Urinary Markers in Heart Failure – Types, Timing and Thresholds. <i>European Journal of Heart Failure</i> Expert Consensus Document
Bibliographic record
Abstract
Several urinary markers reflect disease severity and have the potential to support the management of heart failure (HF). Collecting urine samples is easy and inexpensive, and urine sample composition can be altered not only by underlying kidney impairments (i.e. filtration barrier damage and tubular injury) but also via neurohormonal and inflammatory activation, ageing, comorbidities, other medical conditions and pharmacological interventions. For instance, urinary sodium may help to predict the response to loop diuretic therapy in acute HF, while albuminuria is used as a risk marker and therapeutic target for the progression of cardiovascular and kidney diseases in chronic HF, especially when accompanied by kidney disease. However, these markers remain underutilized in clinical practice. This review paper underscores the role of urinary markers in HF, with a specific focus on: (i) the pathophysiologic mechanisms underlying urinary marker excretion, (ii) the prognostic values of urinary markers across diverse HF phenotypes and non-cardiovascular comorbidities (i.e. chronic kidney disease and diabetes), (iii) the impact of medical therapies on urinary markers, and (iv) existing knowledge gaps that challenge their implementation in clinical practice. The recommendations are aligned with current guidelines, evidence, and expert consensus.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".