Mortality Following Defibrillator Implantation in Patients with Renal Insufficiency
Bibliographic record
Abstract
INTRODUCTION: Patients with renal insufficiency have an increased risk of atherosclerotic coronary artery disease, cardiovascular events, and sudden cardiac death. Due to under-representation of patients with renal disease in large clinical trials, outcomes of implantable cardioverter defibrillator (ICD) implantation in this group remain unclear. METHODS AND RESULTS: Inpatient and ambulatory records were reviewed for 741 consecutive patients undergoing 947 defibrillator implants or replacements at Department of Defense Medical Facilities. Demographics, medical history, and mortality were reviewed. The mean age of the cohort was 64 +/- 14 years and 599 (80.8%) were male. There were 173 patients (23.3%) with chronic renal insufficiency, 22 (3.0%) undergoing hemodialysis, and 546 (73.7%) without reported renal disease. The mean number of annual hospital admissions for heart failure among patients with and without renal failure was 3.8 +/- 4.0 versus 1.2 +/- 1.9 (P < 0.0001), respectively. The 1-year survival for those without renal insufficiency was 96.6%, compared to 87.8% for those with chronic renal insufficiency, and 88.7% for those undergoing hemodialysis. Multivariate analysis demonstrated a significant association between mortality among ICD patients and renal insufficiency, independent of coexisting congestive heart failure, ischemic cardiomyopathy, and diabetes mellitus (P < 0.0001). CONCLUSIONS: Among ICD recipients, those with renal insufficiency have a significantly higher mortality rate than those without renal insufficiency. Among a cohort of patients with ICDs, those with known renal insufficiency have higher rates of health care resource utilization and more heart failure admissions. Development of a national registry for ICDs should include data with regard to renal function.
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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 | 0.000 | 0.000 |
| 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".