes (ACS) with high morbidity and mortality. Early invasive management of ACS is recommended for most high‐risk patients; however, choosing between an early invasive versus conservative management approach may be influenced by the unique risk of kidney failure for patients with CKD. Methods and Results This discrete choice experiment measured the preferences of patients with CKD for future cardiovascular events versus acute kidney injury and kidney failure following invasive heart procedures for ACS. The discrete choice experiment, consisting of 8 choice tasks, was administered to adult patients attending 2 CKD clinics in Calgary, Alberta. The part‐worth utilities of each attribute were determined using multinomial logit models, and preference heterogeneity was explored using latent class analysis. A total of 140 patients completed the discrete choice experiment. The mean age of patients was 64 years, 52% were male, and mean estimated glomerular filtration rate was 37 mL/min per 1.73 m 2 . Across the range of levels, risk of mortality was the most important attribute, followed by risk of end‐stage kidney disease and risk of recurrent myocardial infarction. Latent class analysis identified 2 distinct preference groups. The largest group included 115 (83%) patients, who placed the greatest value on treatment benefits and expressed the strongest preference for reducing mortality. A second group of 25 (17%) patients was identified who were procedure averse and had a strong preference toward conservative management of ACS and avoiding acute kidney injury requiring dialysis. Conclusions The preferences of most patients with CKD for management of ACS were most influenced by lowering mortality. However, a distinct subgroup of patients was strongly averse to invasive management. This highlights the importance of clarifying patient preferences to ensure treatment decisions are aligned with patient values.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".