Cardiovascular Assessment of Diabetic End-Stage Renal Disease Patients Before Renal Transplantation
Bibliographic record
Abstract
BACKGROUND: Although consensus guidelines for preoperative cardiovascular (CV) assessment exist, diabetic patients with renal insufficiency (DM/RI) undergoing assessment for renal transplantation are a unique high-risk group that remains poorly investigated. METHODS: A consecutive cohort of DM/RI patients being assessed for renal transplantation was studied. We analyzed the ability of clinical characteristics and noninvasive investigation to predict significant coronary artery disease (CAD) and incidence of major adverse CV events. RESULTS: Baseline characteristics (n = 280) are as follows: mean age 48.6 years (± 11.5 standard deviation), 66% men, diabetes duration 22.6 years (mean ± 8.9 standard deviation), 92% hypertension, 46% hypercholesterolemia, 24% family history CAD, and 21% known CAD. Abnormal myocardial perfusion imaging was found in 27.8%, and 56.5% had CAD more than or equal to 50%. Although positive myocardial perfusion imaging was the only independent predictor of CAD (odds ratio 7.18, 95% confidence interval 2.98-17.3), a poor negative predicted value was observed with normal imaging in 50.3% of patients having CAD more than or equal to 50%, 35.4% CAD more than 70%, and 41.8% Duke angiographic score more than or equal to 4. At mean follow up of 4 years (median 3.9), 76 of 280 patients suffered major adverse cardiovascular events including 17% mortality. Angiographic evidence of CAD (≥ 70% odds ratio 1.81, 95% confidence interval 1.02-3.23) was the only independent predictor of major adverse cardiac events. CONCLUSION: DM/RI patients being assessed for renal transplantation have frequent CV risk factors, high likelihood of CAD, and a 28% incidence of major adverse cardiac events after 4 years. Myocardial perfusion imagining is of little clinical utility as a screening tool for CAD in this population. Only angiographic CAD was predictive of subsequent major adverse cardiac events. Further studies of risk stratification and revascularization in this high-risk population are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".