Cardiovascular risk prediction model for patients on lipid modifying drugs
Grant Data
Project Title
Cardiovascular risk prediction model for patients on lipid modifying drugs
Principal Investigator
Dr Chui, Sze Ling Celine
(Principal Investigator (PI))
Co-Investigator(s)
Professor Wong Ian Chi Kei
(Co-Investigator)
Dr Wan Yuk Fai Eric
(Co-Investigator)
Professor Chan Esther Wai Yin
(Co-Investigator)
Professor Siu David Chung Wah
(Co-Investigator)
Mr Blais Joseph
(Co-Investigator)
Professor Lam Tak Wah
(Co-Investigator)
Dr Luo Ruibang
(Co-Investigator)
Duration
30
Start Date
2020-12-01
Amount
3184635
Conference Title
Cardiovascular risk prediction model for patients on lipid modifying drugs
Presentation Title
Keywords
Cardiovascular, lipid modifying drugs, risk prediction model
Discipline
Others - Biological Sciences,Others - Medicine, Dentistry and Health
HKU Project Code
PRP/070/19FX
Grant Type
Partnership Research Programme (PRP)
Funding Year
2020
Status
On-going
Objectives
Lipid lowering drugs are prescribed for both primary and secondary prevention of cardiovascular diseases (CVD). Most CVD risk scores available are based on trials (e.g. Framingham score) that included only a few risk factors and simple algorithm that could not give an accurate risk prediction of CVD. This study aims to develop and validate a risk prediction model of cardiovascular events among patients on or at higher risk of receiving lipid lowering drugs with the use of big data and artificial intelligence. Any patients who are on lipid lowering drugs for both primary and secondary prevention for CVD will be identified from CDARS. Their relevant medical records including CVD diagnoses, co-morbidities, biological data and demographics including age and sex will be retrieved. A cohort of 145,875 individuals who received any lipid test in Queen Mary Hospital from 2004-2014 will be used. The 70% of the cohort will be training dataset while 30% of it will be the validation dataset. We expect that the risk prediction model can help identify patients who are at risk of CVD and initiate preventive treatment to lower subsequent risk of CVD. Initiating preventive treatment will lower morbidity and mortality associated with CVD. This could potentially lower the burden of the public healthcare sector.