A novel algorithm to assess risk of heart failure exacerbation using ICD diagnostics: Validation from RAFT
Notice bibliographique
Résumé
BackgroundThe integrated diagnostics (ID) algorithm is an implantable device-based tool that collates data pertaining to heart rhythm, heart rate, intrathoracic fluid status, and activity, producing a risk score that correlates with 30-day risk of heart failure (HF) hospitalization.ObjectiveWe sought to validate the ID algorithm using the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial.MethodsDiagnostic measures of the algorithm include OptiVol fluid index, nighttime heart rate, minutes of patient activity, heart rate variability, and combined measure of cardiac rhythm and biventricular pacing. Monthly evaluations of ID parameters were assessed for the development of HF symptoms and hospitalization for HF.ResultsA total of 1224 patients were included: 741 (61%) with cardiac resynchronization therapy with defibrillator devices and 483 (39%) with implanted cardioverter-defibrillator only. The mean age was 66 ± 9 years, and 1013 (83%) were men. A total of 37,861 months of follow-up data were available, with 258 HF hospitalizations (event rate 0.68% per month). There were 33 HF hospitalizations during low-risk months (0.21% per month), 123 during medium-risk months (0.66% per month), and 102 during high-risk months (2.61% per month). Compared with low-risk months, and 95% confidence intervals) of HF hospitalizations during medium-risk months was 2.9 (2.0–4.4) and during high-risk months was 10.7 (6.9–16.6). Multivariable analysis demonstrated that each ID variable had independent association with HF hospitalization.ConclusionThe risk of HF as determined by the ID algorithm correlated with HF hospitalization and several HF signs and symptoms among patients in the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. This may present a useful adjunct to detect early signs of HF and adjust therapy to reduce morbidity and costs involved with hospital admission. The integrated diagnostics (ID) algorithm is an implantable device-based tool that collates data pertaining to heart rhythm, heart rate, intrathoracic fluid status, and activity, producing a risk score that correlates with 30-day risk of heart failure (HF) hospitalization. We sought to validate the ID algorithm using the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. Diagnostic measures of the algorithm include OptiVol fluid index, nighttime heart rate, minutes of patient activity, heart rate variability, and combined measure of cardiac rhythm and biventricular pacing. Monthly evaluations of ID parameters were assessed for the development of HF symptoms and hospitalization for HF. A total of 1224 patients were included: 741 (61%) with cardiac resynchronization therapy with defibrillator devices and 483 (39%) with implanted cardioverter-defibrillator only. The mean age was 66 ± 9 years, and 1013 (83%) were men. A total of 37,861 months of follow-up data were available, with 258 HF hospitalizations (event rate 0.68% per month). There were 33 HF hospitalizations during low-risk months (0.21% per month), 123 during medium-risk months (0.66% per month), and 102 during high-risk months (2.61% per month). Compared with low-risk months, and 95% confidence intervals) of HF hospitalizations during medium-risk months was 2.9 (2.0–4.4) and during high-risk months was 10.7 (6.9–16.6). Multivariable analysis demonstrated that each ID variable had independent association with HF hospitalization. The risk of HF as determined by the ID algorithm correlated with HF hospitalization and several HF signs and symptoms among patients in the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. This may present a useful adjunct to detect early signs of HF and adjust therapy to reduce morbidity and costs involved with hospital admission.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».