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Record W4322012343 · doi:10.14740/cr1447

Assessing Correlation Between Thoracic Impedance and Remotely Monitored Pulmonary Artery Pressure in Chronic Systolic Heart Failure

2023· article· en· W4322012343 on OpenAlexvenueno aff
Ankita Aggarwal, Z Khan, Christian Machado, Marcel Zughaib

Bibliographic record

VenueCardiology Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyPulmonary arteryInternal medicineHeart failureDecompensationBland–Altman plotDiastoleIntracardiac injectionBlood pressureLimits of agreementNuclear medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.450
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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