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

  • 주제(키워드) bicuspid aortic valve , progression , mathematical model
  • 주제(기타) Medicine, General & Internal
  • 설명문(일반) [Kim, Darae] Sungkyunkwan Univ, Samsung Med Ctr, Dept Med, Div Cardiol,Sch Med, Seoul 03722, South Korea; [Chae, Dongwoo; Park, Kyungsoo] Yonsei Univ, Dept Pharmacol, Div Pharmacometr, Coll Med, Seoul 03722, South Korea; [Shim, Chi Young; Hong, Geu-Ru; Ha, Jong-Won] Yonsei Univ, Severance Cardiovasc Hosp, Cardiol Div, Coll Med, Seoul 03722, South Korea; [Cho, In-Jeong] Ewha Womans Univ, Cardiol Dept, Med Ctr, Seoul 03722, South Korea
  • 등재 SCIE, SCOPUS
  • OA유형 Green Published, gold
  • 발행기관 MDPI
  • 발행년도 2019
  • 총서유형 Journal
  • URI http://www.dcollection.net/handler/ewha/000000171944
  • 본문언어 영어
  • Published As https://dx.doi.org/10.3390/jcm8091302
  • PubMed https://pubmed.ncbi.nlm.nih.gov/31450580

초록/요약

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.

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