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Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling

Title
Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling
Authors
Kim, DaraeChae, DongwooShim, Chi YoungCho, In-JeongHong, Geu-RuPark, KyungsooHa, Jong-Won
Ewha Authors
조인정
Issue Date
2019
Journal Title
JOURNAL OF CLINICAL MEDICINE
ISSN
2077-0383JCR Link
Citation
JOURNAL OF CLINICAL MEDICINE vol. 8, no. 9
Keywords
bicuspid aortic valveprogressionmathematical model
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
We aimed to develop a mathematical model to predict the progression of aortic stenosis (AS) and aortic dilatation (AD) in bicuspid aortic valve patients. Bicuspid AS patients who underwent at least two serial echocardiograms from 2005 to 2017 were enrolled. Mathematical modeling was undertaken to assess (1) the non-linearity associated with the disease progression and (2) the importance of first visit echocardiogram in predicting the overall prognosis. Models were trained in 126 patients and validated in an additional cohort of 43 patients. AS was best described by a logistic function of time. Patients who showed an increase in mean pressure gradient (MPG) at their first visit relative to baseline (denoted as rapid progressors) showed a significantly faster disease progression overall. The core model parameter reflecting the rate of disease progression, alpha, was 0.012/month in the rapid progressors and 0.0032/month in the slow progressors (p < 0.0001). AD progression was best described by a simple linear function, with an increment rate of 0.019 mm/month. Validation of models in a separate prospective cohort yielded comparable R squared statistics for predicted outcomes. Our novel disease progression model for bicuspid AS significantly increased prediction power by including subsequent follow-up visit information rather than baseline information alone.
DOI
10.3390/jcm8091302
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의료원 > 의료원 > Journal papers
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