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Abstract 15961: Multicenter Prospective Prevention of Sudden Death in High Risk Patients Utilizing Enhanced ACC/AHA Risk Model

2020· article· en· W3101522223 on OpenAlexaffabout
Ethan J. Rowin, Martin S. Maron, Arnon Adler, Alfred Albano, Armanda M. Varnava, Kevin Leong, Stephen B. Heitner, Stephen L. Winters, Matthew W. Martinez, Dana Marsy, Emilie Cohen, Dana Spears, Adaya Weissler Snir, Harry Rakowski, Barry J. Maron

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineSudden cardiac deathHypertrophic cardiomyopathyImplantable cardioverter-defibrillatorInternal medicineSudden deathProspective cohort studyCardiologyCohortRisk assessmentRisk factorObservational studyFramingham Risk ScoreDisease

Abstract

fetched live from OpenAlex

Introduction: Strategies for reliable selection of high-risk hypertrophic cardiomyopathy (HCM) patients for prevention of sudden cardiac death (SCD) with implantable cardioverter-defibrillators (ICDs) continue to be debated. Objective: Assess the sensitivity of sudden death risk strategies in predicting SCD events (appropriate ICD shocks, sudden death or out of hospital cardiac arrest) among a large multicenter cohort of high-risk HCM patients. Methods: Observational longitudinal study from 6-HCM centers in North America and Europe to determine outcomes in consecutive HCM patients considered high risk for sudden death based on an enhanced ACC/AHA (U.S./Canada) guidelines-based risk factor algorithm with primary prevention ICD placement. ESC risk score was retrospectively calculated in this cohort and compared to ACC/AHA risk factor method for predicting SCD events. Results: Of 1185 patients with primary prevention ICDs implanted based on ≥ 1 major risk marker, 162 (14%) experienced device therapy terminating VT/VF episodes at 49 ± 18 years of age and 4.6 ± 4.2 years after device implant. Within the 6 HCM centers, only 28 other patients not implanted with ICD died suddenly or had resuscitated cardiac arrests, including 19 (68%) with risk-markers who declined ICDs. Of these 190 high risk patients with SCD or SCD events, 67 (35%) had ESC risk-scores scores ≥6%/5-years, considered sufficient to recommend a prophylactic ICD, while 83 (44%) had low risk scores (<4%/5-years) that likely would have excluded an ICD recommendation. Compared to enhanced ACC/AHA risk factors, the ESC risk-score was less sensitive than ACC/AHA (35% vs. 95%, p<0.01), consistent with identifying fewer high-risk patients with events. Conclusion: In this large multicenter study of high-risk HCM patients, an enhanced ACC/AHA risk factor strategy was superior to the ESC risk score in identifying patients at greatest risk for SCD events.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.297
Teacher spread0.264 · 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
Published2020
Admission routes2
Has abstractyes

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