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

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish
Article number9130843
Pages (from-to)237-246
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Multi-state markov model
  • high blood pressure
  • hypertension
  • proactive prevention
  • transition probability

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