IJERPH, Vol. 17, Pages 1944: Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model

IJERPH, Vol. 17, Pages 1944: Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model:

IJERPH, Vol. 17, Pages 1944: Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model

International Journal of Environmental Research and Public Health doi: 10.3390/ijerph17061944

Authors:
Kung-Jeng Wang
Chia-Min Lee
Gwo-Chi Hu
Kung-Min Wang


Background: Most stroke cases lead to serious mental and physical disabilities, such as dementia and sensory impairment. Chronic diseases are contributory risk factors for stroke. However, few studies considered the transition behaviors of stroke to dementia associated with chronic diseases and environmental risks. Objective: This study aims to develop a prognosis model to address the issue of stroke transitioning to dementia associated with environmental risks. Design: This cohort study used the data from the National Health Insurance Research Database in Taiwan. Setting: Healthcare data were obtained from more than 25 million enrollees and covered over 99% of Taiwan’s entire population. Participants: In this study, 10,627 stroke patients diagnosed from 2000 to 2010 in Taiwan were surveyed. Methods: A Cox regression model and corresponding semi-Markov process were constructed to evaluate the influence of risk factors on stroke, corresponding dementia, and their transition behaviors. Main Outcome Measure: Relative risk and sojourn time were the main outcome measure. Results: Multivariate analysis showed that certain environmental risks, medication, and rehabilitation factors highly influenced the transition of stroke from a chronic disease to dementia. This study also highlighted the high-risk populations of stroke patients against the environmental risk factors; the males below 65 years old were the most sensitive population. Conclusion: Experiments showed that the proposed semi-Markovian model outperformed other benchmark diagnosis algorithms (i.e., linear regression, decision tree, random forest, and support vector machine), with a high R2 of 90%. The proposed model also facilitated an accurate prognosis on the transition time of stroke from chronic diseases to dementias against environmental risks and rehabilitation factors.