Estimated pulse wave velocity (ePWV) as a potential gatekeeper for MRI-assessed PWV
| Autor: | Max J P van Hout, Martin J. Schalij, Arthur J.H.A. Scholte, Hildo J. Lamb, Renée de Mutsert, Jos J.M. Westenberg, Frits R. Rosendaal, J. Wouter Jukema, Sebastiaan C. Boone, Ralph L. Widya, Ling Lin, Ilona A. Dekkers |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male 030204 cardiovascular system & hematology Pulse Wave Analysis 03 medical and health sciences 0302 clinical medicine Vascular Stiffness Magnetic resonance imaging Discriminative model Predictive Value of Tests Statistics Range (statistics) Medicine Humans Radiology Nuclear Medicine and imaging 030212 general & internal medicine Obesity Pulse wave velocity Aged Netherlands Artificial neural network business.industry Regression analysis Anthropometry Middle Aged Regression Epidemiology of obesity Prediction modelling Female Neural Networks Computer Cardiology and Cardiovascular Medicine business |
| Zdroj: | International Journal of Cardiovascular Imaging, 38, 183-193. SPRINGER |
| DOI: | 10.1007/s10554-021-02359-0 |
| Popis: | Pulse wave velocity (PWV) assessed by magnetic resonance imaging (MRI) is a prognostic marker for cardiovascular events. Prediction modelling could enable indirect PWV assessment based on clinical and anthropometric data. The aim was to calculate estimated-PWV (ePWV) based on clinical and anthropometric measures using linear ridge regression as well as a Deep Neural Network (DNN) and to determine the cut-off which provides optimal discriminative performance between lower and higher PWV values. In total 2254 participants from the Netherlands Epidemiology of Obesity study were included (age 45–65 years, 51% male). Both a basic and expanded prediction model were developed. PWV was estimated using linear ridge regression and DNN. External validation was performed in 114 participants (age 30–70 years, 54% female). Performance was compared between models and estimation accuracy was evaluated by ROC-curves. A cut-off for optimal discriminative performance was determined using Youden’s index. The basic ridge regression model provided an adjusted R2 of 0.33 and bias of 2: 0.29). All models showed good discriminative performance for PWV |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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