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Predictive Accuracy of Traditional Methods to Assess Left Ventricular Stiffness in a Porcine Model of Heart Failure with Preserved Ejection Fraction

2023· article· en· W4378675304 on OpenAlexaff
Alexandra K. Gilligan, Emily Hudson, Filip Konečný, John M. Canty, Brian R. Weil

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPreloadCardiologyInternal medicineMedicineHeart failureEjection fractionHeart failure with preserved ejection fractionDiastoleStroke volumeBlood pressureHemodynamics

Abstract

fetched live from OpenAlex

Objective: Left ventricular (LV) stiffening is a key pathophysiological component of heart failure with preserved ejection fraction (HFpEF) and contributes to pulmonary congestion and exertional dyspnea. Accurate assessment of LV stiffness is commonly accomplished via construction of end-diastolic pressure-volume relationships (EDPVRs) during transient reductions in venous return (i.e., preload). However, the extent to which this method accurately predicts the rise in LV pressure in response to an increase in preload is unknown. Accordingly, we sought to assess the predictive accuracy of preload reduction-derived EDPVRs in a porcine model of repetitive pressure overload (RPO)-induced HFpEF. Methods: Swine (n=8) were subjected to 2-weeks of RPO via daily administration of phenylephrine (PE; 400 ug/min; 1 hour/day) through an indwelling jugular vein catheter. This model has previously been shown to produce a HFpEF-like cardiac phenotype characterized by a persistent hypertrophy-independent reduction in LV diastolic compliance. Admittance catheter-based PV analysis (Transonic, ADV500) was subsequently performed at rest and during transient preload reduction to construct the LV EDPVR. The predicted change in LV end-diastolic pressure (EDP) for a given change in LV end-diastolic volume (EDV) was compared to the actual change in LVEDP measured in response to an acute PE-mediated increase in preload. Results were compared to those from a series of size-matched control animals (n=8) that underwent the same physiological study protocol. Results: Construction of the EDPVR via transient preload reduction revealed an increase in the LV diastolic stiffness coefficient (ß) from 0.016±.003 in normal control animals to 0.047±.008 after RPO (p<0.01). As a result, the predicted increase in LVEDP for a 20% increase in LVEDV was significantly greater in RPO animals (22.0±4.3 mmHg) than normal controls (8.5±1.3 mmHg; p<0.01). However, administration of PE demonstrated that this was a significant underestimation, as the actual change in LVEDP in response to an increase in preload was nearly 2-fold higher in each group (RPO: 38.1±7.3 mmHg; Control: 15.3±2.2 mmHg; both p<0.05 vs. predicted change in LVEDP). As a result, LV diastolic stiffness (ΔLVEDP/ΔLVEDV) was significantly higher when measured in response to an increase in preload (RPO: 1.78±0.31 mmHg/mL; Control: 0.59±0.09 mmHg/mL) than when extrapolated from EDPVRs derived from the hemodynamic response to preload reduction (RPO: 1.08±0.23 mmHg/mL; Control: 0.33±0.05 mmHg/mL; both p<0.05). Conclusions: Traditional approaches to determine the LV EDPVR underestimate the actual rise in LVEDP for a given rise in LVEDV. These findings highlight challenges in quantifying myocardial stiffness and reinforce the importance of assessing the hemodynamic response to increased preload directly, rather than extrapolating from EDPVRs created via transient preload reduction. The National Heart Lung and Blood Institute, the National Center for Advancing Translational Sciences, and the Department of Veterans Affairs. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.338
Teacher spread0.271 · 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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