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Record W2292067254

Implantable Cardiac Defibrillators for Primary Prevention of Sudden Cardiac Death in High Risk Patients: A Meta-Analysis and Economic Review

2007· article· en· W2292067254 on OpenAlexaffabout
Chuong Ho

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsCanadian Agency for Drugs and Technologies in Health
Fundersnot available
KeywordsMedicineSudden cardiac deathPopulationClinical trialRandomized controlled trialMeta-analysisCost-effectiveness analysisPrimary preventionIntensive care medicineCost effectivenessEmergency medicineImplantable cardioverter-defibrillatorInternal medicineCardiologyDisease
DOInot available

Abstract

fetched live from OpenAlex

Background and objectives Sudden cardiac death (SCD) due to cardiac arrhythmia is responsible for many deaths each year. Since only 5% of patients survive a cardiac arrest, primary prevention in high risk patients is necessary. Because of the large amount of patients eligible for an implantable cardiac defibrillator (ICD), the uncertain clinical efficacy of ICD therapy, and the device high cost, this work aims to provide health policy makers of the evidence on clinical efficacy and cost-effectiveness of this therapy. Methods A meta-analysis of randomized controlled trials reporting clinical outcomes from the use of ICDs for primary prevention was done. A literature review of cost-effectiveness surrounding ICD treatment and a budget impact analysis were performed. Using a population-based approach, we defined the budgetary impact of ICD therapy for primary prevention of SCD as the difference between budgets with and without ICD prophylactic use. Microsoft Excel was used to program the budget impact analysis. Results ICDs in addition to conventional therapy significantly reduced the risk of SCD by 67% in ischemic and 74% in non-ischemic, patients. Numbers-needed-to-treat to prevent one SCD were 12 and 28 in ischemic and non-ischemic patients, respectively. Our review showed that ICDs generally cost more than conventional management but were more effective in treating patients without prior clinical arrhythmia. If effectiveness was measured by life-year, the majority of incremental cost-effectiveness ratio (ICER) estimations were below or slightly above the commonly-used willingness to pay threshold of US$50,000 per life year gained. If effectiveness was measured by quality adjusted life year (QALY), the ICER ranged from US$34,000 to $97,863, but all were below US$100,000 per QALY gained for patients with ejection fraction (EF) ≤ 0.30. For patients with 0.31 to 0.40 EF, the ICER went up to US$195,700 per QALY gained. From the perspective of the health care system, if the cost associated with SCD was C$300 per case, the estimated budget impact of using ICD for primary prevention of SCD was C$88.58 millions, C$332.37 millions, C$634.39 millions, C$834.40 millions and C$1.04 billions respectively for 1-, 3-, 5-, 6-, and 7-year time horizons. Conclusions Our review provides evidence that the use of ICDs, combined with optimized pharmacological therapy, can significantly reduce all-cause death and SCD in patients at high-risk of ventricular arrhythmia. Whether the ICD treatment is cost effective or not compared with conventional therapy depends on the threshold of willingness-to-pay for one life year or one QALY gained. Our review indicated that the cost-effectiveness of ICD treatment was mainly driven by the device efficacy, implantation cost and patient's health utility. ICD prophylactic use would result in a substantial budget impact for the Canadian health care system. Compared with the cardiac event risk a patient with usual medical therapy would experience and the resulting cost, the expensive ICD device and its replacement cost within five to ten years absolutely determined the budget impact.

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)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.032
Bibliometrics0.0070.006
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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 designMeta-analysis
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
Published2007
Admission routes2
Has abstractyes

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