Functional Mixed-Effects Modeling of Longitudinal Duchenne Muscular Dystrophy Electrical Impedance Myography Data Using State-Space Approach

Objective: Electrical impedance myography (EIM) is a quantitative and objective tool to evaluate muscle status. EIM offers the possibility to replace conventional physical functioning scores or quality of life measures, which depend on patient cooperation and mood. Methods: Here, we propose a functi...

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Veröffentlicht in:IEEE transactions on biomedical engineering Jg. 66; H. 6; S. 1761 - 1768
Hauptverfasser: Kapur, Kush, Sanchez, Benjamin, Pacheck, Adam, Darras, Basil, Rutkove, Seward B., Selukar, Rajesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bayesian analysis
Biomarkers
Biomedical measurement
Child
Clinical trials
Confidence
Covariance matrix
Data models
Deltoid Muscle - physiology
Deltoid Muscle - physiopathology
Diseases
Duchenne's muscular dystrophy
Dystrophy
Electric Impedance
Electrical impedance
electrical impedance myography
Electromyography - methods
Functional data
Humans
Image Processing, Computer-Assisted - methods
Impedance
Kalman filtering
Kalman filters
Male
Medical research
mixed-effects models
Modelling
Mood
Muscles
Muscular dystrophy
Muscular Dystrophy, Duchenne - diagnosis
Muscular Dystrophy, Duchenne - physiopathology
Quality of life
Smoothing
Smoothing methods
smoothing splines
Splines (mathematics)
Trajectory measurement
ISSN:0018-9294, 1558-2531, 1558-2531
Online-Zugang:Volltext
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Zusammenfassung:Objective: Electrical impedance myography (EIM) is a quantitative and objective tool to evaluate muscle status. EIM offers the possibility to replace conventional physical functioning scores or quality of life measures, which depend on patient cooperation and mood. Methods: Here, we propose a functional mixed-effects model using a state-space approach to describe the response trajectories of EIM data measured on 16 boys with Duchenne muscular dystrophy and 12 healthy controls, both groups measured over a period of two years. The modeling framework presented imposes a smoothing spline structure on EIM data collected at each visit and taking into account of within subject correlations of these curves along the longitudinal measurements. The modeling framework is recast in a state-space approach, thereby allowing for the employment of computationally efficient diffuse Kalman filtering and smoothing algorithms for the model estimation, as well as the estimates of the posterior variance-covariance matrix for the construction of the Bayesian 95% confidence bands. Results: The proposed model allows us to simultaneously adjust for baseline variables, differentiate the longitudinal changes in the smooth functional response and estimate the subject and subject-time specific deviations from the population-averaged response curves. The code is made publicly available in the supplementary material. Significance: The modeling approach presented will potentially enhance EIM capability to serve as a biomarker for testing therapeutic efficacy in DMD and other clinical trials.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2879227