Cardiovascular risk prediction model for patients on lipid modifying drugs

Professor Chui, Sze Ling Celine   (Principal Investigator (PI))

Professor Chan Esther Wai Yin   (Co-Investigator)

Professor Luo Ruibang   (Co-Investigator)

Professor Lam Tak Wah   (Co-Investigator)

Mr Blais Joseph   (Co-Investigator)

Professor Wong Ian Chi Kei   (Co-Investigator)

Professor Siu David Chung Wah   (Co-Investigator)

Professor Wan Yuk Fai Eric   (Co-Investigator)

18

2021-05-03

2022-11-30

634432

Cardiovascular risk prediction model for patients on lipid modifying drugs

Cardiovascular, lipid modifying drugs, risk prediction model

Others - Biological SciencesOthers - Medicine, Dentistry and Health

PiH/119/21

Research Talent Hub for ITF Projects (RTH-ITF)

2021

Completed

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.