The Effect of Bromocriptine on Left Ventricular Functional Recovery in Peripartum Cardiomyopathy: Insights from the BRO-HF Retrospective Cohort Study
Bibliographic record
Abstract
AIMS: Bromocriptine is thought to facilitate left ventricular (LV) recovery in peripartum cardiomyopathy (PPCM) through inhibition of prolactin secretion. However, this potential therapeutic effect remains controversial and was incompletely studied in diverse populations. METHODS AND RESULTS: Consecutive women with new-onset PPCM (n = 76) between 1994 and 2015 in Quebec, Canada, were classified according to treatment (n = 8, 11%) vs. no treatment (n = 68, 89%) with bromocriptine. We assessed LV functional recovery at mid-term (6 months) and long-term (last follow-up) and compared outcomes among groups. Women treated with bromocriptine experienced better mid-term left ventricular ejection fraction (LVEF) recovery from 23 ± 10% at baseline to 55 ± 12% at 6 months, compared with a change from 30 ± 12% at baseline to 45 ± 13% at 6 months in women treated with standard medical therapy (P interaction < 0.01). At long-term, a similar positive association was found with bromocriptine (9% greater LVEF variation, P interaction < 0.01). In linear regressions adjusted for obstetrical, clinical, echocardiographic, and pharmacological variables, treatment with bromocriptine was associated with a greater improvement in LVEF [β coefficient (standard error), 14.1 (4.4); P = 0.03]. However, there was no significant association between bromocriptine use and the combined occurrence of all-cause death and heart failure events (hazard ratio, 1.18; 95% confidence interval, 0.15 to 9.31), using univariable Cox regressions based over a cumulative follow-up period of 285 patient-years. CONCLUSIONS: In women newly diagnosed with PPCM, treatment with bromocriptine was independently associated with greater LV functional recovery.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".