Dopamine agonist use and the risk of heart failure
Bibliographic record
Abstract
INTRODUCTION: A potential risk of heart failure was recently observed in randomized trials of the dopamine agonist pramipexole. The extent of the risk with this and other dopamine agonists is unknown. METHODS: We used the UK General Practice Research Database (GPRD) to identify all users of anti-parkinsonian drugs, 40-89 years of age, between 1997 and 2009. All incident heart failure cases were identified and classified as probable or possible on the basis of their treatment and mortality. Using a nested case-control approach, each case was matched with up to 10 controls selected among the cohort members. Incidence rate ratios (RR) of heart failure associated with the current use of dopamine agonists were estimated using conditional logistic regression, adjusted for covariates. RESULTS: The cohort included 26,814 users of anti-parkinsonian drugs, with 783 newly diagnosed with heart failure during follow-up (rate 8.7 per 1000 per year). The incidence rate of heart failure was increased with the current use of any dopamine agonist (RR = 1.58, 95% CI = 1.26-1.96), and particularly so for pramipexole (RR = 1.86, 95%CI = 1.21-2.85) and cabergoline (RR = 2.07, 95%CI = 1.39-3.07), compared with no use. The increase was not significant with ropinirole (RR = 1.23, 95%CI = 0.85-1.97) or pergolide (RR = 1.42, 95%CI = 0.95-2.12). Pramipexole was not associated with a significantly increased rate when compared with all other dopamine agonists collectively (RR = 1.28, 95%CI = 0.82-2.00). DISCUSSION: The use of dopamine agonists, especially pramipexole and cabergoline, is associated with an increased risk of heart failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".