A strategy to personalize a 1D pulse wave propagation model for estimating subject-specific central aortic pressure waveform. (July 2022)

• Record Type: Journal Article
• Authors: Hao, Liling; Zhang, Qi; Liu, Jun; Wang, Zhuo; Xu, Lisheng; van de Vosse, Frans N.
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: Central aortic pressure -- Transfer function -- Wave propagation model -- Sensitivity analysis -- Screening method of Morris -- Adaptive sparse generalized polynomial chaos expansion; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105528
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The central aortic pressure (CAP) provides insights into the prediction, prevention, diagnosis, and treatment of cardiovascular disease, but can't be directly measured non-invasively. Therefore, the development of a noninvasive CAP estimation method based on the non-invasively measured peripheral pressure waveform is critical for clinical decisions based on the CAP. Some existing widely applied methods, such as the generalized transfer function (GTF) method relating measured peripheral pressure to the CAP, do not or only partly account for inter-subject or intra-subject variability of the cardiovascular system. To overcome this pitfall, we propose a subject-specific central aortic pressure estimation method in this paper. The novel method presented can derive an accurate aortic pressure from the peripheral pressure based on an individualized pulse wave propagation model using a GTF method as a first guess. To develop a strategy to personalize a pulse wave propagation model one usually needs to optimize many input parameters. Therefore, we present a two-step approach with the screening method of Morris and the adaptive sparse generalized polynomial chaos expansion (agPCE) algorithm for the sensitivity analysis of the wave propagation model. First, for a-priori defined output of the model, a subset of important parameters is identified using the screening method of Morris. Next, a quantitative variance-based sensitivity analysis is performed using agPCE. This approach is applied to a 1D pulse wave propagation model to get the personalized parameters of the pulse wave propagation model for the estimation of a subject-specific central aortic pressure waveform and is validated with 26 patients. Compared with the GTF method, the proposed method showed better performance in estimating the central aortic pulse wave and predicting the parameters. Highlights: A strategy to personalize a 1D pulse wave propagation model based on sensitivity analysis for estimating subject-specific central aortic pressure waveform. A two-step approach for the sensitivity analysis of models with many model parameters. The proposed method is able to reliably and accurately estimate the aortic pulse wave using the brachial pulse wave.