The Association of Glomerular Filtration Rate With Echocardiographic Parameters in Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the primary cause of mortality and morbidity in chronic kidney disease (CKD) patients. Aortic propagation velocity (APV), epicardial fat thickness (EFT) and carotid intima-media thickness (CIMT) measurements could provide additional information on assessing renal decline in CKD patients. The study aimed to evaluate EFT, AVP and CIMT in CKD patients and then investigate the association among those parameters. METHODS: A total of 170 CKD consecutive subjects were enrolled in the study. Patients were divided into five groups according to their estimated glomerular filtration rate (eGFR) values. Each patient underwent complete transthoracic echocardiography examination. APV, EFT and CIMT were measured for analyses. A multivariate linear regression model was used for analysis to determine the independent predictors of eGFR. RESULTS: The lowest APV was observed in stage IV-V, and the highest APV was observed in stage I-II (P < 0.001). Stage IV-V patients had the highest EFT and stage I-II patients had the lowest EFT (P < 0.001). Moreover, the lowest CIMT was observed in stage III, and the highest CIMT was observed in stage V (P < 0.001). eGFR was significantly and positively correlated with APV and negatively correlated with EFT and CIMT. In multivariate analyses, APV (odds ratio (OR): 0.289, P < 0.001), EFT (OR: -0.135, P < 0.001) and CIMT (OR: -0.388, P < 0.001) were independent predictors of eGFR. CONCLUSION: We found that APV decreased, and EFT and CIMT increased as CKD progress. The present study suggests that APV, EFT and CIMT might be incorporated with the examination of CKD patients in daily practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".