Objective: The use of ergot-derived dopamine agonists (DA) to treat patients with prolactinomas has not been associated with an increased risk of significant heart valve dysfunction. Accordingly, the present study evaluated whether the long-term use of DA for hyperprolactinaemia may be associated with increased risk of significant valvular heart disease.
Methods: A total of 74 patients (mean age 48 ± 1·4 years, 23% male) with prolactinoma treated with DA for at least 1 year were evaluated with 2-dimensional echocardiography at baseline. After 2 years of follow-up, a repeat echocardiography was performed to evaluate significant changes in valvular heart structure (thickening, calcifications and leaflet motion abnormalities) and function (regurgitation or stenosis). Patients were classified according to treatment: patients treated with cabergoline (group 1: n = 45), and patients not treated with cabergoline (group 2: n = 29).
Results: At 2-year follow-up, no significant valvular stenosis was observed in any patient. In addition, the prevalence of any significant valve regurgitation did not change significantly (from 12% to 15%, P = NS). However, there was a significant increase in the prevalence of valvular calcifications (from 48% to 58%, P = 0·004) and, particularly, in the prevalence of aortic valve calcifications (from 39% to 53%, P = 0·002). In a per-treatment-based analysis, the group of patients treated with cabergoline had significantly higher prevalence of aortic valve calcification at 2 years follow-up as compared to the group of patients not treated with cabergoline (63%vs 38%, P = 0·016).
Conclusions: The long-term therapy with DA (cabergoline) of patients with prolactinoma is associated with an increased prevalence of valvular calcification. However, these structural changes were not accompanied by an increased prevalence of valvular dysfunction.
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Biomed Phys Eng Express
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Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
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Background: Psoriasis is an inflammatory disease primarily treated through molecular-targeted therapies. However, emerging evidence suggests that dietary interventions may also play a role in managing inflammation associated with this condition. The Mediterranean diet (MedDiet), prevalent in southern European countries, has been widely recognized for its ability to reduce cardiovascular mortality, largely due to its anti-inflammatory properties.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!