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Record W4412887616 · doi:10.1093/ehjqcco/qcaf079

Non-cardiac surgery after transcatheter aortic valve implantation

2025· article· en· W4412887616 on OpenAlexaff
Amir Geressu, Robert T. Sparrow, Santiago García, Pedro Villablanca, Islam Y. Elgendy, SeonHo Jang, Mamas A. Mamas, Rodrigo Bagur

Bibliographic record

VenueEuropean Heart Journal - Quality of Care and Clinical Outcomes · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicinePerioperativeIncidence (geometry)Stroke (engine)SurgeryCardiac surgeryPopulationAdverse effectCardiologyInternal medicine

Abstract

fetched live from OpenAlex

AIMS: There is a lack of data on perioperative outcomes for patients undergoing non-cardiac surgery (NCS) after transcatheter aortic valve implantation (TAVI). Hence, we aimed to determine the incidence, type of surgery, timing, and perioperative outcomes of individuals undergoing elective NCS after discharge for TAVI. METHODS AND RESULTS: Hospitalisations for TAVI were identified from the US National Readmission Database between 2012 and 2021, and patients who received NCS within 6 months were included for analysis. Incidence, type, and timing of planned readmissions for NCS were evaluated according to the surgical risk as low, intermediate, and high. The primary outcome was the occurrence of in-hospital major adverse events (MAE), defined as the composite of death, cardiac complications, and stroke/transient ischaemic attack. Multivariable regression models were constructed to identify independent factors associated with MAE. Out of 502 775 TAVI procedures, 2390 (0.48%) patients were electively admitted to undergo NCS within 6 months after discharge for TAVI. Surgeries were classified as low- (n = 321, 13.4%), intermediate- (n = 1522, 63.7%), and high-risk (n = 547, 22.9%). The median age of the study population was 78 years (IQR 73-84), with 59% of participants being male. Overall surgeries occurred at a median of 83 days (IQR 48-120) after discharge for TAVI, a time-period which was significantly shorter for those who underwent high-risk surgeries (median 67, IQR 41-109 days, P < 0.001). The overall rate of post-operative MAE was 7.6% (n = 181), and these rates did not differ between surgical risk groups (P = 0.46). The primary outcome was driven primarily by cardiac complications (3.6%), while rates of death were low (1.3%) and almost identical between surgical risk groups (P = 0.99). Factors independently associated with the primary outcome were congestive heart failure (aOR: 1.62, CI: 1.23-2.12, P < 0.001), liver disease (aOR: 2.17, CI: 1.37-3.45, P = 0.001), diabetes mellitus (aOR: 1.44, CI: 1.13-1.82, P = 0.003), cancer (aOR: 1.18, CI: 0.92-1.50, P < 0.001), anaemia (aOR: 0.76, CI: 0.58-0.99, P = 0.046), and time to readmission (aOR: 1.00, CI: 0.99-1.00, P = 0.004). CONCLUSION: Elective NCS occurred infrequently post-TAVI and was associated with low rates of mortality. While diabetes mellitus, congestive heart failure, liver disease, cancer, anaemia, and time to readmission were associated with post-procedural adverse events, the surgical risk was not. The risk of NCS after TAVI should be balanced against the risk of delaying an operation.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.461
Teacher spread0.396 · 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
Published2025
Admission routes1
Has abstractyes

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