Coagulation abnormalities as predictors of renal dysfunction in heart failure with reduced ejection fraction
Bibliographic record
Abstract
Introduction Heart failure (HF) is a prothrombotic state that is also associated with the progression of renal dysfunction. However, it is unknown whether coagulation abnormalities are associated with progressive cardiorenal syndrome. Aim of the research To evaluate activators and inhibitors of coagulation and fibrinolysis and their relationship with renal failure in HF patients. Material and methods Coagulation biomarkers such as thrombin-antithrombin III, human tissue-type plasminogen activator, human plasminogen activator inhibitor, von Willebrand factor (vWF), soluble thrombomodulin (sTM), human prothrombin fragments (F1+F2), and protein C were evaluated in 36 consecutive HF patients without anticoagulation and in 19 controls matched in age and gender. Results HF patients, compared to controls, had lower levels of C protein (p = 0.04) and F1 + F2 (p < 0.001) but higher levels of vWF (p < 0.001) and borderline sTM (p = 0.07). Similarly, haemoglobin (p < 0.001) and glomerular filtration rate (GFR) (p = 0.004) were lower in HF, while INR (p < 0.001), NT-proBNP (p < 0.001), and asymmetric dimethylarginine (ADMA) (p < 0.001) were higher. Most of the echocardiographic parameters differed between the 2 groups. From coagulation biomarkers, sTM (r = –0.66; p < 0.001) and vWF (r = –0.41; p = 0.002) were associated with eGFR. Most of the echocardiographic and laboratory parameters were also related to eGFR. After classifying all variables into 5 categories; laboratory tests, echocardiographic parameters, vascular reactivity, haemodynamics, and coagulation parameters, multivariable linear regression showed that coagulation parameters were the most strongly associated with eGFR (r2 = 0.48, p < 0.001). Conclusions In the study population, coagulation disorders were most strongly associated with impaired renal function, independently of other parameters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".