Propensity Score Analysis of Possible Medication Effects on Outcomes in Patients With Systemic Right Ventricles
Bibliographic record
Abstract
Background: Patients with systemic right ventricle (SRV), either d-transposition of the great arteries following an atrial switch procedure or congenitally corrected transposition of the great arteries, develop severe right ventricular dysfunction, prompting appropriate medical therapy. However, the efficacy of beta-blockers and angiotensin receptor blockers or angiotensin-converting enzyme inhibitors (ACEI) in SRV patients is unproven. Objectives: The objective of this study was to determine the effects of ACEI/ARB and beta-blockers on outcomes in SRV patients after accounting for likely cofounders affecting their use. Methods: From a retrospective, multicenter study on heart failure-related outcome in individuals with SRV, those who were taking an ACEI/ARB, beta-blocker, or both of these medication were identified. We performed a propensity analysis to match them to those not using these medications at their initial visit. Matching was based on a propensity score, which captured co-morbidities, demographics, and baseline echocardiographic parameters. Primary outcome of death, transplant, or mechanical circulatory support, and secondary outcomes of heart failure hospitalizations/atrial arrhythmias were analyzed respectively. Results: We identified 393 patients taking ACEI/ARB or beta-blocker, or taking both a beta-blocker and ACEI/ARB (62.1% male, median age 31.3 years) and 484 patients (56.4% male, median age of 26.0 years) who were neither on a beta-blocker nor on ACEI/ARB at the time of initial clinic visit. Median follow-up was ∼8 years. After propensity matching, medication use was not associated with decreased mortality, heart failure hospitalizations, or arrhythmias. Hazard ratios remained positive for beta blockers, implying potential harm rather than benefit. Conclusions: In this large multicenter propensity-matched observational study, patients with SRV taking beta-blockers or ACEI/ARB did not have a benefit in survival or reduced hospitalization. The likelihood of demonstrating favorable effects in larger studies appears remote.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".