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Background: Fast progression of the transaortic mean gradient ( ) is relevant for clinical decision making of valve replacement in patients with moderate and severe aortic stenosis (AS) patients. However, there is currently little knowledge regarding the determinants affecting progression of transvalvular gradient in AS patients.
Methods: This monocentric retrospective study included consecutive patients presenting with at least two transthoracic echocardiography examinations covering a time interval of one year or more between April 2006 and February 2016 and diagnosed as moderate or severe aortic stenosis at the final echocardiographic examination. Laboratory parameters, medication, and prevalence of eight known cardiac comorbidities and risk factors (hypertension, diabetes, coronary heart disease, peripheral artery occlusive disease, cerebrovascular disease, renal dysfunction, body mass index ≥30 Kg/m, and history of smoking) were analyzed. Patients were divided into slow ( < 5 mmHg/year) or fast ( ≥ 5 mmHg/year) progression groups.
Results: A total of 402 patients (mean age 78 ± 9.4 years, 58% males) were included in the study. Mean follow-up duration was 3.4 ± 1.9 years. The average number of cardiac comorbidities and risk factors was 3.1 ± 1.6. Average number of cardiac comorbidities and risk factors was higher in patients in slow progression group than in fast progression group (3.3 ± 1.5 vs 2.9 ± 1.7; =0.036). Patients in slow progression group had more often coronary heart disease (49.2% vs 33.6%; =0.003) compared to patients in fast progression group. LDL-cholesterol values were lower in the slow progression group (100 ± 32.6 mg/dl vs 110.8 ± 36.6 mg/dl; =0.005).
Conclusion: These findings suggest that disease progression of aortic valve stenosis is faster in patients with fewer cardiac comorbidities and risk factors, especially if they do not have coronary heart disease. Further prospective studies are warranted to investigate the outcome of patients with slow versus fast progression of transvalvular gradient with regards to comorbidities and risk factors.
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http://dx.doi.org/10.1155/2018/3713897 | DOI Listing |
Med J Aust
September 2025
University of New South Wales, Sydney, NSW.
Anxiety disorders are the most prevalent mental illness in Australia and are more common in women relative to men, as well as transgender and gender diverse people relative to cisgender people. Sex and gender differences in anxiety prevalence are likely driven by a combination of factors including differential exposure to different types of stressors and trauma, gendered enculturation of different coping responses and perceived stigma of mental illness, differences in medical comorbidities, and differences in symptom presentations. The established impact of gonadal hormone changes on anxiety risk and symptom presentation across the female lifespan underscore the need for sex- and gender-responsive management of anxiety disorders.
View Article and Find Full Text PDFBrain Behav
September 2025
Department of Dermatology, Yulin First Hospital, Yulin, Shaanxi Province, China.
Background: Psoriasis is linked with an elevated risk of anxiety disorders, and there may be a temporal relationship between the two. However, the association between anxiety status and its duration with psoriasis is unclear.
Objectives: The present work aimed to figure out the association between anxiety and the risk of psoriasis.
Geroscience
September 2025
Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden.
To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).
View Article and Find Full Text PDFBariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
View Article and Find Full Text PDFJ Neural Transm (Vienna)
September 2025
Parkinson's Foundation Centre of Excellence, King's College Hospital, Denmark Hill, London, SE5 9RS, UK.
Parkinson's disease patients are at increased risk of road traffic and car accidents and those with excessive daytime sleepiness are specially susceptible. Abnormal scores on the Epworth Sleepiness Scale predicts risk for driving-related somnolence which may cause road traffic accidents in driving patients as many such patients declare dozing of while in a car. Our study estimates that over 40% of patients with daytime somnolence have risks of dozing off in a car.
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