Biomarkers of Inflammation, Fibrosis, and Acute Kidney Injury in Patients with Heart Failure with and without Left Ventricular Assist Device Implantation
Bibliographic record
Abstract
BACKGROUND/AIMS: Renal dysfunction or renal failure is a common complication in left ventricular assist device (LVAD) recipients and is associated with reduced survival. To date, serum creatinine and glomerular filtration rate (GFR) are used for the evaluation of kidney function. However, serum creatinine and GFR have limitations. The objective of our study is to assess the levels of kidney biomarkers in LVAD recipients compared to heart failure patients and healthy controls and to examine their association with conventional clinical biomarkers. METHODS: The biomarkers neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), plasminogen activator inhibitor-1 (PAI-1), and adiponectin were assessed in 51 participants: 19 heart failure patients, 16 LVAD recipients, and 16 healthy controls. Linear regressions were performed to assess whether demographic and clinical variables predict the levels of biomarkers that are associated with acute kidney injury and the risk of chronic kidney disease. RESULTS: The levels of NGAL and adiponectin were higher in LVAD recipients and patients with heart failure as compared with healthy controls. The levels of PAI-1 and KIM-1 were not elevated in LVAD recipients. The results of linear regression analysis indicated that when controlling for the effect of CRP and BNP, 40.1% of the variance in NGAL levels can be explained by GFR (R2 = 0.401, F = 5.56, p = 0.005), while CRP can explain 35.3% of the variance in adiponectin levels (R2 = 0.353, F = 4.55, p = 0.01), when controlling for the effect of BNP and GFR. CONCLUSIONS: The levels of NGAL and adiponectin were augmented in LVAD recipients, suggesting that renal functions were not restored with circulatory support. Larger studies should assess the predictability of these biomarkers of renal dysfunction in LVAD recipients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".