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)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».