Abstract
More than a quarter of all Americans are estimated to have multiple chronic conditions (MCC). It is known that shared modifiable lifestyle behaviors account for many common MCC. What is not precisely known is the dynamic effect of changes in lifestyle behaviors on the trajectories of MCC emergence. This paper proposes dynamic functional continuous time Bayesian networks to effectively formulate the dynamic effect of patients' modifiable lifestyle behaviors and their interaction with non-modifiable demographics and preexisting conditions on the emergence of MCC. The proposed method considers the parameters of the conditional dependencies of MCC as a nonlinear state-space model and develops an extended Kalman filter to capture the dynamics of the modifiable risk factors on the MCC evolution. It also develops a tensor-based control chart based on the integration of multilinear principal component analysis and multivariate exponentially weighted moving average chart to monitor the effect of changes in the modifiable risk factors on the risk of new MCC. We validate the proposed method based on a combination of simulation and a real dataset of 385 patients from the Cameron County Hispanic Cohort. The dataset examines the emergence of 5 chronic conditions (Diabetes, Obesity, Cognitive Impairment, Hyperlipidemia, Hypertension) based on 4 modifiable lifestyle behaviors representing (Diet, Exercise, Smoking Habits, Drinking Habits) and 3 non-modifiable demographic risk factors (Age, Gender, Education). For the simulated study, the proposed algorithm shows a run-length of 4 samples (4 months) to identify behavioral changes with significant impacts on the risk of new MCC. For the real data study, the proposed algorithm shows a run-length of one sample (one year) to identify behavioral changes with significant impacts on the risk of new MCC. The results demonstrate the sensitivity of the proposed methodology for dynamic prediction and monitoring of the risk of MCC emergence in individual patients.
Highlights
The proposed methodology has the following contributions: 1) We propose to formulate the conditional dependencies of functional continuous time Bayesian network (FCTBN) as a non-linear state space model based on extended Kalman filter (EKF) to create a dynamic FCTBN (D-FCTBN) that captures the dynamics of modifiable lifestyle behavioral risk factors on the structure and parameters of the multiple chronic conditions (MCC) network
The proposed method first, utilizes a low-rank tensor decomposition method based on multilinear principal component analysis (MPCA) to extract main features of the Dynamic FCTBN (D-FCTBN) parameters which form a three-dimensional tensor, and develops a multivariate exponentially weighted moving average (MEWMA) control chart to monitor the reconstruction error
We hypothesize any out-of-control signal from the proposed Multivariate Exponential Weighted Moving Average (MEWMA) control chart, which is resulted from a large reconstruction error, accounts for a change in the patient’s modifiable risk factors that significantly change the risk of developing new MCC
Summary
T HE evolution of multiple chronic conditions (MCC) follows a complex stochastic process. This path of evolution is often influenced by several factors, including interrelationship of preexisting conditions, patient-level modifiable and non-modifiable risk factors [1]. MCCs are associated with 66% of the total healthcare costs in the United States, and approximately one in four Americans and 75% of Americans aged 65 years are burdened with MCC [2], [3]. What makes MCC one of the biggest challenges of the 21st century in healthcare [5], is the complex dynamic nature of MCC which is affected by the preexisting chronic conditions and non-modifiable demographic risk factors, such as age, gender, ethnicity, etc, and the modifiable lifestyle behavioral risk factors, such as diet, exercise, etc
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