Hemodynamic Monitoring During Hemodialysis Using Bioimpedance: A Comparison of Changes in Resistance Between Different Body Segments
Bibliographic record
Abstract
INTRODUCTION: Prevention of hemodynamic complications during hemodialysis remains challenging. Although whole body bioimpedance is well established in fluid status assessment, its use for dynamic or continuous recordings is limited. A segmental approach may serve this purpose better. This study investigates which body segment is best targeted to measure bioimpedance for hemodynamic monitoring. METHODS: In this observational study, serial bioimpedance measurements were conducted on the whole body, lower leg, upper arm, and thorax of 15 patients during two hemodialysis sessions. The resistance component of bioimpedance was used to investigate the relationship with changes in volume and systolic blood pressure (SBP). FINDINGS: Predialysis to postdialysis changes in relative resistance between the two sessions revealed the lowest intraclass correlation coefficient for upper arm (0.023) and the highest for thoracic resistance (0.728). Correlation between ultrafiltration volume and relative resistance was comparable between upper arm and thoracic segment (0.538 [0.447-0.618] and 0.537 [0.446-0.617], both p < 0.001, respectively) and the highest for whole-body and lower leg (0.697 [0.63-0.754] and 0.670 [0.598-0.731], both p < 0.001, respectively). In contrast, the correlation between changes in SBP and relative resistance was the highest in the thoracic segment (-0.33 [-0.432 to -0.219], p < 0.001) and the lowest for whole body measurements (-0.154 [-0.269 to -0.036], p = 0.01). In addition, multiple regression analysis indicated thoracic resistance as the best predictor for changes in SBP (β = -0.261 [-0.353 to -0.126], p < 0.001). DISCUSSION: These findings suggest that the thorax is the most suitable region for segmental bioimpedance measurements to assess hemodynamic parameters. Thoracic bioimpedance may innovate the hemodynamic monitoring of hemodialysis 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".