Ignite Creation Date:
2024-05-06 @ 4:08 PM
Last Modification Date:
2024-10-26 @ 2:04 PM
Study NCT ID:
NCT04881383
Status:
UNKNOWN
Last Update Posted:
2022-05-18
First Post:
2021-05-04
Brief Title:
Development and Validation of DM and Pre-DM Risk Prediction Model
Sponsor:
The University of Hong Kong
Organization:
The University of Hong Kong
Study Overview
Official Title:
The Development and Validation of a DM and Pre-DM Risk Prediction Function for Case Finding in Primary Care in Hong Kong
Status:
UNKNOWN
Status Verified Date:
2022-05
Last Known Status:
ACTIVE_NOT_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Many DM and pre-DM remain undiagnosed The aim is to develop and validate a risk prediction function to detect DM and pre-DM in Chinese adults aged 18-84 in primary care PC The objectives are to
1 Develop a risk prediction function using non-laboratory parameters to predict DM and pre-DM from the data of the HK Population Health Survey 20142015
2 Develop a risk scoring algorithm and determine the cut-off score
3 Validate the risk prediction function and determine its sensitivity in predicting DM and pre-DM in PC
Hypothesis to be tested
The prediction function developed from the Population Health Survey PHS 20142015 is valid and sensitive in PC
Design and subjects
We will develop a risk prediction function for DM and pre-DM using data of 1857 subjects from the PHS 20142015 We will recruit 1014 Chinese adults aged 18-84 from PC clinics to validate the risk prediction function Each subject will complete an assessment on the relevant risk factors and have a blood test on OGTT and HbA1c on recruitment and at 12 months
Main outcome measures
The area under the Receiver operating characteristic ROC curve sensitivity and specificity of the prediction function
Data analysis and expected results
Machine learning and Logistic regressions will be used to develop the best model ROC curve will be used to determine the cut-off score Sensitivity and specificity will be determined by descriptive statistics A new HK Chinese general population specific risk prediction function will enable early case finding and intervention to prevent DM and DM complications in PC
1 Develop a risk prediction function using non-laboratory parameters to predict DM and pre-DM from the data of the HK Population Health Survey 20142015
2 Develop a risk scoring algorithm and determine the cut-off score
3 Validate the risk prediction function and determine its sensitivity in predicting DM and pre-DM in PC
Hypothesis to be tested
The prediction function developed from the Population Health Survey PHS 20142015 is valid and sensitive in PC
Design and subjects
We will develop a risk prediction function for DM and pre-DM using data of 1857 subjects from the PHS 20142015 We will recruit 1014 Chinese adults aged 18-84 from PC clinics to validate the risk prediction function Each subject will complete an assessment on the relevant risk factors and have a blood test on OGTT and HbA1c on recruitment and at 12 months
Main outcome measures
The area under the Receiver operating characteristic ROC curve sensitivity and specificity of the prediction function
Data analysis and expected results
Machine learning and Logistic regressions will be used to develop the best model ROC curve will be used to determine the cut-off score Sensitivity and specificity will be determined by descriptive statistics A new HK Chinese general population specific risk prediction function will enable early case finding and intervention to prevent DM and DM complications in PC
Detailed Description:
Diabetes Mellitus DM is the second most common chronic non-communicable disease NCD and a major public health issue In 2017 it was estimated that 451 million adults worldwide had DM a number that is anticipated to rise to 693 million by 2045 In terms of economic burden it was estimated that the global cost of DM in 2015 was 131 trillion US dollars which accounted for 18 of global gross domestic product In China the prevalence of DM has increased rapidly from less than 1 in 1980 to 109 in 2013 with approximately 1096 million Chinese adults 258 of all cases worldwide currently living with the condition Among the Chinese population Hong Kong has one of the highest prevalence of DM The Population Health Survey PHS 20142015 conducted by Department of Health found a prevalence of 84 of DM among persons aged 15-84 in Hong Kong more than half 45 of which were previously unknown Data unpublished from the Population Health Survey 20142015 showed a further 95 of persons aged 15-84 had hyperglycaemia pre-diabetes but were unaware of the problem before the survey DM can result in severe complications which lead to disabling morbidity and premature mortality A number of randomised controlled trials RCTs have found that lifestyle interventions eg diet exercise and pharmacological treatments are effective in preventing DM and its complications However it has been reported that 224 million adults 497 of all cases world-wide are unaware that they have the condition similar to the finding of the Hong Kong PHS 20142015 DM can be present for 9-12 years prior to a diagnosis and is often only detected when patients present with complications Hence there is an urgent need for earlier detection of DM so that appropriate interventions can be provided to prevent andor delay progression to complications It would be even more effective if individuals could be identified at the pre-diabetes pre-DM stage when there may still be an opportunity to revert to normoglycaemia by life-style modifications While DM satisfies all Wilson and Jungners 1968 criteria of screening studies have shown that general population screening is not effective and the current recommendation is case finding targeting at high-risk individuals The Hong Kong Reference Framework for Diabetes Care for Adults in Primary Care Settings recommends periodic screening for DM among persons aged 45 years old or having DM risk factors The recommended methods for screening include 75-g oral glucose tolerance tests OGTT fasting plasma glucose FPG tests or HbA1c tests Indeed a cost-effectiveness analysis reported that screening for DM and prediabetes was cost-saving among patients identified as being at high risk eg body mass index BMI 35 kgm2 systolic blood pressure 130mmHg or 55 years of age when compared with no screening In order to identify high risk individuals more accurately multivariate risk prediction models have been developed and incorporated into DM prevention programs in a number of Western countries Such models have included sociodemographic factors eg age sex clinical factors eg family history of DM gestational DM or biomarkers eg BMI blood pressure However the majority of these models were developed primarily in Caucasian populations and have not performed as well among Chinese populations For example the Prospective Cardiovascular Münster Cambridge San Antonia and Framingham models were found to have inferior discrimination in a cohort of Chinese people This can be due to ethnic differences as well as lifestyle and socioeconomic factors calling for the need of population-specific risk prediction models Since 2009 a number of risk prediction models and scoring algorithms have been developed specifically for Chinese populations mostly from Mainland China only two of which were developed and validated on Hong Kong Chinese people The first used simple self-reported factors and laboratory measurements to develop a scoring algorithm However the generalisability of the model to primary care patients may be limited as 70 of the subjects of the development and validation samples had known risk factors for DM The second risk prediction model for Hong Kong Chinese was previously developed by members of the investigators team with data from 3357 asymptomatic non-diabetic professional drivers Non-laboratory risk factors included age BMI family history of DM regular physical activity PA and high blood pressure Triglyceride was added to the laboratory-based algorithm The application of this risk predication model is limited because the sample was predominately male 927 professional drivers and the accuracy was modest It is noted that the majority of factors included in previous models are non-modifiable eg family history of DM gestational DM age and there is a call for future research to incorporate more lifestyle factors in order to improve the predictive validity and impact of risk prediction models Lifestyle factors that may be associated with DM and pre-DM include physical activity PA level dietary factors eg fibre sugar or fat intake alcohol consumption smoking and sleep This proposed study aims to develop a new DM and pre-DM risk prediction model specific for the Hong Kong general Chinese population that incorporates traditional and modifiable life style factors The investigators will apply the novel method of machine learning as well as the traditional logistic regression in model development to improve predictive power The investigators hope the results will enable the implementation of effective case finding of DM and pre-DM in primary care and prevent mortality and morbidity from this common but silent NCD for the people in Hong Kong
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None