546 Machine-learning based exploration of echocardiographic patterns and clinical parameters to understand their relation to death or transplant in pediatric dilated cardiomyopathy
Notice bibliographique
Résumé
Abstract Background Pediatric dilated cardiomyopathy (DCM) affects left ventricular (LV) function and carries a high risk of death or heart transplantation. However, the relation of LV regional function and inefficiency to clinical outcomes is underexplored. Purpose The aim of this study was to understand the relationship of regional LV mechanics, global LV function and clinical characteristics to the outcomes of death or heart transplant in children with DCM; through the integration of a vast amount of information enabled by unsupervised machine learning techniques. Methods DCM was defined by a LV end-diastolic dimension z-score > 2 and LV ejection fraction (EF) <55%. Longitudinal strain curves were sampled at 6 LV lateral wall and septal locations from the 4ch apical view. In addition, we analyzed other echo parameters including the aortic outflow pattern as a measure of LV pump function, QRS duration, LV EF, indexed end-diastolic LV dimension, global longitudinal strain and patient characteristics including age, weight, body surface area and medications (diuretics, ACE inhibitor, beta-blockers, mineralocorticoid receptor antagonist, digoxin, inotropes, antiarrhythmics). We used an unsupervised machine learning algorithm (multiple kernel learning) to reduce the dimensionality of these data, and position patients based on similarities. We subsequently used k-means clustering to recover homogeneous groups of patients. We then interpreted the data patterns associated to each of the groups for the occurrence of death or transplant through non-linear regression analysis (multi-scale kernel regression). Results 50 children with DCM (age 0 to 18 years) were analyzed. Clustering on the two first dimensions of the low-dimensional space resulted in three clusters (Figure A), with significantly different proportions of the composite outcome of death or heart transplant (Cl1 = 79%, Cl2 = 50%, Cl3 = 20%; p = 0.01). The group with the highest proportion of death or transplant (cluster 1) comprised the oldest and most frequently medicated subjects, with impaired LVEF and GLS, and with the widest QRS duration (p < 0.01) (Figure B). The group with the second highest proportion of death or transplant (cluster 2) comprised patients with the lowest LVEF (p < 0.01) and GLS (p < 0.001), reduced and delayed peak aortic outflow velocity and severely impaired basal and apical LV strain (Figure C). In contrast, the group with highest transplant-free survival (cluster 3) had the highest LVEF and GLS values, the most synchronous LV contraction as assessed by strain and QRS duration and the highest amplitude and earliest peaking aortic flow. Conclusion Our results serve as a proof-of-concept that machine-learning based approaches can be useful to explore and understand which regional and global echo parameters in combination with clinical parameters are associated with a higher risk of death or transplant in pediatric DCM. Abstract 546 Figure
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 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 | 0,000 |
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 ».