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Record W4381125569 · doi:10.1093/ehjci/jead119.245

Do postoperative hemodynamic parameters add prognostic value for the prediction of mortality after surgical aortic valve replacement?

2023· article· en· W4381125569 on OpenAlexaff
Bart J.J. Velders, Michiel D. Vriesendorp, Federico M. Asch, François Dagenais, Rüdiger Lange, Michael J. Reardon, Vivek Rao, Joseph F. Sabik, Rolf H. H. Groenwold, Robert J.M. Klautz

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General HospitalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineAortic valve replacementPromCardiologyHemodynamicsInternal medicineEuroSCOREProsthesisAortic valveSurgeryCardiac surgeryStenosis

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): The PERIGON Pivotal Trial was funded by Medtronic. Background While different parameters to assess prosthetic valve performance are available, prosthesis-patient mismatch (PPM) is exclusively based on indexed effective orifice area (EOAi). Purpose We investigated the prognostic value of multiple postoperative hemodynamic parameters, independent of the STS predicted risk of mortality (STS PROM), for prediction of 5-year mortality after surgical aortic valve replacement (SAVR). Methods The study population consisted of patients enrolled in a prospective multicenter study. All patients underwent SAVR with the same stented bioprosthesis. Patients who underwent previous open-heart surgery, or died or withdrew consent before echocardiographic assessment at their first follow-up visit (3–6 months), were excluded. Cox regression models were fitted to estimate the relation between STS PROM, postoperative hemodynamic parameters (as continuous variables except for PPM), and all-cause mortality. Follow-up started at the first postoperative outpatient clinic visit and continued until death or withdrawal from the study, whichever came first. In addition to the STS PROM, candidate predictors included peak aortic jet velocity (Vmax), mean pressure gradient (MPG), EOA, predicted and measured EOAi, Doppler velocity index (DVI), internal prosthesis orifice area indexed (POAi) by stroke volume, and Valve Academic Research Consortium (VARC) 3 categories for PPM (1). All echocardiograms were evaluated by a core laboratory. Although the STS PROM was initially developed to predict 30-day mortality (2), it has also been proven to predict late mortality (3). Model performance was investigated using the c-statistic, likelihood ratio test, and net reclassification improvement (NRI), among others. Results Among 1118 patients enrolled, 1022 patients were included in the study. Patients were on average 70 years, 75% was male, and 88% had a left ventricle ejection fraction of ≥50%. At 5-year follow-up, 89 patients had died, and the median follow-up time was 1697 days. In univariate analysis, STS PROM was the only significant predictor of 5-year mortality (hazard ratio 1.40, 95%-confidence interval [CI] 1.26, 1.55). Moreover, STS PROM performed best in terms of the c-statistic (0.66, 95%-CI 0.60, 0.72). When extending the STS PROM with single hemodynamic parameters (Table 1), neither the c-statistics nor the NRI demonstrated added prognostic value compared to a model with STS PROM alone. Similar findings were observed when multiple hemodynamic parameters were added to STS PROM (c-statistic 0.68, 95%-CI 0.62, 0.74, and NRI 0.04, 95%-CI −0.08, 0.16). Conclusions The STS PROM was found to be the main predictor of patients’ prognosis. In this analysis, the added prognostic value of postoperative hemodynamic parameters for 5-year mortality after SAVR was limited (Figure 1). These results warrant further research on the value of PPM, residual postoperative hemodynamic obstruction and their relation with adverse outcomes.

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 score0.013
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.006
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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