Application of Decision Analysis Techniques' in Huge Health-checkup Database to Explore the High-risk Group With Metabolic Syndrome

Application of Decision Analysis Techniques' in Huge Health-checkup Database to Explore the High-risk Group With Metabolic Syndrome (MetS)

Study Overview

• Status: Completed
• Conditions: Mets
• Intervention / Treatment: Other

Detailed Description

According to the American Heart Association（AHA）modified Adult Treatment Panel III（ATP-III） and the criteria of Health Promotion Administration, Taiwan. Five indices are used to define metabolic syndrome（MetS): including waist circumference （WC), high blood pressure（H/P）, fasting plasma glucose（FPG）, triglyceride（TG）, and high-density lipoprotein-cholesterol（HDL-C）. The recent researches showed us, there was no research applying the five criteria into their decision model. This study proposal will be the first study which evaluated the importance of the criteria related to apply these indices and decision model into the clinic for risk factors assessment.

This study was divided into 2 stages: (1) to analyze the big database of health examination to find out the major decision-making analysis module of MetS, including the level of importance and decision-making weight of 5 indicators, which can be provided as reference for suggestions on clinical medical treatments or health education focuses of health management of sub-health population; (2) to analyze other demographic variables of the database (educational background, residence, occupation, etc.) and the variables affecting health patterns (including smoking, drinking, long-term sitting work pattern) to find out the important variables affecting high risk group for MetS among populations of all ages, in order to investigate the improvement strategies for early prevention or intervention of important variables. As aging society is coming, it is estimated that the elderly people over the age of 65 in Taiwan will reach 20% by 2025. According to the estimation of Executive Yuan, the growth rate of healthcare service industry will reach at least 17%, and the annual output value will reach USD 18 billion. Therefore, this study intends to develop strategies for preventing chronic illness in the middle-aged and elderly people and find out the characteristic variables of high risk group according to different age groups to further reduce the incidence of MetS or CVD.

• Study Type: Observational
• Enrollment (Actual): 100000

Contacts and Locations

Study Locations

• Taiwan
  • Pan-Chiao Dist.
    • New Taipei City, Pan-Chiao Dist., Taiwan, 22061
      • Oriental Institute of Technology / Far Eastern Memorial Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

As aging society is coming, it is estimated that the elderly people over the age of 65 in Taiwan will reach 20% by 2025. According to the estimation of Executive Yuan, the growth rate of healthcare service industry will reach at least 17%, and the annual output value will reach USD 18 billion. Therefore, this study intends to develop strategies for preventing chronic illness in the middle-aged and elderly people and find out the characteristic variables of high risk group according to different age groups to further reduce the incidence of MetS or CVD.

Description

Inclusion Criteria:

1. In 2006-2016, the MJ Health Research Foundation's member,which continuously tested twice or more of the annual health check database , about 90,000 people.
2. The person who was in charge of the taxi driver health checkup project commissioned by the New North City Transportation Bureau at Far Eastern Memorial Hospital, data period 2012-2016, about 2,000 people.

Exclusion Criteria:

• no

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Other

What is the study measuring?

Primary Outcome Measures

Outcome Measure

Measure Description

analyze the big database of health examination to find out the major decision-making analysis module of MetS

This study plans to use decision analysis and new statistical techniques, including decision tree algorithms; random forest algorithms; multivariate linear regression combinations and hierarchical linear models, and with a large number of health databases. Analysis, through the comprehensive health check report and physiological indicator data accumulated over many years, find more key variables or physiological indicators that can be used to evaluate MetS or CVD, in order to provide government departments, medical institutions or nationals early Detect or prevent, and further reduce the overall rate of MetS in Taiwan at this stage.

Collaborators and Investigators

• Sponsor: Far Eastern Memorial Hospital
• Investigators: Study Director: Ming-Shu Chen, PhD, Oriental Institute of Technology

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2018
• Primary Completion (Actual): September 15, 2019
• Study Completion (Actual): January 1, 2022

Study Registration Dates

• First Submitted: April 18, 2019
• First Submitted That Met QC Criteria: October 25, 2019
• First Posted (Actual): October 29, 2019

Study Record Updates

• Last Update Posted (Actual): March 22, 2022
• Last Update Submitted That Met QC Criteria: March 20, 2022
• Last Verified: January 1, 2022

More Information

Terms related to this study

• Keywords: Classification; Metabolic Syndrome（MetS）; Decision Tree; Risk Factors Assessment
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Insulin Resistance; Hyperinsulinism; Metabolic Syndrome
• Other Study ID Numbers: 107126-E

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No