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Record W4200101218 · doi:10.1093/eurheartj/suab147.025

599 Gender-based differences in TAVI outcomes: report from a large contemporary real-world population of self-expandable valves

2021· article· en· W4200101218 on OpenAlexaff
Alessandro Sticchi, Francesco Gallo, Vincenzo De Marzo, Kim Won-keun, Tobias Zeus, Rossella Ruggiero, Stefan Toggweiler, Federico De Marco, Bernhard Reimers, Luis Nombela‐Franco, Marco Barbanti, Salvatore Brugaletta, Tommaso Piva, Josep Rodés‐Cabau, Italo Porto, Antonio Colombo, Francesco Giannini

Bibliographic record

VenueEuropean Heart Journal Supplements · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineStroke (engine)Clinical endpointCohortInternal medicineDiabetes mellitusPopulationRetrospective cohort studyRandomized controlled trialCohort studyCardiologySurgeryEndocrinology

Abstract

fetched live from OpenAlex

Abstract Aims Small sub-study data derived from randomized clinical trials suggest a gender-based disparity in TAVI outcomes. However, large real-world contemporary data is missing. The aim of this study is to compare the risk factors, procedural characteristics and clinical outcomes of male and female patients who underwent transcatheter aortic valve implantation (TAVI) using two next-generation self-expandable bioprostheses (ACURATE neo and Evolut R/Pro valves). Methods We performed a first unmatched comparison and a propensity score-matched analysis (PSM) to assess the outcomes derived by the sex difference beyond the impact of pre-procedural risk factors in a large, contemporary, real-world, multicentre, international, retrospective registry of 3862 consecutive patients. The primary endpoint was a composite of all-cause death or any stroke (disabling and non-disabling) at 1 year. Results Sixty-four per cent (2162/3353 patients) of the study cohort was female and was older (mean age 82.3 years vs. 81.1 years for men (P<0.001)) had a higher BMI (27.7±5.7 for women vs. 27.2±4.5 for men), and lower prevalence of dyslipidaemia (50.2% vs. 54.7, P=0.037), diabetes (26.8% vs. 33.7, P<0.001), smoking (10.0% vs. 24.3%, P<0.001), COPD (17.4% vs. 21.9%, P=0.002), pacemaker/ICD (9.6% vs. 14.0%, P<0.001), previous cardiac surgery (8.6% vs. 18.8%, P<0.001), previous PCI (23.0% vs. 36.8%, P<0.001). Mean STS score for women was higher 5.2±3.9% vs. 4.5±3.4% (P<0.001). Women had higher mean valve gradients (45.4±17.1 vs. 42.7±14.7 mmHg; P<0.001), smaller valve areas (mean 0.7 cm2 vs. 0.9 cm2, P=0.037) and smaller annular perimeters (56.8±23.0 vs. 62.0±23.8, P<0.001). The primary endpoint was resulted in a rate of 7.9% vs. 6.9% (P=0.337) in the unmatched population and 9.4% vs. 6.0% (P=0.014) after the PSM, respectively for women and for men. Independently, there was no difference in mortality (5.9% vs. 5.6%; P=0.786) and stroke (2.5% vs. 1.8%; P=0.243) rates between women and men in the un-matched groups. Rates of cardiac tamponade (1.5% vs. 0.4%, P=0.008), major vascular complications (7.7% vs. 4.1%, P<0.001), life-threatening bleeding (2.8% vs. 1.4%, P=0.016), major bleeding (5.1% vs. 2.9%, P=0.004), need of transfusion (8.9% vs. 4.6%, P<0.001) and acute kidney injury (8.5% vs. 5.7%, P=0.009), were all significantly higher in women. After PSM, mortality was similar between the two groups (11.3% for women vs. 9.5% for men, P=0.264) but strokes were more prevalent in women (2.8% vs. 1.2%, P<0.024). Furthermore, in the matched population, major vascular complications (6.8% vs. 4.1%, P=0.024), need of transfusion (9.1% vs. 4.6%, P<0.001) and acute kidney injury (8.7% vs. 5.6%, P=0.009) remained significantly different between women and men, respectively. Conclusions In this large real-world contemporary TAVI registry, female gender was associated with higher rates of stroke, vascular complications, major bleeding, and acute kidney injury. Further studies are required to explore the underlying pathophysiological mechanisms for these observations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.075
GPT teacher head0.387
Teacher spread0.312 · 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.

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
Published2021
Admission routes1
Has abstractyes

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