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Record W3191469024 · doi:10.1136/heartjnl-2017-bcis.27

27 The association of frailty and coronary artery disease burden in older patients with non-st elevation acute coronary syndrome

2017· article· en· W3191469024 on OpenAlexaboutno aff
Sophie Gu, Jonathan A. Batty, Rebecca Jordan, Murugapathy Veerasamy, Hannah Sinclair, Alan Bagnall, Rajiv Das, Ioakim Spyridopoulos, Mohaned Egred, Azfar Zaman, Ian Purcell, Richard Edwards, Javed Ahmed, Vijay Kunadian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute coronary syndromeInternal medicineCoronary artery diseaseCardiologyPerforationComplicationSurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Introduction Frailty is common in older patients who present with Acute Coronary Syndrome (ACS). However, its association with coronary disease complexity measured by Syntax and British Cardiovascular Intervention Society jeopardy score (BCIS JS) is not known. Methods 276 patients ≥75 years of age, admitted for invasive management of Non-ST Elevation ACS (NSTEACS), were enrolled into a 2 centre prospective observational study (ICON1 study-NCT01933581). Frailty was assessed using the Fried criteria (score 0 robust, 1 or 2 pre-frail and ≥3 is frail). Syntax 1.0 calculator, BCIS algorithm and Global Registry of Acute Coronary Events (GRACE) 2.0 calculator were used to calculate scores. Procedure complication includes dissection, distal embolization, abrupt closure, thrombus, perforation and loss of side branch evaluated at Newcastle angiographic core lab. Results Frail patients tend to have higher GRACE 2.0 scores (124.0±13.7 vs 131.3±19.7 vs 136.0±19.5, p=0.008). With increasing frailty, there was a non-significant increase in the proportion with SYNTAX score greater than 22 (20.8% vs 27.5% vs 31.5%, p=0.436) and BCIS JS ≥6 (56.0% vs 61.6% vs 64.1%, p=0.651). Frail patients were more likely to have moderate to severe calcification (34% vs 44.8% vs 62.0%, p=0.005). Overall procedure complication rate is low at 4.6%, with no difference among frailty groups (2.3% vs 4.5% vs 6.3%, p=0.76). Abstract 27 Table 1 Baseline characteristics by fried frailty status Variables Overall n= 276 Robust n= 50 Pre-frail n= 147 Frail n= 79 P value Age (mean±SD) years 81.1±4.1 80.0±3.8 81.2±4.1 81.7±4.2 0.076 Male gender n(%) 165 (59.8) 37 (74.0) 90 (61.2) 38 (48.1) 0.012 Hypertension n(%) 202 (73.5) 35 (70.0) 103 (70.5) 64 (81.0) 0.197 Diabetes n(%) 70 (25.5) 7 (14.0) 40 (27.4) 23 (29.1) 0.116 Smoking history n(%) 159 (57.6) 31 (62) 75 (51) 53 (67.1) 0.052 Hypercholesterolaemia n(%) 159 (57.8) 29 (58.0) 83 (56.8) 47 (59.5) 0.929 Family history of IHD n(%) 78 (28.7) 12 (25.0) 41 (28.3) 25 (31.6) 0.716 Previous MI n(%) 92 (33.3) 11 (22.0) 45 (30.6) 36 (45.6) 0.013 Cerebrovascular disease n(%) 46 (16.7) 3 (6.0) 21 (14.3) 22 (27.8) 0.003 Congestive cardiac failure n(%) 24 (8.7) 1 (2.0) 10 (6.8) 13 (16.5) 0.009 NYHA class III/IV n(%) 53 (19.2) 4 (8.0) 24 (16.3) 25 (31.6) 0.002 CCS class III or IV n(%) 39 (14.1) 6 (12.0) 19 (12.9) 14 (17.7) 0.548 BMI- body mass index, CCS- Canadian cardiovascular society, IHD- ischaemic heart disease, NYHA- New York heart association, PCI- percutaneous coronary intervention, SD- standard deviation, MI- myocardial infarction Conclusions Frail older patients have higher GRACE score and more severe calcification in the coronary arteries. However, the complexity of coronary disease is not significantly different in this patient group.

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)0.002
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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".

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Published2017
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