M. Taconné, V. Rolle, K. Owashi, V. Panis, A. Hubert, V. Auffret, E. Galli, Alfredo I. Hernández, E. Donal
{"title":"Sensitivity Analysis and Parameter Identification of a Cardiovascular Model in Aortic Stenosis","authors":"M. Taconné, V. Rolle, K. Owashi, V. Panis, A. Hubert, V. Auffret, E. Galli, Alfredo I. Hernández, E. Donal","doi":"10.23919/cinc53138.2021.9662851","DOIUrl":null,"url":null,"abstract":"The objective of this study is to propose a model-based method, adapted to patients with severe aortic stenosis (AS), in order to reproduce left ventricle (LV) pressure and volume from patient specific data. A formal sensitivity analysis is proposed, focused on left ventricle volume and pressure. The most influent parameters of this analysis are then selected to be identified in a parameter identification strategy and provide a patient specific pressure curve. This was implemented on 3 AS patients and a close match was observed between experimental and simulated pressure and volume curves. The global root mean square error (RMSE) for pressure and volume curves are respectively 21.8 $(\\pm 1.8)$ mmHg and 14.8 $(\\pm 9.4)ml$,. The model-based approach proposed shows promising results to generate accurate LV pressure and volume in AS case.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"60 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Computing in Cardiology (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/cinc53138.2021.9662851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The objective of this study is to propose a model-based method, adapted to patients with severe aortic stenosis (AS), in order to reproduce left ventricle (LV) pressure and volume from patient specific data. A formal sensitivity analysis is proposed, focused on left ventricle volume and pressure. The most influent parameters of this analysis are then selected to be identified in a parameter identification strategy and provide a patient specific pressure curve. This was implemented on 3 AS patients and a close match was observed between experimental and simulated pressure and volume curves. The global root mean square error (RMSE) for pressure and volume curves are respectively 21.8 $(\pm 1.8)$ mmHg and 14.8 $(\pm 9.4)ml$,. The model-based approach proposed shows promising results to generate accurate LV pressure and volume in AS case.