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Record W2981478676 · doi:10.1093/eurheartj/ehz747.0020

91Transcatheter valve-in-valve versus redo surgical aortic valve replacement for the management of failed biological prosthesis: early and late outcomes

2019· article· en· W2981478676 on OpenAlexaffabout
Derrick Y. Tam, Christoffer Dharma, Harindra C. Wijeysundera, Peter C. Austin, Maral Ouzounian, Rodolfo V. Rocha, Stephen E. Fremes

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General HospitalHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAortic valve replacementAtrial fibrillationProsthesisMcNemar's testHazard ratioInternal medicinePropensity score matchingCardiologyHeart failureValve replacementAortic valveSurgeryStenosisConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background While the gold standard for the management of failed previous biological prosthesis was redo surgical aortic valve replacement (SAVR), valve-in-valve (ViV) transcatheter AVR (TAVR) has emerged as a less invasive option. Published studies comparing the two techniques have been small and limited to early outcomes. Herein, we compare early mortality, morbidity and late mortality between ViV TAVR and redo SAVR. Methods Clinical and administrative databases for Canada's most populous province, Ontario (>13 million patients), were linked to identify patients undergoing ViV and redo SAVR for a failed biological prosthesis. Baseline characteristics were compared and 1:1 propensity score matching (PSM) was performed to account for baseline differences. Standardized mean differences (SMD) were used to assess adequacy of PSM, whereby a SMD<0.10 indicated a good match. Early outcomes were compared in the matched groups using McNemar's test. In accordance to government privacy legislation, outcomes with <6 events, were presented as absolute risk difference (ARD) between ViV and Redo SAVR to prevent patient re-identification. Late mortality was compared between the matched groups using Kaplan-Meier survival curves and a Cox-proportional hazard model. Results Records of 558 patients undergoing intervention for a failed biological prosthesis between March 2008 to September 2017 in 11 Ontario institutions were reviewed (ViV = 214, redo SAVR = 344). Patients who underwent ViV were older (78.2±8.2 vs 69.1±11.4, p<0.001, SMD=0.92) and had more hypertension, diabetes, ischemic heart disease, atrial fibrillation, congestive heart failure compared to redo SAVR before PSM (SMD>0.20). Propensity matching on 24 variables yielded similar groups for comparison (n=133 pairs). Ages were similar between ViV and Redo SAVR (76.0±6.2 vs 76.0±8.7, SMD=0.003) along with all other comorbidities (SMD<0.1). 30-day mortality was significantly lower with ViV compared to Redo SAVR (ARD: −7.4%, 95% confidence interval (95% CI): −12.4%, −2.3%). The rate of permanent pacemaker implantation (ARD: −8.1%, 95% CI: −14.2%, −2.1%), blood transfusions (ARD: −62.2%, 95% CI: −75.2%, −49.1%) and length of stay (LOS) (mean difference: −7.8 days, 95% CI: −11.0, −4.6 days) were also lower with ViV. All-cause mortality at 5 years was similar between ViV and redo SAVR (Figure, p=0.19). Figure 1 Conclusion ViV TAVR was associated with lower early mortality, risk of permanent pacemaker implantation, any blood transfusion, and hospital LOS compared to redo SAVR in the largest PSM study to date. While there was no difference in late mortality at 5 years, additional studies with more subjects and longer follow-up are necessary. ViV TAVR may be the preferred approach for the treatment of failed biological prosthesis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.344
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes2
Has abstractyes

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