About us      |      Background      |      Creators


ABOUT US

Our group comprises of clinical cardiologists, bioinformatic scientists as well as PhD scholars. We investigate the utility of registry data in anticipating deaths for patients with heart attacks. We have now synthesized the data into an easy calculator to be used by clinicians in Malaysia to better risk stratify patients with heart attacks.

We hope with this unique calculator, we can help prevent avoidable deaths in the Malaysian population with heart attacks.


Why did we develop?

Malaysia have seen a rapid evolution in the past 50years. We have moved away from having Communicable Diseases as the number ONE cause of death to Heart Diseases as the number ONE cause of death for the past TEN years.

Heart attacks are divided into Unstable Angina, Non ST-Elevation Myocardial Infarction and ST-elevation Myocardial Infarction (STEMI). STEMI patients have the highest risk of dying. In the western population, where most cardiac outcome calculator, deaths from STEMI varies between 5-8% (please check reference). In Malaysia, our mortality rate from the NCVD registry is DOUBLE at 10-12%. Intercepting patients at high risk of death currently utilise predicting calculators derived from Western population.

Our population is different from the aspect of genetics, dietary, environment, risk factors and healthcare facility. Hence, we may have different risks contributing to the higher death rate that is unbeknown to us. A locally produced calculator helps to provide an accurate estimation to facilitate better patient care.

  • Patients with STEMI are at high risk of death in Malaysia.

  • Thrombolysis in Myocardial Infarction (TIMI) is used for 30 days mortality risk prediction in Malaysian hospital.

  • Derived mainly from Western Caucasian pool and may not reflect our diversity.

  • In Malaysia, patients presenting with STEMI are younger than the Western cohort.

  • These risk scores are not population-specific and may not be able to account nuances related to a specific region.

  • The machine learning calculator for STEMI may help a cardiologist, health care practitioners with evaluating risk/benefit of invasive and non-invasive procedures by knowing a patient’s baseline risk.


How it started?

    APRIL 2018
  • Preliminary study on the application of Machine Learning to predict mortality in Malaysian patients using sample dataset from UiTM NCVD ACS Registry.

    • APRIL 2019
  • Large scale study using National Cardiovascular Disease Registry (NCVD) to predict mortality after STEMI using machine learning algorithms and validated against the TIM RiskI score. Machine learning algorithm performed better than TIMI in the Malaysian population.

    • SEPTEMBER 2019
  • Developed an online STEMI mortality calculator.

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BACKGROUND

  • Retrospective data from the national cardiovascular disease database (NCVD-ACS) registry collected by the Ministry of Health Malaysia and National Heart Association Malaysia (NHAM) from 2006 – 2017 was utilized.

  • The registry collects data on a standardized set of clinical, demographic, and procedural variables, along with outcomes, for consecutive patients treated at participating institutions.

  • The calculator includes in hospital, 30-day, and 1-year all-cause mortality.

  • All patients from the ACS registry without exclusion were used; patients who received reperfusion (fibrinolysis, primary PCI (PPCI), angiography demonstrating spontaneous reperfusion, or urgent coronary artery bypass grafting (CABG)) for STEMI.

  • This score also included patients with left bundle branch block.

  • Support Vector Machine was used as the primary algorithm for risk score stratification of the STEMI patients.

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CREATORS

  • Prof. Sazzli Kasim - Consultant Cardiologist at UiTM Specialist Center.

  • Dr. Sorayya Malek - Senior Lecturer at Bioinformatics unit, Institute of Biological Sciences, Faculty of Science University of Malaya.

  • Dr. Khairul Shafiq Inin Ibrahim - Cardiologist at UiTM Specialist Center.

  • Mr. Firdaus Aziz - Postgraduate student at Bioinformatics unit, Institute of Biological Sciences, Faculty of Science University of Malaya.

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Acknowledgement

We would like to thank The National Cardiovascular Disease Database (NCVD). The NCVD is sponsored by the Ministry of Health Malaysia and co-sponsored by the National Heart Association of Malaysia (NHAM). NCVD is responsible to collect information about cardiovascular disease in Malaysia, which will enable us to know the incidence of cardiovascular disease (CVD) and to evaluate its risk factors and treatment in the country.

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