105 Differences in myocardial mechanics between ischaemic and non-ischaemic cardiomyopathy assessed by cmr: a sub-group analysis of the vindicate trial
Notice bibliographique
Résumé
Background Prognosis and treatment of patients with chronic heart failure (CHF) differs according to whether it is ischaemic (ICM) or non-ischaemic cardiomyopathy (NICM). Multi-parametric cardiovascular magnetic resonance (CMR) can distinguish these aetiologies; strain imaging however may confer incremental diagnostic and prognostic information over left ventricular ejection fraction (LVEF). We hypothesised in a prospectively recruited sample of CHF patients, ICM and NICM have different myocardial strain patterns. Methods The VINDICATE trial investigated efficacy of high dose vitamin D in patients with CHF. A subgroup of the trial underwent CMR, blood and cardiopulmonary exercise tests at baseline. 53 patients (31 ICM, 22 NICM) underwent identical 3.0T CMR protocols (Achieva, Philips Healthcare, Best, The Netherlands). Tissue tagging by spatial modulation of magnetization (SPAMM) (spatial resolution 1.51 × 1.57×10mm 3 , tag separation 7 mm, 18 phases, typical TR/TE 5.8/3.5 ms, flip angle 10°, typical temporal resolution 55 ms) was acquired in short axis slices acquired at the apex, mid-ventricle, and base. Late gadolinium enhancement (LGE) was performed 15 min following administration of 0.15 mmol/kg gadolinium DTPA. CMR data were analysed quantitatively using commercially available software (CVI42, Circle Cardiovascular Imaging Inc. Calgary, Canada and inTag v1.0, CREATIS lab, Lyon, France). Endocardial and epicardial contours were drawn on SPAMM sequences using a semi-automated process. Peak circumferential LV strain (E cc ) was measured at apex, mid-ventricle, and base. LV twist was calculated by subtracting basal from apical rotation. Torsion was determined by: Torsion = Peak Twist x (Apical Radius+Basal Radius)/2xApex to Base length Results The two groups were comparable for baseline demographics (Table 1). The ICM group had significantly more prior revascularisation (CABG/PCI). There was no significant difference between the 2 groups in both LV dimensions and LVEF, however ICM had significantly more LGE (Table 2). There was no significant difference between the 2 groups in E cc . NICM patients had significantly lower LV twist and torsion compared to the ICM group 6.0±3.68° vs 8.8±4.32° p=0.020 and 6.3±3.79° vs 8.8±4.69° p=0.048 respectively. Conclusion Despite similar EF and E cc , patients with NICM had significantly less LV torsion than ICM. Myocyte dysfunction in ICM is more sub-endocardial due to the wave-front of ischaemia and more global in NICM. Relative perseveration of LV torsion of ICM over NICM is likely a result of sparing of sub-epicardial fibres and an increased compensatory recruitment of sub-epicardial fibres that are predominantly responsible for LV torsion. Recognition of different torsion patterns of ICM and NICM gives insight into aetiology of CHF, which may assist patient diagnosis and management, especially in those unable to have contrast agents. Abstract 105 Table 1 Demographics ICM NICM P-value Age, years 65.2±15.9 59.0±16.9 0.182 Sex (female)% 29 36 0.582 BMI kg/m 2 26.9±3.9 27.6±5.6 0.654 SBP, mmHg 119±21 115±18 0.399 DBP mmHg 70±11 72±11 0.601 Diabetes Mellitus,% 19 4.5 0.195 CABG,% 32 0 <jats:
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 | 0,001 | 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 ».