Novel ventricular tachyarrhythmia detection enhancement detects undertreated life-threatening arrhythmias
Notice bibliographique
Résumé
BackgroundVentricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter-defibrillator algorithms.ObjectiveDevelop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high-voltage therapy (HVT) for these arrhythmias.MethodsArrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott’s Merlin.net database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT >30 seconds earlier than without VFTA, were readjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA did not activate or alter HVT were not adjudicated.ResultsAmong 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% of the episodes (56/86) were ventricular fibrillation, were polymorphic, did not self-terminate during the recording window, or were not amenable antitachycardia pacing. Of those, 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices.ConclusionThe VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, owing to undersensed ventricular signals resulting in the rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT. Ventricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter-defibrillator algorithms. Develop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high-voltage therapy (HVT) for these arrhythmias. Arrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott’s Merlin.net database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT >30 seconds earlier than without VFTA, were readjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA did not activate or alter HVT were not adjudicated. Among 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% of the episodes (56/86) were ventricular fibrillation, were polymorphic, did not self-terminate during the recording window, or were not amenable antitachycardia pacing. Of those, 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices. The VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, owing to undersensed ventricular signals resulting in the rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT.
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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 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 ».