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Record W3096957700

Primary prevention implantable cardioverter defibrillators: where’s the block? A pilot study in a regional cardiac centre

2009· article· en· W3096957700 on OpenAlexaboutno aff
PP Sadarmin, Kck Wong, Yaver Bashir, Kim Rajappan, TR Betts

Bibliographic record

VenueHighWire Press Open Archive · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEjection fractionImplantable cardioverter-defibrillatorNicePopulationImplantDefibrillationCanadian Cardiovascular SocietySecondary preventionHeart failureEmergency medicineInternal medicineSurgeryMyocardial infarction
DOInot available

Abstract

fetched live from OpenAlex

Introduction The UK has one of the lowest implantable cardioverter defibrillator (ICD) implant rates in the western world. The majority of implants are for secondary prevention. It has been estimated that there are a minimum of 40 patients/million per year who satisfy current National Institute for Health and Clinical Excellence (NICE) criteria for primary prevention ICD alone. It is not known whether the low implant rate for primary prevention is due to failure to identify eligible patients in primary or secondary care, a lack of knowledge of ICD guidelines, failure to refer to implanting cardiologists, a lack of access or capacity in tertiary care, financial restrictions or patient choice to refuse treatment. This pilot study was designed to identify the stumbling blocks for potential primary prevention ICD recipients who might otherwise qualify for an ICD according to current NICE guidelines. Methods A search was performed on the Oxford Radcliffe Hospitals echo and the British Cardiovascular Intervention Society (BCIS) databases for patients with an Oxfordshire postcode who had documentation of left ventricular ejection fraction (LVEF) recorded in the calendar year 2007. Patients less than 18 years were excluded. The search criteria included LVEF ⩽35%. In addition, the descriptive terms “severely impaired function” or “poor LVEF” were taken to indicate LVEF <30%. Medical notes were assessed for age, aetiology of heart disease, time from myocardial infarction, 12-lead ECG QRS duration, Holter or electophysiological studies, NYHA status and review by a cardiologist. Results 215 patients with LVEF ⩽35% were identified from a database population of 3100 assessments and 30 patients were randomly sampled. The findings are summarised in the fig. Three patients already had an ICD. 11 patients were deemed not suitable. 12/16 had confirmed ischaemic heart disease. Five of 12 patients satisfied MADIT2 trial criteria and had QRS duration greater than 120 ms. Four of those five patients had seen a cardiologist yet none had discussed potential primary prevention ICD implants or had referral to an electrophysiologist. Seven of 12 patients were eligible for Holter monitor screening to look for non-sustained ventricular tachycardia, yet none had undergone monitoring or been referred for ventricular stimulation studies. Abstract 096 Figure ICD, implantable cardioverter defibrillator; IHD, ischaemic heart disease; LVEF, left ventricular ejection fraction; NICE, National Institute for Health and Clinical Excellence; VT, ventricular fibrillation. Conclusions In this pilot study, screening of an echo and BCIS database revealed that at least one sixth of patients with LVEF ⩽35% fulfilled NICE guidelines for primary prevention ICD, yet despite seeing cardiologists, none had been offered this therapy. Holter screening of potentially eligible patients is not being performed. A larger study is now underway to explore further the current barriers to the uptake of primary prevention ICD in the UK.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.310
Teacher spread0.265 · 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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Citations0
Published2009
Admission routes1
Has abstractyes

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