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Record W2998189850 · doi:10.1136/heartjnl-2019-bcs.69

71 Outcome in patients undergoing high-risk pci using impella circulatory support - 10 year experience

2019· article· en· W2998189850 on OpenAlexaboutno aff
Vincenzo Vetrugno, Muhammad Waqas, Kulwinder Singh Sandhu, Anthony Mechery, M. Adnan Nadir, Sudhakar George, Alexander Zaphiriou, Hoong Sern Lim, Peter Ludman, Sagar N. Doshi, John Townend, Sohail Q. Khan

Bibliographic record

VenueInterventional Cardiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImpellaConventional PCICardiogenic shockPercutaneous coronary interventionInternal medicineCardiologyCanadian Cardiovascular SocietyAnginaCoronary artery diseaseEjection fractionHeart failureRevascularizationMyocardial infarction

Abstract

fetched live from OpenAlex

<h3>Introduction</h3> The use of Impella in the setting of high-risk percutaneous coronary intervention (PCI) has been introduced in clinical practice in the last few years in selected hospitals, giving a chance for revascularization to a sensible number of patients who are usually unsuitable for cardiac surgery due to high risks. In addition, some of these patients present pre-procedural cardiogenic shock, further increasing the rate of morbidity and mortality. Our purpose was to assess outcome of patients undergoing Impella-assisted high-risk PCI in our 10 year experience and to assess possible predictors of adverse events. <h3>Methods</h3> From May 2008 to September 2018 patients undergoing Impella-assisted high-risk PCI were enrolled. Clinical, laboratory, echocardiographic, angiographic and procedural data were collected. Coronary artery disease burden was graded using the British Cardiovascular Intervention Society Jeopardy Score (BCIS-JS). In-hospital MACCE were recorded. During routine follow-up visits data including MACCE, hospital admissions for heart failure, Canadian Cardiovascular Society (CCS) angina grade and New York Heart Association (NYHA) functional class were recorded. Long-term survival Kaplan Meier analysis was performed according to national registry death data. <h3>Results</h3> A total of 80 consecutive patients were enrolled (71.2±13.7 years, male gender 73.8%), 21 (26.3%) presenting with stable angina, 53 (66.3%) with NSTE-ACS and 6 (7.5%) with STEMI. 67 (83.8%) of them showed multivessel disease, 42 (52.5%) unprotected left main disease, 47 (58.8%) severe left ventricle systolic dysfunction (LVEF&lt;30%), 10 (12.5%) pre-procedural cardiogenic shock. Median BCIS-JS was 10 [8,00; 12,00]. In-hospital MACCE occurred in 16 (20%) patients with death in 15 (18.8%). Median time to first follow-up visit for survivors was 105 (64.5; 282.0) days: at this time 11 (13.8%) patients had MACCE, 3 (3.8%) had hospital admissions for heart failure, median CCS was 0.00 (0.00; 0.00) and median NYHA was 1.00 (1.00; 2.00). Mean survival time (procedure to death, months) was 21 months (C.I. 14.4 - 29.0). Multivariate logistic regression analysis for possible predictors of in-hospital MACCE was performed including the variables showing a p value &lt;0.100 at univariate analysis, i.e. pre-procedural cardiogenic shock [OR 9.00, C.I. (2.1–37.6), p=0.003] and CK peak [OR 1.00, C.I. (1.0–1.0), p=0.051]. Pre-procedural cardiogenic shock was the only predictor [OR 7.058, C.I. (1.2–40.6), p=0.029] of in-hospital MACCE. No significant predictors of MACCE at follow-up were found at logistic regression analysis. <h3>Conclusion</h3> In our 10 year experience of Impella-assisted high-risk PCI, 20% patients had in-hospital MACCE and mean survival was 21 months. At follow up, MACCE rate was less than 4% and both angina and heart failure symptoms were well controlled. Pre-procedural cardiogenic shock was the only predictor of in-hospital MACCE. <h3>Conflict of Interest</h3> No conflict of interest

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)0.000
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 score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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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Citations2
Published2019
Admission routes1
Has abstractyes

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