Prevalence and management of electrical lead abnormalities in cardiac implantable electronic device leads
Notice bibliographique
Résumé
BackgroundElectrical lead abnormalities (ELAs) can result in device malfunction, leading to significant morbidity in patients with cardiac implantable electronic devices (CIEDs).ObjectiveWe sought to determine the prevalence and management of ELAs in patients with CIEDs.MethodsThis was a retrospective cohort study of patients implanted with a CIED between 2012 and 2019 at a tertiary care center. The primary outcome was ELA defined as increased capture threshold (≥2× implantation value), decreased sensing (≤0.5 implantation value), change in impedance (>50% over 3 months), or nonphysiologic potentials. A secondary outcome of device clinic utilization was also collected.ResultsThere were 2996 unique patients (35% female) included with 4600 leads (57% Abbott, 43% Medtronic). ELAs were observed in 135 (3%) leads, including 124 (92%) Abbott and 10 (7%) Medtronic leads (hazard ratio 9.25, P < .001). Mean follow-up was 4.5 ± 2.2 years. ELAs were associated smaller lead French size, atrial location, and Abbott leads. Lead revision was required in 28% of cases. Patients with lead abnormalities had 38% more in-clinic visits per patient year of follow-up compared with those without (P < .001).ConclusionELAs were more frequent in certain models, which increased rates of revision and follow-up. Identification of factors that mitigate these abnormalities to improve lead performance are required to improve care for these devices and provide efficient healthcare. Electrical lead abnormalities (ELAs) can result in device malfunction, leading to significant morbidity in patients with cardiac implantable electronic devices (CIEDs). We sought to determine the prevalence and management of ELAs in patients with CIEDs. This was a retrospective cohort study of patients implanted with a CIED between 2012 and 2019 at a tertiary care center. The primary outcome was ELA defined as increased capture threshold (≥2× implantation value), decreased sensing (≤0.5 implantation value), change in impedance (>50% over 3 months), or nonphysiologic potentials. A secondary outcome of device clinic utilization was also collected. There were 2996 unique patients (35% female) included with 4600 leads (57% Abbott, 43% Medtronic). ELAs were observed in 135 (3%) leads, including 124 (92%) Abbott and 10 (7%) Medtronic leads (hazard ratio 9.25, P < .001). Mean follow-up was 4.5 ± 2.2 years. ELAs were associated smaller lead French size, atrial location, and Abbott leads. Lead revision was required in 28% of cases. Patients with lead abnormalities had 38% more in-clinic visits per patient year of follow-up compared with those without (P < .001). ELAs were more frequent in certain models, which increased rates of revision and follow-up. Identification of factors that mitigate these abnormalities to improve lead performance are required to improve care for these devices and provide efficient healthcare.
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 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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».