Temporal Trends in Utilization of Cardiac Therapies and Outcomes for Myocardial Infarction by Degree of Chronic Kidney Disease: A Report From the NCDR Chest Pain–MI Registry
Bibliographic record
Abstract
Background We sought to determine temporal trends in use of evidence‐based therapies and clinical outcomes among myocardial infarction ( MI) patients with chronic kidney disease ( CKD ). Methods and Results MI patients from the NCDR (National Cardiovascular Data Registry) Chest Pain– MI Registry between January 2007 and December 2015 were categorized into 3 groups by degree of CKD (end‐stage renal disease on dialysis, CKD [glomerular filtration rate <60 mL/min per 1.73 m 2 ] not requiring dialysis, and no CKD [glomerular filtration rate ≥60 mL/min per 1.73 m 2 ]). Logistic regression modeling was used to determine the association between calendar years (2014–2015 versus 2007–2008) and each outcome by degree of CKD . Among 325 396 patients with ST‐segment–elevation MI, 1.0% had end‐stage renal disease requiring dialysis, and 26.1% had CKD not requiring dialysis. Use of primary percutaneous coronary intervention increased over time regardless of the presence or degree of CKD ( P= 0.40 for interaction). In‐hospital mortality was temporally higher among patients with preserved renal function (odds ratio: 1.25; 95% confidence interval, 1.13–1.39; P <0.001) but not among patients with CKD ( P =0.035 for interaction). Among 506 876 non–ST‐segment–elevation MI patients, 3.4% had end‐stage renal disease requiring dialysis, and 34.4% had CKD not requiring dialysis. P2Y 12 inhibitor use within 24 hours increased over time only among dialysis patients ( P for interaction <0.001). Use of coronary angiography and percutaneous coronary intervention also increased, with the greatest increase among dialysis patients ( P for interaction <0.001 and <0.001, respectively). In‐hospital mortality was lower, regardless of the presence or degree of CKD ( P =0.64 for interaction). Conclusions Uptake of evidence‐based medical and invasive therapies has increased over the past decade among MI patients with CKD , particularly dialysis patients, with improvement of in‐hospital mortality observed among patients with non–ST‐segment–elevation MI, but not ST‐segment–elevation MI, and CKD .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".