Carotid pulsatile energy fraction surpasses traditional hemodynamic markers in explaining cognitive impairment among hemodialysis patients
Bibliographic record
Abstract
Cognitive impairment is a frequent yet poorly understood complication of maintenance hemodialysis. We studied 162 thrice-weekly hemodialysis patients and 1858 community adults characterized previously with identical pressure-flow protocols. Group comparisons were conducted following propensity score matching and utilizing ANCOVA, adjusted for age, sex, educational attainment, body mass index, diabetes, and hypertension. On a mid-week non-dialysis day, synchronous applanation tonometry and Doppler ultrasound recorded ascending-aortic and common-carotid waveforms; mean hydraulic energy, pulsatile energy and the pulsatile energy fraction (PEF) were computed for each heartbeat. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA), with scores < 26 indicating impairment. Cognitive dysfunction was present in 39% of 152 hemodialysis patients versus 33% of community adults (Standardized Mean Difference = 0.134). Compared with the Community group, hemodialysis group exhibited a 45% increase in aortic PEF (0.16 ± 0.07 vs 0.11 ± 0.03), and a 74% increase in carotid PEF (0.080 ± 0.04 vs 0.046 ± 0.02). In multivariable models adjusted for age, sex, education level, and body-mass index, carotid PEF displayed the strongest inverse association with MoCA (standardized β = -0.287, p < 0.001). Introducing carotid PEF increased the model's explained variance and rendered pulse pressure and carotid-femoral pulse-wave velocity non-significant. Conversely, higher steady mean carotid energy correlated positively with MoCA (β = +0.158, p = 0.030). These findings indicate that cognitive performance in hemodialysis patients is governed less by conventional pressure or stiffness metrics than by the pulsatile energy transmitted to the carotids, positioning carotid PEF as a mechanistic marker and promising therapeutic target for preserving cognition in this vulnerable 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".