Heart-Kidney Biomarkers in Patients Undergoing Cardiac Stress Testing
Bibliographic record
Abstract
We examined association of inducible myocardial perfusion defects with cardiorenal biomarkers, and of diminished left ventricular ejection fraction (LVEF) with kidney injury marker plasma neutrophil gelatinase-associated lipocalin (NGAL). Patients undergoing nuclear myocardial perfusion stress imaging were divided into 2 groups. Biomarkers were analyzed pre- and poststress testing. Compared to the patients in the low ischemia group (n = 16), the patients in the high ischemia group (n = 18) demonstrated a significantly greater rise in cardiac biomarkers plasma BNP, NT-proBNP and cTnI. Subjects were also categorized based on pre- or poststress test detectable plasma NGAL. With stress, the group with no detectable NGAL had a segmental defect score 4.2 compared to 8.2 (P = .06) in the detectable NGAL group, and 0.9 vs. 3.8 (P = .03) at rest. BNP rose with stress to a greater degree in patients with detectable NGAL (10.2 vs. 3.5 pg/mL, P = .03). LVEF at rest and with stress was significantly lower in the detectable NGAL group; 55.8 versus 65.0 (P = .03) and 55.1 vs. 63.8 (P = .04), respectively. Myocardial perfusion defects associate with biomarkers of cardiac stress, and detectable plasma NGAL with significantly lower LVEF, suggesting a specific heart-kidney link.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".