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

Mets Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04142593
• Other study ID #: 107126-E
• Status: Completed
• Start date: September 1, 2018
• Est. completion date: January 1, 2022

Study information

• Verified date: January 2022
• Source: Far Eastern Memorial Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 100000
• Est. completion date: January 1, 2022
• Est. primary completion date: September 15, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Metabolic Syndrome
• Mets

Locations

Country	Name	City	State
Taiwan	Oriental Institute of Technology / Far Eastern Memorial Hospital	New Taipei City	Pan-Chiao Dist.

Sponsors (1)

Lead Sponsor	Collaborator
Far Eastern Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.