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Abstract 4142400: Clinical implication and potential role of immunosuppression for myocarditis episodes in patients with desmoplakin cardiomyopathy: hope from the DSP-ERADOS Network?

2024· article· en· W4404359922 on OpenAlexaff
Alessio Gasperetti, Giovanni Peretto, Steven A. Muller, Mikaël Laredo, Richard Carrick, Babken Asatryan, Alexandros Protonotarios, Brittney Murray, Paul J. Scheel, Kalliopi Pilichou, Petros Syrris, Max Jason, Kristen Medo, Valentina A. Rossi, Ardan M. Saguner, Robyn J. Hylind, Dominic J. Abrams, Julia Cadrin‐Tourigny, Iacopo Olivotto, Elena Biagini, Moniek G.P.J. Cox, Philippe Charron, James S. Ware, L. A. Cal, Eric D. Smith, Jodie Ingles, Flavie Ader, Stacey Peters, Jeremy Russo, Dominica Zentner, Eric Schulze Bahr, Eric Carruth, Chris Haggerty, Victoria N. Parikh, Matthew Taylor, Luisa Mestroni, Gianfranco Sinagra, Arthur A. Wilde, Estelle Gandjbakhch, Anneline Te Riele, J. Peter van Tintelen, Perry Elliott, Luigi Adamo, Cynthia A James, M. Merlo, Neal K. Lakdawala, Barbara Bauce, Adam Helms, Hugh Calkins, Nisha A. Gilotra

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineDesmoplakinCardiomyopathyImmunosuppressionMyocarditisInternal medicineCardiologyHeart failureGenetics

Abstract

fetched live from OpenAlex

Introduction: Variants in the desmoplakin (DSP) gene are associated with a form of arrhythmogenic cardiomyopathy (ACM) characterized by high risk of ventricular arrhythmias (VAs), heart failure (HF), and recurrent myocarditis. The clinical implications and management of myocarditis episodes in DSP-ACM have been scarcely investigated and are a major unmet clinical challenge. Research Question: what is the clinical impact of myocarditis episodes in DSP-ACM? Is there any role for immunosuppression? Methods: Patients (pts) with DSP-ACM in the worldwide DSP-ERADOS Network (26 institutions across 9 countries in 3 continents) were enrolled. Pts were classified as myocarditis-positive (Myo+) if they had a diagnosis of myocarditis at any time as per ESC criteria and otherwise as negative (Myo-). A multi-state model was used to compare the time-dependent risk of the outcome of interest (combined occurrence of VAs or HF hospitalization) at follow up. Hazard ratios (HR) were reported for quantifying the impact of single or repeated myocarditis on combined outcome occurrences, as well as the impact of immunosuppression. Results: A total of 801 pts (38.8±17.3 yo, 63.9% female; LVEF 49.7±14.7%; RVEF 50.1±13.2%) were enrolled. Over 3.5 [1.2–6.7] years, 153 (19.1%) patients (Myo+) experienced 260 myocarditis episodes (1.7 episode/pt; n=54 pts with 2+ episodes), 79 of which were treated with immunosuppression. A total of 193 (24.1%; 6.7%/year) pts experienced the combined end-point (n=120/648 (18.5%) Myo-; n=73/153 (47.7%) Myo+; log rank p <0.001). Occurrence of myocarditis was associated with increased risk in the composite outcome, with repeated myocarditis having a non-significant trend towards a higher risk than single events (Panel A). However, when adjusting for treatment with immunosuppressants in the Myo+ cohort, there were no differences in endpoint compared to pts in the Myo- cohort (Panel B) (Relative HR to Myo- pt: for treated Myo+ episodes: 0.76 [0.33–1.72], p=0.503; for untreated Myo+ episodes: 2.89 [2.11–3.95], p <0.001). Application: Myocarditis episodes in pts with DSP-ACM are associated with increased risk in VA/HF hospitalizations. This analysis suggests a potential benefit of immunosuppression therapy for myocarditis episodes in DSP cardiomyopathy and warrants further study. Next Steps/Future: A randomized controlled trial is needed to test the role of immunotherapy in this patient population.

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)0.002
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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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Citations3
Published2024
Admission routes1
Has abstractyes

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