ReviewJournal of clinical medicine2024
Cardiac Geometry and Function in Patients with Reflex Syncope.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reflex syncope (RS) is the most prevalent form of syncope, yet its pathophysiology and clinical presentation are not well understood. Despite controversy, the 'ventricular theory' remains the most plausible hypothesis to explain RS in susceptible patients. Certain assumptions regarding the geometry and function of the heart are essential in supporting this theory. Given these considerations, the goal of this review was to try to integrate data on heart morphology and function in a phenotype of a patient susceptible to RS. Previous research suggests that a small left ventricle and atria, in addition to a normo- or hypercontractile myocardium, predispose to more syncopal events. These findings have been confirmed in different subsets of patients, including those with small heart and chronic fatigue syndrome, highlighting common pathophysiologic pathways in these subgroups of population. Heart geometry and function seem to play a role in different treatment strategies for RS patients, including the administration of medications, pacing, and possibly cardioneural ablation. In addition, parameters related to the geometry of the heart chambers and of the electrical activation of the heart seem to have predictive value for syncope recurrence. These parameters could be included in the future and improve the accuracy of predictive models for RS.
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