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Abstract 360: How Should We Measure Quality of Care for Transcatheter Aortic Valve Implantation (TAVI)? Results for 6 Quebec TAVI Programs Compared with International Registries

2015· article· en· W2268578868 on OpenAlexaffabout
Laurie Lambert, G. Sas, L. Azzi, Michel Carrier, Benoit Daneault, Éric Dumont, Philippe Généreux, Réda Ibrahim, Yoan Lamarche, Giuseppe Martucci, Nicolas Noiseux, Josep Rodés‐Cabau, Benoît de Varennes, Jean Morin, Peter Bogaty

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsHôtel-Dieu de MontréalRoyal Victoria HospitalHôpital du Sacré-Cœur de MontréalCentre Hospitalier Universitaire de SherbrookeMontreal Heart InstituteInstitut universitaire de cardiologie et de pneumologie de QuébecInstitut National d'Excellence en Santé et en Services Sociaux
Fundersnot available
KeywordsInterquartile rangeMedicineStroke (engine)Emergency medicineIncidence (geometry)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Our publicly funded cardiology evaluation unit examined quality indicators relating to the use and outcomes of transcatheter aortic valve implantation (TAVI). We compared results in Québec hospitals with registries around the world. Methods: We abstracted data on all TAVI cases (n=294) in 6 hospitals in 2013-14. Variables, outcomes and definitions were chosen to facilitate comparisons with registries. Results were presented to TAVI teams with the goal of choosing quality indicators and improving performance. Results: Annual TAVI center volume ranged from 30 to 115 and rate of TAVI varied widely across Quebec’s 16 health regions, as did rates around the world. Median patient age was 83 years (interquartile range [IQR]: 78-86) with little variation across centers and international registries. Documented proportion of patients with NYHA class III/IV was 64% (188/294) for Quebec and varied from 75 to 86% across registries. A surgical risk score (STS) was recorded in only 53% (156/294) of Quebec patients but was often obligatory in registries. The median STS score for Quebec was very similar to that reported in the USA [6% (IQR: 4-10) vs 7% (IQR: 5-11), respectively] but was much lower than in France, Austria and Brazil. Frequency of in-hospital adverse events varied widely across Québec centers: stroke (0-7.5%), bleeding (16.7-26.7%) and transfusion (16.7-43.3%). Overall incidence of in-hospital stroke was 2.3% (7/294) and varied from 1.8 to 5.3% across registries. Bleeding and transfusion were rarely reported in registries. Conversion to surgery was 3% (9/294) in Quebec, and 0.4-4.3% in registries. In Quebec, 15% of patients required a new pacemaker, with wide variation across centers (0-21%) and registries (6.6-25%). Rates of procedural success varied widely across Quebec, largely due to non-standardized classification of post-TAVI aortic regurgitation. Such classification also varied across registries. Overall procedural success for Quebec (73%; 215/294) was lower than in registries but in the latter, the definition of success was often unclear. In-hospital mortality in Quebec was 6.5% (19/294) versus 5.5% in USA and a reported 5.1% for transvascular and 7.7% for transapical TAVI in Germany. Other registries only reported 30-day mortality, varying from 4.1 to 9.1%. Discharge home was more likely in Quebec (80%) than the USA (63%); this outcome was not reported elsewhere. Conclusions: Practice and outcomes in TAVI vary widely across Quebec hospitals and internationally. Despite publication of recommended endpoints (VARC and BARC), there is a lack of standardized reported outcomes and patient populations. While quality benchmarks for patient selection and outcomes remain unclear, continued monitoring with timely feedback to TAVI teams and decision-makers is essential for this new and costly intervention that is predominantly being performed in the very elderly.

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)0.001
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 score0.070
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
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 head0.146
GPT teacher head0.397
Teacher spread0.250 · 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
Published2015
Admission routes2
Has abstractyes

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