Literature DB >> 32749984

Blood Pressure States Transition Inference Based on Multi-State Markov Model.

Jingmei Yang, Feng Liu, Boyu Wang, Chaoyang Chen, Timothy Church, Lee Dukes, Jeffrey O Smith.   

Abstract

The investigation of risk factors associated with hypertension patients has been extensively studied in the past decades. However, the pattern of natural progressive trajectories to hypertension from nonhypertensive states was rarely explored. In this study, we are interested in discovering the underlying transition patterns between different blood pressure states, namely normal state, elevated state, and hypertensive state among the working population in the United States. A multi-state Markov model was built based on 88,966 clinical records from 34,719 participants we collected during the worksite preventive screening from 2012 to 2018. We first investigated the various risk factors, and we found that body mass index (BMI) is the most critical factor for developing new-onset hypertension. The transition probabilities, survival probabilities, and sojourn time of each state were derived given different levels of BMI, age groups, and gender categories. We found the underweight participants are more likely to remain in the current nonhypertensive states within 3 years, while extremely obese participants have a higher probability of developing hypertension. We discovered the distinct transition patterns among male and female participants. On average, the sojourn time in the normal state for normal-weight participants is 4.33 years for females and 2.18 years for their male counterparts. For the extremely obese participants, the average sojourn time in the normal state is 1.38 years for females and 0.71 years for males. In the end, a web-based graphical user interface (GUI) application was developed for clinicians to visualize the impact of behavioral interventions on delaying the progression of hypertension. Our analysis can provide a unique insight into hypertension research and proactive interventions.

Entities:  

Year:  2021        PMID: 32749984     DOI: 10.1109/JBHI.2020.3006217

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  2 in total

1.  Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.

Authors:  Jingmei Yang; Xinglong Ju; Feng Liu; Onur Asan; Timothy Church; Jeff Smith
Journal:  IEEE Open J Eng Med Biol       Date:  2021-10-06

2.  Multistate Markov model application for blood pressure transition among the Chinese elderly population: a quantitative longitudinal study.

Authors:  Xujuan Zheng; Juan Xiong; Yiqin Zhang; Liping Xu; Lina Zhou; Bin Zhao; Yuxin Wang
Journal:  BMJ Open       Date:  2022-07-14       Impact factor: 3.006

  2 in total

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