Non-invasive intradialytic percutaneous perfusion monitoring: a view to the heart through the skin
Bibliographic record
Abstract
Introduction: The life-sustaining treatment of hemodialysis (HD) induces recurrent and cumulative systemic circulatory stress resulting in cardiovascular injury. These recurrent insults compound preexisting cardiovascular sequalae leading to the development of myocardial injury and resulting in extremely high morbidity/mortality. This is largely a consequence of challenged microcirculatory flow within the myocardium (evidenced by detailed imaging-based studies). Currently, monitoring during HD is performed at the macrovascular level. Non-invasive monitoring of organ perfusion would allow the detection and therapeutic amelioration of this pathophysiological response to HD. Non-invasive percutaneous perfusion monitoring of the skin (using photoplethysmography-PPG) has been shown to be predictive of HD-induced myocardial stunning (a consequence of segmental ischemia). In this study, we extended these observations to include a dynamic assessment of skin perfusion during HD compared with directly measured myocardial perfusion during dialysis and cardiac contractile function. Methods: We evaluated the intradialytic microcirculatory response in 12 patients receiving conventional HD treatments using continuous percutaneous perfusion monitoring throughout HD. Cardiac echocardiography was performed prior to the initiation of HD, and again at peak-HD stress, to assess the development of regional wall motion abnormalities (RWMAs). Myocardial perfusion imaging was obtained at the same timepoints (pre-HD and peak-HD stress), utilizing intravenous administered contrast and a computerized tomography (CT)-based method. Intradialytic changes in pulse strength (derived from PPG) were compared with the development of HD-induced RWMAs (indicative of myocardial stunning) and changes in myocardial perfusion. Results: = 0.03)]. Discussion: Percutaneous perfusion monitoring using PPG is a useful method of assessing intradialytic hemodynamic stability and HD-induced circulatory stress. The information generated at the microcirculatory level of the skin is reflective of direct measures of myocardial perfusion and the development of HD-induced myocardial stunning. This approach for the detection and management of HD-induced cardiac injury warrants additional evaluation.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".