Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

Akkoyun E., Kwon S. T., Acar A. C., Lee W., Baek S.

COMPUTERS IN BIOLOGY AND MEDICINE, vol.117, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 117
  • Publication Date: 2020
  • Doi Number: 10.1016/j.compbiomed.2020.103620
  • Journal Name: COMPUTERS IN BIOLOGY AND MEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Library, Information Science & Technology Abstracts (LISTA)
  • Keywords: Abdominal aortic aneurysm, Probabilistic programming, Bayesian inference, Rupture risk assessment, Patient-oriented prediction model, CALIBRATION, RUPTURE, SURVEILLANCE, VALIDATION, THROMBUS, IMPACT, RISK, PATH
  • Middle East Technical University Affiliated: Yes

Abstract

Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.