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Abstract 14343: Early Genetic Screening and Cardiac Intervention in Patients With Genetic Cardiomyopathies Within a Multidisciplinary Care Model

2023· article· en· W4389958158 on OpenAlexaff
Chandu Sadasivan, Luke Gagnon, Kaiming Wang, Deepan Hazra, Tara Dzwiniel, Susan Christian, Jissy Thomas, Anita Y.M. Chan, D. Ian Paterson, Wayne Tymchak, Gavin Y. Oudit

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsMedicineGenetic testingCardiomyopathyInternal medicineEjection fractionHypertrophic cardiomyopathyProspective cohort studyCardiologyHeart failure

Abstract

fetched live from OpenAlex

Introduction: Patients with genetic cardiomyopathies are a heterogeneous group of patients who experience significant morbidity and mortality. Early cardiac assessment and intervention coupled with access to genetic counselling and testing may improve clinical outcomes and prevent progression to end-stage heart failure. Methods: Our prospective cohort study was conducted at our multidisciplinary Cardiomyopathy Clinic and patients with suspected genetic cardiomyopathy (CM) or a family history of CM ( n =405) were recruited. Patients with genetic analysis completed ( n =228) were categorized into dilated cardiomyopathy (DCM) ( n= 118), hypertrophic cardiomyopathy (HCM) ( n= 55), infiltrative cardiomyopathy (CM) ( n= 18), and Stage A/at-risk for CM ( n= 37). Continuous variables were analyzed using Wilcoxon signed-rank test or paired t test, while categorical variables were compared using Pearson chi-square tests. Results: There was a median follow-up time of 13 months (IQR: 7 - 21 months) with an increase in genetic testing, cascade family screening, optimization of guideline-directed medical therapy, and usage of device therapies compared to prior to clinic enrolment. In patients with genetic testing, 29.4% ( n= 67) had positive genotypes (pathogenic or likely pathogenic variants), 11.8% ( n= 27) had variants of unknown significance (VUS), and 58.8% ( n= 134) had negative or inconclusive genotypes. Optimization of medical and device therapies resulted in improvements in left ventricular ejection fraction from 30.5% (20.8 - 42.5%) to 45.5% (34.0 - 48.5%, P< 0.001), reduced left ventricular mass index from 121.2 g/m 2 (116.2 - 146.4 g/m 2 ) to 107.0 g/m 2 (88.9 - 134.7 g/m 2 , P< 0.001), and reduced E/e’ ratio from 12.6 cm/s (9.2 - 15.3 cm/s) to 10.9 cm/s (9.0 - 14.2 cm/s, P= 0.015). These improvements were driven by patients with negative genotypes (no variants or VUS) relative to those with positive genotypes. Additionally, patients with normal blood pressure (SBP < 120 mmHg and DBP < 80 mmHg) at follow-up had improvement in LVEF ( P< 0.001) and LVMI ( P= 0.005). Conclusion: Our findings demonstrate the efficacy of a combined cardiovascular genetics clinic in improving the clinical trajectories of patients with genetic cardiomyopathies.

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)0.004
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.254
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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