Trends in implantable cardioverter-defibrillator programming practices and its impact on therapies: Insights from a North American Remote Monitoring Registry 2007–2018
Notice bibliographique
Résumé
BackgroundRecent evidence has revealed the utility of prolonged arrhythmia detection duration and increased rate cutoff to reduce implantable cardioverter-defibrillator (ICD) therapies. Data on real-world trends in ICD programming and its impact on outcomes are limited.ObjectiveThe purpose of this study was to evaluate trends in ICD programming and its impact on ICD therapy using a large remote monitoring database.MethodsA retrospective analysis of patients with ICD implanted from 2007 to 2018 was conducted using the de-identified Medtronic CareLink database. Data on ICD programming (number of intervals to detection [NID] and therapy rate cutoff) and delivered ICD therapies were collected.ResultsAmong 210,810 patients, the proportion programmed to a rate cutoff of ≥188 beats/min increased from 41% to 49% and an NID of ≥30/40 increased from 17% to 67% before May 2013 vs after February 2016. Programming to a rate cutoff of ≥188 beats/min, a ventricular fibrillation (VF) NID of ≥30/40, or a combined rate cutoff of ≥188 beats/min and VF NID of ≥30/40 were associated with reductions in ICD therapy. The largest reductions in ICD therapy occurred when the combination of rate cutoff ≥ 188 beats/min and VF NID ≥ 30/40 was programmed (antitachycardia pacing: hazard ratio [HR] 0.35; 95% confidence interval [CI] 0.34–0.36; P < .001; shocks: HR 0.67; 95% CI 0.65–0.69; P < .001; and antitachycardia pacing/shocks: HR 0.43; 95% CI 0.42–0.44; P < .001).ConclusionDespite evidence supporting the use of prolonged detection duration and high rate cutoff, implementation of shock reduction programming strategies in real-world clinical practice has been modest. The use of evidence-based ICD programming is associated with reduced ICD shocks over long-term follow-up. Recent evidence has revealed the utility of prolonged arrhythmia detection duration and increased rate cutoff to reduce implantable cardioverter-defibrillator (ICD) therapies. Data on real-world trends in ICD programming and its impact on outcomes are limited. The purpose of this study was to evaluate trends in ICD programming and its impact on ICD therapy using a large remote monitoring database. A retrospective analysis of patients with ICD implanted from 2007 to 2018 was conducted using the de-identified Medtronic CareLink database. Data on ICD programming (number of intervals to detection [NID] and therapy rate cutoff) and delivered ICD therapies were collected. Among 210,810 patients, the proportion programmed to a rate cutoff of ≥188 beats/min increased from 41% to 49% and an NID of ≥30/40 increased from 17% to 67% before May 2013 vs after February 2016. Programming to a rate cutoff of ≥188 beats/min, a ventricular fibrillation (VF) NID of ≥30/40, or a combined rate cutoff of ≥188 beats/min and VF NID of ≥30/40 were associated with reductions in ICD therapy. The largest reductions in ICD therapy occurred when the combination of rate cutoff ≥ 188 beats/min and VF NID ≥ 30/40 was programmed (antitachycardia pacing: hazard ratio [HR] 0.35; 95% confidence interval [CI] 0.34–0.36; P < .001; shocks: HR 0.67; 95% CI 0.65–0.69; P < .001; and antitachycardia pacing/shocks: HR 0.43; 95% CI 0.42–0.44; P < .001). Despite evidence supporting the use of prolonged detection duration and high rate cutoff, implementation of shock reduction programming strategies in real-world clinical practice has been modest. The use of evidence-based ICD programming is associated with reduced ICD shocks over long-term follow-up.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».