3123The impact of renal disease on target vessel revascularisation following percutaneous coronary intervention: a contemporary analysis of 45,287 patients from the British Columbia Cardiac Registry
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is an established risk factor for the development and progression of coronary artery disease. It is present in approximately 40% of patients undergoing percutaneous coronary intervention (PCI) and confers a strong independent risk for morbidity and mortality after PCI. CKD is also often perceived as a risk factor for repeat revascularization, based on small limited studies. Whether or not CKD predicts restenosis and/or repeat revascularization in the contemporary era is unknown. Purpose: We evaluated the relationship between baseline renal function and target vessel revascularization (TVR) in unselected patients undergoing PCI. Methods: We analysed 45,287 patients undergoing PCI between 2008–2014 enrolled in the British Columbia Cardiac registry. We evaluated TVR up to 2 years. Renal disease was categorized by glomerular filtration rate (GFR, mL/min/1.73m2): ≥90 (n=10219), 90>GFR≥60 (n=17019), 60>GFR≥30 (14876), 30>GFR≥0 (n=2594) and dialysis-dependence (n=579). We used Cox proportional hazard regression models and Kaplan-Meier analyses. Results: The 2-year TVR rates were 10.7% (GFR>90); 10.4% (90>GFR≥60); 10.4% (60>GFR≥30); 9.1% (30>GFR≥0); and 19.2% (dialysis). The TVR rates were significantly higher in dialysis patients versus non-dialysis patients (19.2% vs. 10.4%, p<0.001). Multivariable analyses indicated that declining GFR was not associated with TVR in non-dialysis patients, but was a strong independent predictor for 2-year TVR in those dialysis-dependent (HR=1.69, 95% CI: 1.37–2.08, p<0.001) (Figure 1A and 1B). This association was consistently observed for all clinical indications, and in stratified analyses for patient groups considered to have increased risk for TVR, including diabetic (HR=1.69, 95% CI: 1.32-.18, p<0.0001) versus non-diabetic patients (HR=1.73, 95% CI: 1.24–2.42, p=0.001); stent length ≥30mm (HR=1.42, 95% CI: 1.06–1.91, p=0.021) versus <30mm (HR=2.04, 95% CI: 1.51–2.74, p<0.001); stent diameter ≥3mm (HR=1.99, 95% CI: 1.5–2.55, p<0.001) versus <3mm (HR=2.43, 95% CI: 1.66–3.56, p<0.001); and bare metal stents use (HR=1.52, 95% CI: 0.99–2.36, p=0.056) versus drug-eluting stents (HR=1.77, 95% CI: 1.39–2.24, p<0.001).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".