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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Mar 30, 2021
Date Accepted: Sep 24, 2021

The final, peer-reviewed published version of this preprint can be found here:

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation

Park D, Cho SJ, Kim K, Woo H, Kim JE, Lee JY, Koh J, Lee J, Choi JS, Chang DK, Choi YH, Chung JI, Cha WC, Jeong OS, Jekal SY, Kang M

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation

JMIR Med Inform 2021;9(12):e29212

DOI: 10.2196/29212

PMID: 34889753

PMCID: 8701706

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Korean Healthy Men.

  • Dohyun Park; 
  • Soo Jin Cho; 
  • Kyunga Kim; 
  • Hyunki Woo; 
  • Jee Eun Kim; 
  • Jin-Young Lee; 
  • Janghyun Koh; 
  • JeanHyoung Lee; 
  • Jong Soo Choi; 
  • Dong Kyung Chang; 
  • Yoon-Ho Choi; 
  • Ji In Chung; 
  • Won Chul Cha; 
  • Ok Soon Jeong; 
  • Se Yong Jekal; 
  • Mira Kang

ABSTRACT

Background:

Pulse transit time (PTT) and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to utilize these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP.

Objective:

This study aimed to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models.

Methods:

This study was conducted on 1,362 healthy men over 18 years who visited Samsung Medical Center. The SBP and DBP were estimated using the multiple linear regression method. Models were divided into two groups based on age: under 60 and 60 &over, 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error (RMSE) were used as performance metrics.

Results:

The model divided into two age groups (under 60 and 60 &over) performed better than the model without it. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, age met the criteria presented by the American Association for the Advancement of Medical Instrumentation (AAMI). The prediction errors were within the range of 9~12mmHg that can occur with a gold standard mercury sphygmomanometer.

Conclusions:

Dividing age based on age 60 showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.


 Citation

Please cite as:

Park D, Cho SJ, Kim K, Woo H, Kim JE, Lee JY, Koh J, Lee J, Choi JS, Chang DK, Choi YH, Chung JI, Cha WC, Jeong OS, Jekal SY, Kang M

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation

JMIR Med Inform 2021;9(12):e29212

DOI: 10.2196/29212

PMID: 34889753

PMCID: 8701706

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