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Enregistrement W4385456956 · doi:10.31083/j.rcm2408217

Assessing Myocardial Strain and Myocardial Work as a Marker for Hypertensive Heart Disease: A Meta-Analysis

2023· review· en· W4385456956 sur OpenAlexaff
Simon W. Rabkin

Notice bibliographique

RevueReviews in Cardiovascular Medicine · 2023
Typereview
Langueen
DomaineMedicine
ThématiqueCardiovascular Function and Risk Factors
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineCardiologyInternal medicineLeft ventricular hypertrophyHypertensive heart diseaseSpeckle tracking echocardiographyMeta-analysisStrain (injury)Blood pressureHeart failure

Résumé

récupéré en direct d'OpenAlex

Background: The main objective of this study was to determine whether myocardial strain and myocardial work are altered in hypertension and whether the strain is independent of hypertension-induced left ventricular hypertrophy. Methods: Two systematic literature searches were conducted using Medline and EMBASE through to June 30, 2022. In the first, search terms left ventricular strain or speckle tracking AND hypertension and left ventricular hypertrophy were used in conjunction with Boolean operators to identify articles reporting left ventricular strain in patients with hypertension. In the second, the terms Global cardiac or myocardial work AND hypertension were used to identify articles. Publication bias was assessed by examination of funnel plots and calculation of the Failsafe N and Duval and Tweedie’s Trim and fill. The results were presented as Forrest plots. Results: Global longitudinal strain (GLS) was significantly lower in patients with hypertension compared to those without hypertension with a mean difference of 2.0 ± 0.1 (standard error of mean(SEM)) in the fixed effect model. Global circumferential strain (GCS) was significantly lower in hypertension. The mean difference between the hypertensive and non-hypertensive groups was 1.37 ± 0.17. Global radial strain (GRS) was significantly (p < 0.05) greater in hypertension. However, this difference was significant in only 3 and of borderline significance in 3 of 14 studies where GRS was measured. The mean difference between the hypertensive and non-hypertensive groups was 1.5 ± 0.5 using the fixed effects model. There was a significant relationship between GLS and GCS as well as between GCS and GRS but no significant relationship between GLS and GRS. There was no significant difference in left ventricular ejection fraction (LVEF) between the hypertension and no hypertension groups. There was no significant relationship between LVEF and either GLS or GCS but a significant negative correlation was found between LVEF and GRS. GLS was further reduced in persons with hypertension and left ventricular hypertrophy (LVH) compared to hypertension without LVH. In contrast, there were no or minimal differences in GCS and GRS for individuals with hypertension and LVH compared to those without LVH. Global myocardial work index (GWI) and Global constructive work (GCW) were significantly greater in patients with hypertension compared to controls. Global wasted work (GWW) indicated significantly less wasted work in controls compared to hypertension. In contrast, Global work efficiency (GWE) was significantly lower in hypertension compared to the control. Conclusions: There was a significant reduction in GLS and GCS in hypertension while GRS was increased. The reduction in GLS in hypertension was not dependent on the presence of LVH. GLS was further reduced in persons with hypertension when LVH was present. In contrast, there were no or minimal differences in GCS and GRS for individuals with LVH compared to those without LVH. GLS was independent of left ventricle (LV) ejection fraction. GWI, GCW and GWW were greater in hypertension while GWE was lower in hypertension compared to controls. These data support the contention that GLS and indices of global work are early markers of hypertensive heart disease.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesMéta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,515
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0120,005
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0340,057
Bibliométrie0,0020,005
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,189
Tête enseignante GPT0,396
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2023
Routes d'admission1
Résumé présentoui

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