Cardiorenal Effects of Angiotensin-converting enzyme inhibitors and Angiotensin receptor blockers in people underrepresented in trials: analysis of routinely collected data with validation against a target trial
Bibliographic record
Abstract
Abstract Background Cardiovascular disease (CVD) is a leading cause of death globally. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), compared in the ONTARGET trial, each prevent CVD. However, trial populations may not be representative of the general population. Methods Using trial replication methods within routine-care data, we explored replicability of the ONTARGET trial. For people prescribed an ACEi and/or an ARB in the UK Clinical Practice Research Datalink CPRD GOLD from 1/1/2001-31/7/2019, we applied trial criteria and propensity-score methods to create an ONTARGET trial-eligible cohort. Comparing ARB to ACEi, using Cox-proportional hazards models, we estimated hazard ratios for the primary composite trial outcome (cardiovascular death, myocardial infarction, stroke, or hospitalisation for heart failure), as well as secondary outcomes. As the pre-specified criteria were met confirming trial replicability, we then explored treatment effect heterogeneity of ACEi and ARB among three trial-underrepresented subgroups: females, those aged ≥75 years and those with chronic kidney disease. Findings In the trial-eligible population (n=137,155), results for the primary outcome met pre-specified criteria for similarity to the ONTARGET trial and demonstrated similar effects of ARB and ACEi, (HR 0.97 [95% CI: 0.93, 1.01]). When extending to trial-underrepresented groups, similar treatment effects of ARB and ACEi were observed by sex (P=0.09), age (P=0.70) and chronic kidney disease status (P=0.10). Interpretation We were able to replicate the results of the ONTARGET trial using routinely-collected healthcare data. Results suggest that trial findings were generalisable to population subgroups underrepresented in the trial. Funding GlaxoSmithKline Research in Context Evidence before this study Trial replication is an important methodology increasingly used to validate findings from observational studies against target trials. Unlike many naïve observational comparisons, a previous study demonstrated replicability of the ONTARGET trial using United States insurance claims data. However, it is unknown whether trial replicability can be extended to UK routinely-collected healthcare data. In addition, little work has been done to extend findings of comparative effectiveness among trial-underrepresented subgroups such as women, the elderly and those with chronic kidney disease despite high rates of prescribing of ACE Inhibitors and angiotensin receptor blockers among these groups in routine-care. Added value of this study With access to the individual patient data from the ONTARGET study and using propensity-score methods to address confounding, we demonstrated trial replicability using routinely-collected primary care data, representative of a large proportion of the UK population. We were then able to leverage the large sample size of the trial-eligible cohort to extend findings to trial-underrepresented groups and demonstrated similar comparative effectiveness for subgroups of patients treated with ARB and ACEi among women, those aged ≥75 years and those with chronic kidney disease. Implications of all the available evidence Our findings support similar effectiveness for cardiovascular and renal outcomes for patients receiving an ARB compared to an ACEi in a trial-eligible cohort and subgroups for which there is currently a lack of evidence of treatment effectiveness. Trial-replication methodology can be used to provide evidence for populations underrepresented in clinical trials.
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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 | 0.359 | 0.545 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".