Assessing Correlation Between Thoracic Impedance and Remotely Monitored Pulmonary Artery Pressure in Chronic Systolic Heart Failure
Bibliographic record
Abstract
Background: Heart failure (HF) readmission continues to be a major health problem. Monitoring pulmonary artery pressure (PAP) and thoracic impedance (TI) are the two modalities utilized for early identification of decompensation in HF patients. We aimed to assess the correlation between these two modalities in patients who simultaneously had both the devices. Methods: Patients with history of New York Heart Association class III systolic HF with a pre-implanted intracardiac defibrillator (ICD) capable of monitoring TI and pre-implanted CardioMEMs™ remote HF monitoring device were included. Hemodynamic data including TI and PAPs were measured at baseline and then weekly. Weekly percentage change was then calculated as: Weekly percentage change = (week 2 - week1)/week 1 × 100. Variability between the methods was expressed by Bland-Altman analysis. Significance was determined as a P-value < 0.05. Results: Nine patients met the inclusion criteria. There was no significant correlation between the assessed weekly percentage changes in pulmonary artery diastolic pressure (PAdP) and TI measurements (r = -0.180, P = 0.065). Using Bland-Altman analytic methods, both methods had no significant difference in agreement (0.011±0.094%, P = 0.215). With the linear regression model applied for Bland-Altman analysis, the two methods appeared to have proportional bias without agreement (unstandardized beta-coefficient of 1.91, t 22.9, P ≤ 0.001). Conclusion: Our study demonstrated that variations exist between measurement of PAdP and TI; however, there is no significant correlation between weekly variations between them.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".