Relationship between plasma volume and essential blood constituents in patients with heart failure and preserved ejection fraction
Bibliographic record
Abstract
Summary Introduction Notwithstanding recent progress on molecular mechanisms underlying heart failure with preserved ejection fraction (HFpEF), multiple pathophysiological aspects of this condition including the basis of anaemia and other haematological disorders remain unresolved. In this study, we sought to determine the relationship of plasma volume (PV), a plausible confounding factor for the concentration of solutes in blood, with key haematological markers in HFpEF patients. Methods Total circulating PV was determined with high precision, automated carbon monoxide rebreathing in 24 stable HFpEF patients (70 ± 8 years, left ventricular ejection fraction = 55±5%) and 18 healthy age‐ and sex‐matched control (HC) individuals. Linear regression analyses were performed to determine the association of PV with a comprehensive set of haematological variables. Results Haematocrit (40·1 ± 4·9 versus 43·6 ± 2·7%, P = 0·004) and haemoglobin concentration (131 ± 16 versus 142 ± 7 g l−1, P = 0·003) were reduced in HFpEF patients compared with HC individuals. In regression analyses, PV was negatively associated with haematocrit (r = −0·45, P = 0·029) and haemoglobin concentration (r = −0·44, P = 0·030) in HFpEF patients, whereas these variables were not associated with PV in HC individuals (P≥0·198). Regarding blood electrolytes, PV was negatively associated with K+ (r = −0·43, P = 0·036) and Ca2+ (r = −0·44, P = 0·032) in HFpEF patients but not in HC individuals (P≥0·734). None of the above associations were detected in HFpEF patients when using ideal instead of measured PV. Conclusion The blood concentration of routine markers of anaemia and electrolyte balance is specifically and linearly associated with PV in HFpEF patients. Excess or deficit of circulating PV may confound clinical diagnosis in this population.
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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 | 0.000 | 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".