In this research theme, we aim to improve outcomes for critically ill patients in Singapore through early detection and population/systems level interventions for sudden cardiac arrest. This research theme proposes 5 complementary programmes with the overarching aim of improving the survival rate and functional outcome of patients at-risk of cardiac and critical care events in Singapore.
1. Out of Hospital Cardiac Arrest (OHCA) Data Warehouse
Develop a dynamic data model that integrates information from our national 995 dispatch center (operated by our collaborator, the Singapore Civil Defense Force or SCDF), first responders, ambulance, emergency department and hospital medical records, enabling the delivery of mathematical programming (MP) models and multi-criteria decision support systems (DSS).
2. Community based interventions in OHCA
Develop and evaluate the effectiveness of a bundle of community based interventions on increasing bystander CPR and public access defibrillation rates, and hence OHCA survival rates, in 6 pilot constituencies, including mass simplified CPR training, installation of AEDs in residential blocks, a first responder app and a novel CPR card.
3. Ambulance based interventions
To develop and evaluate the effectiveness of a bundle of ambulance based interventions on pre-hospital return of spontaneous circulation (ROSC), which include the Impedance Threshold Device (ITD), manual defibrillation by intermediate life-support providers, IV amiodarone and high performance team CPR.
4. In-hospital Therapeutic Temperature Management (TTM)
To develop a national TTM registry and implement a post-resuscitation bundle, following guidelines from the American Heart Association and National Resuscitation Council, with a view to improving in-hospital survival and minimize neurological morbidity post cardiac arrest resuscitation.
5. Early risk-prediction with wearable Sensor Networks for Patients (ESP)
To utilize a novel wearable sensor system tracking temperature, pulse, respiration and cardiac rhythm wirelessly with Machine Learning (ML) capability to enable safe, early step-down care in patients.
Research Theme Lead:
A/Prof Marcus Ong Eng Hock A/Prof Emile Tan Kwong Wei
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