#530 Cardiorenal effects of dual blockade with ACE inhibitors and ARBs among people with CKD: emulation of a reference trial (ONTARGET) using CPRD
Bibliographic record
Abstract
Abstract Background and Aims Cardiovascular disease (CVD) is a leading cause of death globally, and individuals with chronic kidney disease (CKD) are at increased risk. Results from the ONTARGET trial, in conjunction with the ALTITUDE and VA-Nephron-D studies, led to an end of recommendations for dual ACE inhibitor and ARB therapy due to an increase in acute kidney injury. However, these studies had low power to address long-term kidney outcomes and there remains uncertainty about whether dual therapy could be effective at reducing adverse cardiovascular and renal outcomes in patients with CKD. Observational data provides an opportunity to explore such hypotheses in subgroups underrepresented in trials and with power and follow-up enabling estimation of risk of rare outcomes. We aimed to use ONTARGET to perform a reference trial emulation analysis, before extending analysis to explore treatment effectiveness of dual ARB and ACEi use vs ACEi alone in preventing cardiovascular and renal outcomes among those with CKD. Method Using routinely-collected data from the UK Clinical Practice Research Datalink (CPRD) Aurum linked with Hospital Episode Statistics secondary care data, we applied the ONTARGET trial eligibility criteria to patients prescribed an ARB/ACE inhibitor between 1/1/2001-31/7/2019. As the number of patients receiving prescriptions for both medications on the same day was likely to be small in routine care, we used an operational definition to capture dual users. Outcomes included ONTARGET primary cardiovascular composite outcome of cardiovascular-related death, myocardial infarction, stroke, or hospitalisation for heart failure and a composite renal outcome of ≥50% reduction in GFR or end-stage kidney disease (ESKD). Within the trial-eligible cohort, outcomes of interest were compared between groups prescribed dual therapy vs ACE inhibitors alone using a propensity-score—weighted time-to-event analysis using a Cox proportional hazards model. Conditional on successfully benchmarking results against the ONTARGET trial, we explored treatment effect heterogeneity by chronic kidney disease (CKD) at baseline, with CKD defined as estimated glomerular rate (eGFR) < 60 ml/min/1.73 m2. Results 412,406 trial-eligible patients in CPRD were included in analysis. Among those with non-missing eGFR at baseline, 37% had CKD (Table 1). We found similar effectiveness of dual therapy and ACE inhibitors in reducing the risk of the primary composite cardiovascular outcome (HR 0.98 (95% CI: 0.93, 1.03), consistent with the ONTARGET trial results (HR 0.99 (95% CI: 0.92, 1.07), with no evidence of heterogeneity by CKD (P-value for interaction = 0.14). However, dual therapy use was associated with a greater risk for the composite renal outcome compared to ACE inhibitor, HR 1.24 (95% CI: 1.14, 1.35), with no evidence of heterogeneity by CKD (P = 0.93) (Fig. 1). Analysing components of the composite renal outcome separately gave consistent results (≥50% reduction in GFR: HR 1.22 (95% CI: 1.12, 1.33); ESKD: HR 1.34 (95% CI: 1.24, 1.57)), as did a post-hoc sensitivity analysis by proteinuria status (no proteinuria: HR 1.28 (95% CI: 1.03, 1.59); proteinuria: 1.27 (95% CI: 1.08, 1.49)). Conclusion We found evidence that dual therapy use was associated with increased risk of renal outcomes in both those with and without CKD at baseline. Applying a reference trial emulation approach and successfully benchmarking findings against ONTARGET, where confounding was not present due to randomisation, provides confidence in the validity of these results.
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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 | 0.077 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".