Real-world performance of a fully automatic antitachycardia pacing algorithm
Notice bibliographique
Résumé
Abstract Background Antitachycardia pacing (ATP) found in most implantable cardioverter defibrillators (ICDs) is an important therapy for painless termination of ventricular tachycardias (VT). In ICDs programmed to guideline-recommended detection and therapy settings typical ATP success rates are 50-70%.[1-3] The first closed-loop, automated ATP (AATP) algorithm, introduced in 2021, applies an S1 train derived from the detected VT, followed by an S2 derived initially from a refractory period estimator and then adjusted using analysis of the post-pacing interval if ATP fails. The termination efficacy of AATP in the real-world population is unknown. Purpose To assess the effectiveness AATP in a large real-world population. Methods A database of de-identified ICD transmissions from subjects in the United States, Australia, Canada, and New Zealand was queried for AATP treated episodes. ICDs were randomly sampled, biased toward ICDs with multiple treated VT episodes. For rhythms classified as monomorphic ventricular tachycardia, each AATP pulse train was reviewed for: rhythm at delivery, effect of ATP, and any non-capturing pulses. Acceleration was defined as a post-ATP decrease in MVT cycle length (VTCL) of at least 10% and at least 30ms. Acceleration to polymorphic VT or ventricular fibrillation (PVT/VF) was assessed. Rates of termination and acceleration were calculated for all MVT and dichotomized using a 320ms VTCL cutoff. The method of generalized estimating equations (GEE) was used to correct event rates for multiple episodes from a single patient. Results There were 2281 episodes of MVT in 340 patients; 63% of patients had 2 or more episodes. There were 724 episodes with initial VTCL <320ms and 1557 episodes with VTCL ≥320ms. The rate of termination of MVT by AATP was 78% for all VTCL [95% CI: 74%-81%]. In 300 patients (88%) AATP terminated at least one MVT. For MVT with VTCL ≥320ms the termination rate was 80% [95% CI: 75%-83%] and for VTCL <320ms the termination rate was 75% [95% CI: 69%-80%]. The rate of acceleration was 3.6% [95% CI: 2.7%-4.9%], with a 0.8% rate of acceleration to PVT/VF [95% CI: 0.4%-1.8%]. Acceleration led to shock therapy in 63% of accelerated episodes. All MVT accelerated to PVT/VF either spontaneously terminated (4/14) or were successfully converted by a single shock therapy (10/14). All accelerations that remained MVT were successfully treated by the device with either subsequent ATP (35%) or shock therapy (61%), or spontaneously terminated (4%). Conclusion In this large, real-world cohort analysis, automated ATP’s termination rate exceeded the typical success rate of burst ATP, particularly for MVT <320ms. Acceleration to PVT/VF was low and effective first-shock defibrillation was consistent. Subsequent ATP for accelerated MVT had incremental benefit, leading to successful termination one-third of the time. Additional data are currently being prospectively collected in a separate post-approval study.
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| 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) | 0,002 | 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 ».