4-dimensional strain imaging of the right ventricle: application in patients with severe pulmonary hypertension
Notice bibliographique
Résumé
The risk stratification of patients with pulmonary hypertension (PHTN) using non-invasive techniques is challenging. 4D analysis techniques of right ventricular (RV) strain may provide unique opportunities for the identification of high-risk individuals. However, conventional strain metrics, developed for left ventricular geometry, pose limitations for RV geometry and its associated fibre orientations. In this study we examine a novel 4D strain analysis tool affording calculation of axis-independent Principal strains for the assessment of right ventricular mechanics. Patients with mild-moderate and severe PHTN were studied in comparison to a young healthy volunteer cohort. Fifteen patients with PHTN, defined as a mean pulmonary arterial pressure (PAP) >25mmHg at right heart catheterization, and 18 young healthy volunteers were studied. Severe PHTN was defined as a mean PAP ≥55mmHg. A standardized imaging protocol was performed using a 3T MRI scanner. Cine images were blindly analyzed for LV and RV volumes using commercial software (cvi42, Circle Cardiovascular Inc., Calgary). 4D strain analysis was performed using prototype software, generating a 3D end-diastolic mesh model of the RV and then deforming this model within a 4D displacement field, the latter contributed to by short and long-axis cine image data (Figure 1 ). Peak Principal Strain Amplitude (PPSA) and Normalized Time to Peak (NTTP) strain were obtained for each model node and a novel metric calculated called the "Normalized Strain Attainment Rate" (NorStAR), where NorStAR = PPSA / NNTP (Figure 1 ). The latter composite metric was hypothesized to represent global RV electro-mechanical recruitment. Analysis of right ventricle (RV) myocardial strain. (A) The 3D-distribution of Maximum Principal Strain (%) across the RV wall at end-diastole and end-systole. (B) Illustration of the NorStAR calculation as the ratio between the Peak Principal Strain Amplitude (PPSA) and the Normalized Time To Peak (NTTP; as a percentage of the cardiac cycle). The mean age of the PHTN cohort and healthy volunteers was 62.5±6.4yrs and 26.6±5.8yrs respectively. Among those with mild-moderate PHTN (n=10) the mean PAP was 39.2±9.4 (n=10) versus 62.4±9.0mmHg among those with severe PHTN (n=5). As shown in Figure 2 a significant difference in NorStAR was identified between severe PHTN and healthy controls (-30±10 vs -47±9, p=0.002), while no difference was seen for mild-moderate PHTN (-44±10 vs 47±9, P=N.S.). The capacity of NorStAR to identify patients with invasive hemodynamics of severe PHTN was incrementally assessed using ROC analysis. This achieved an AUC of 0.85 for the identification of severe PHTN with a threshold of -38 providing optimal sensitivity (80%) and specificity (60%). Mean values of the NorStAR strain parameter. NorStAR in healthy volunteers, mild-moderate patients, and patients with severe pulmonary hypertension, indicating a significant impairment in the severe group (* p<0.01). In this pilot study, we demonstrate the feasibility of 4D Strain analysis using Principal Strains for the characterization of RV mechanical deformation in PHTN. A novel marker, NorStAR, accurately identified patients with invasive hemodynamic evidence of severe versus non-severe PHTN and may be of value for the risk stratification of this population.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».