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Record W2998812319 · doi:10.1093/ehjci/jez319.290

560 The inclusion of pulmonary arterial pressure misclassifies diastolic function using the current EACVI guidelines in pre-capillary pulmonary hypertension

2020· article· en· W2998812319 on OpenAlexaff
A Telmisani, J Deschamps, Abeer Alturki, Ben Fox, Igal A. Sebag, Robert Schlesinger, Jonathan Afilalo, Mark J. Eisenberg, Ali Abualsaud, David Langleben, Lawrence Rudski

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2020
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicineCardiologyPreloadInternal medicineDiastoleEjection fractionPulmonary hypertensionCardiac catheterizationPulmonary wedge pressureHemodynamicsBlood pressureHeart failure

Abstract

fetched live from OpenAlex

Abstract Background Pulmonary hypertension (PH) can be pre-capillary or post-capillary (PVH) etiology based on left-sided filling pressures and pulmonary vascular resistance. The 2016 EACVI/ASE Recommendations for the Evaluation of Left Ventricular Diastolic Function (LVDF) provides flow-diagrams to categorize patients. Parameters used include left atrial volume, Doppler-derived transmitral and mitral annular velocities, and systolic PA pressure (sPAP). There are no dedicated criteria to assess the diastolic function in pulmonary arterial hypertension (PAH). Additionally, diseases such as scleroderma can result in both PAH and PVH, thus including sPAP may alter LVDF diagnostic reliability in this population. Purpose Because elevated PAP is fundamental to PAH, we hypothesized that the EACVI/ASE diastolic function algorithm has a lower predictive value in correctly classifying diastolic function in scleroderma. Methodology We performed a single-center retrospective analysis of scleroderma patients who underwent complete echocardiography and comprehensive right and left heart catheterization for PH evaluation. PH categorization was defined using the 6th World Symposium hemodynamic definitions (PAH as mPAP ≥20 mmHg, PCWP ≤15 mmHg, PVR ≥ 3 WU). Diastolic function categorization used 2016 EACVI/ASE recommendations. Index catheterization and echocardiogram closest to cardiac catheterization were analyzed. Results 260 patients underwent evaluation and 63 were diagnosed with PH. PAH was diagnosed in 35 (age 64 ±10, mPAP 55± 18 mmHg, LVEF 60 ± 6%) and PVH in 28 (age 65 ± 10, mPAP 34 ± 14 mmHg, LVEF 63 ± 6%). Of the PAH patients, 20 had normal LVEDP (≤ 12 mmHg) and 15 increased LVEDP. In the PAH normal LVEDP patients, the EACVI algorithm classified diastolic function as normal in 25%, grade 2 in 5%, Grade 3 in 5%, and "indeterminate" in 65%. In the PAH group with increased LVEDP (> 12 mmHg), 27% were incorrectly identified as normal, 7% as grade 2 dysfunction, and 66% as indeterminate. The diastolic function algorithm has a sensitivity of 27% and specificity of 75% to diagnose a LVEDP ≤ 12 mmHg, with an AUC of 0.508 (p = 0.91). With exclusion of sPAP from the algorithm, indeterminate cases in both PAH groups were reclassified as normal, resulting in improved sensitivity (93%) but poorer specificity (10%), and a similar AUC (0.517, p = 0.72). In PVH patients, the algorithm performed better with a sensitivity of 63% and specificity of 83% to predict LVEDP > 12 mmHg with AUC 0.773, p = 0.017. Conclusion In scleroderma patients with PAH, the EACVI diastolic algorithm performs poorly and is confounded by including PAP as a parameter. The sensitivity of the algorithm is improved by the exclusion of sPAP although with reduced specificity. It remains inadequate to reliably diagnose normal LVEDP. While useful in other populations, algorithm modifications including exclusion of PAP, must be employed in suspected scleroderma PAH.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.088
GPT teacher head0.316
Teacher spread0.228 · 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 designOther design
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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