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Record W2118534861 · doi:10.1093/europace/eus406

The role of implantable cardiac defibrillators in cardiac sarcoidosis: saviour or sinner?

2013· letter· en· W2118534861 on OpenAlexaboutno aff
Patrick M. Heck, P. Roberts

Bibliographic record

VenueEP Europace · 2013
Typeletter
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiac sarcoidosisCardiologyInternal medicineSarcoidosis

Abstract

fetched live from OpenAlex

This editorial refers to ‘Efficacy and safety of implantable cardiac defibrillators for treatment of ventricular arrhythmias in patients with cardiac sarcoidosis’ by J. Kron et al., on page 347 Sarcoidosis has been described as an enigmatic disease and shows significant heterogeneity in pattern, severity, and clinical course, with substantial geographical variations. For the majority affected, it carries a benign course, typically confined to the lungs and requiring no treatment.1 The same cannot be said when sarcoidosis involves the heart. Cardiac involvement in sarcoidosis manifests clinically in ∼5% of patients, although autopsy studies have estimated cardiac involvement at closer to 30%.2 Progressive heart failure and malignant cardiac arrhythmias account for up to 77% of deaths due to sarcoidosis.3 Although there are no randomized studies evaluating the role of implantable cardiac defibrillators (ICDs) in cardiac sarcoidosis (CS), ICD implantation for CS carries a Class IIa recommendation in published guidelines.4 In this issue of the Journal, Kron et al.5 report on the largest retrospective series of patients with CS and ICDs. A total of 235 patients from Canada, USA, and India were included in their analysis, with a mean follow-up period of more than 4 years. Within the limitations of a retrospective analysis, their data reinforces two important points: first, CS has a high incidence of potentially life-threatening arrhythmias; secondly, ICDs are not benign devices. Two other single-centre series assessing the long-term follow-up of patients with CS and ICDs have been published recently. Schuller et al.6 published a series of 112 patients with CS and ICDs, and Betensky et al.7 a series of 45 patients. Although some patients are acknowledged to feature in more than one series, the messages from each study are fairly consistent. The majority of patients (62–74%) received their ICD for a primary prevention indication, with a mean left ventricular ejection fraction of ∼45%. The annual appropriate ICD therapy rate ranged from 8.6 to 15%, notably higher than seen in large primary prevention ICD trials, such as SCD-HeFT (Sudden Cardiac Death In Heart Failure Trial), where the rate was 5.1% per year.8 While it is evident that potentially life-threatening arrhythmias occur frequently in this patient population, the benefits of ICD therapy do not come without a price. Inappropriate shock therapy was seen in almost a quarter of patients (24.3%). While atrial fibrillation was the commonest cause, mechanisms to avoid this, such as a tailored approach to device programming require further evaluation. Other ICD-related adverse events occurred in ∼15% of patients, most notably lead dislodgement or fracture. These factors need to be balanced against the potential benefits offered by ICD therapy and it is incumbent on physicians to openly discuss these issues with patients prior to implantation. More effective risk stratification in patients with CS would potentially improve the risk: benefit ratio for ICD therapy. Programmed stimulation in CS has been shown to predict future arrhythmic events in two small series,9,10 although in one series 10% of patients with a negative ventricular stimulation study experienced sustained ventricular arrhythmia or sudden cardiac death during follow-up.10 All three case series following CS patients with ICDs found reduced ejection fraction to be a predictor of appropriate ICD therapy,5–7 although the majority of patients receiving therapy had an ejection fraction greater than the 35% cut-off used in many studies for primary prevention ICDs, suggesting this cut-off is not applicable for CS. Two of the studies also showed that advanced conduction system disease, represented by either complete heart block or ventricular pacing, was also a significant predictor of appropriate ICD therapy.5,7 This is, perhaps, not surprising as advanced conduction system disease is likely to be a surrogate marker for more extensive granulomatous infiltration of the myocardium and specifically the septum. It does, however, raise the question as to whether patients with sarcoidosis and standard pacing indications should be encouraged to have a primary prevention ICD implanted at the outset, rather than a simple pacemaker. A further challenge in the management of patients with non-cardiac sarcoid is the issue of how to screen for cardiac involvement. While only a minority of patients will have cardiac sarcoid, it carries an adverse outcome and accounts for a significant mortality in this population. There is very little guidance on how patients should be screened for cardiac involvement other than directed investigations when symptoms occur and utilization of a multidisciplinary approach to management of the patient with sarcoid.11 In the absence of randomized studies of ICDs in CS (and it is unlikely any will be undertaken), the study by Kron et al. provides valuable insight into the role ICDs play in CS patients. Undoubtedly ICDs save lives, but not without exacting a price, so it is important that physicians and industry continue to refine device therapy so as to minimize inappropriate shocks and adverse events. Conflict of interest: P.R.R is currently conducting research for Medtronic and St Jude Medical. He has received consultancy fees from Medtronic and Boston Scientific.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.270
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations9
Published2013
Admission routes1
Has abstractyes

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