Risk factors for intradialytic decline in cerebral perfusion and impaired cerebral autoregulation in adults on hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Hemodialysis (HD) patients have significant burden of cerebral ischemic pathology noted on brain imaging. These ischemic type lesions maybe due to cerebral hypoperfusion that may be occurring during blood pressure (BP) fluctuations commonly noted during HD sessions. We evaluated changes in cerebral perfusion and measured an index of cerebral autoregulation (CA index) during HD to identify potential risk factors for intradialytic decline in cerebral perfusion and impaired cerebral autoregulation. METHODS: In this cross-sectional study, we included HD patients age 50 years or older receiving conventional in-center HD. We measured cerebral perfusion during HD, using cerebral oximetry, and calculated the correlation between cerebral perfusion and BP during HD as an index of CA. We measured the association between potential risk factors for intradialytic decline in cerebral perfusion and CA index. FINDINGS: We included 32 participants and 118 HD sessions in our analysis. The mean ± SD decline in cerebral oxygen saturation during HD was 6.5% ± 2.9% with a relative decline from baseline values of 9.2% ± 4.4%. Greater drop in systolic BP (SBP) during HD was associated with decline in cerebral oxygen saturation, p = 0.02. Impaired CA index was noted in 37.3% of HD sessions. Having diabetes and >20 mmHg drop in SBP during HD were associated with increased (worse) CA index with an increase of 0.24 95%CI [0.06, 0.41] for diabetes and increase of 0.43 95%CI [0.27, 0.56] for a >20 mmHg drop in SBP during HD. DISCUSSION: Cerebral perfusion can decline during HD and is associated with changes in systemic BP. This may be due to impaired cerebral autoregulation in HD patients. Risk factors for worse CA index include diabetes and >20 mmHg drop in SBP during HD. This study highlights the risk of intradialytic decline in cerebral perfusion and impaired cerebral autoregulation in HD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".