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Record W4388594854 · doi:10.1093/eurheartj/ehad655.679

Subcutaneous versus transvenous ICDs: a population-level comparison of incidence, patient characteristics, and complications of S-ICDs, ICDs, and CRT-Ds in a nationwide Dutch cohort (2018-2020)

2023· article· en· W4388594854 on OpenAlexaff
Michelle Samuel, M Van Der Stoel, Michiel Rienstra, Alexander H. Maass

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineIncidence (geometry)Implantable cardioverter-defibrillatorPoisson regressionLogistic regressionPopulationCohortSudden cardiac deathImplantCardiac resynchronization therapyInternal medicinePediatricsHeart failureSurgeryEjection fraction

Abstract

fetched live from OpenAlex

Abstract Background Subcutaneous implantable cardioverter-defibrillators (S-ICDs) have emerged as a safe and effective alternative to ICDs for the prevention of sudden cardiac death. Compared to ICDs, S-ICDs aim to reduce morbidities associated with the transvenous lead including lead dislocation and infection. Limited evidence exists for the utilization, patient selection, and complications of S-ICDs in current real-world clinical practice. Purpose Our aim was to compare the implantation rate, patient characteristics, and complications of patients who underwent S-ICD, ICD, and CRT-D implants and replacements in the Netherlands from 2018 to 2020. Methods The Netherlands Heart Registration includes all adult patients who underwent an ICD implant or replacement at 28 hospitals in the Netherlands (2018-2020). Annual crude, age-, and sex-standardized incidence rates were assessed with Poisson regression models with robust variances. Patient characteristics and complications were compared between ICD types with ANOVA tests. Determinants of complications were identified with multivariable logistic regression models. Results Of 17,080 patients who had a de novo ICD implant (n=10,317) or replacement (n=6,763) in the Netherlands (2018-2020), 1408 (8.2%) patients had an S-ICD, 9441 (55.3%) patients received an ICD only, and 6231 (36.5%) patients received a CRT-D. The incidence of de novo S-ICDs were stable over time, however the incidence of de novo ICD only and CRT-D implants decreased by 5% and 11%, respectively (p<0.05 for both; Figure 1). Patients who received S-ICDs were younger (53 years, IQR 41-62) than ICD (66 years, IQR 57-73) and CRT-D (70 years, IQR 62-75) patients (p<0.05). A greater proportion women had S-ICDs (25.0%) and CRT-Ds (26.7%) implanted compared to ICDs (19.9%) (p<0.05). Further, less atrial fibrillation patients had S-ICDs (21.6% vs ICD 30.9%, and CRT-D 32.4%). ICDs for primary prevention were implanted in 56.4%, 48.4%, and 81.2% of S-ICD, ICD, and CRT-D patients, respectively (p<0.05). The incidence of lead dislocations was less among S-ICD patients compared to ICD and CRT-D patients (p<0.001; Figure 2). There was no difference detected for other complications or long-term mortality (p>0.05 for all; Figure 2). Infections in S-ICD patients [aOR 0.2 (95% CI 0.1-0.9)] and in-hospital bleeding for CRT-D patients [aOR 0.4 (95% CI 0.2-0.9)] decreased among patients who underwent an ICD implant in recent years. In addition, advancing age was identified as a risk factor for in hospital bleeding in S-ICD patients [aOR 1.1 (95% CI 1.0-1.2)] and for pneumothorax in CRT-D patients [aOR 3.6 (95% CI 2.0-6.8)]. Conclusion In real-world practice, S-ICDs were implanted in younger patients with fewer comorbidities and portended a lower risk of lead dislodgement than ICDs and CRT-Ds; however, the population-level implantation rate of S-ICDs did not vary between 2018 to 2020 in the Netherlands.

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)0.002
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.347
Teacher spread0.270 · 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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Citations0
Published2023
Admission routes1
Has abstractyes

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