Project 04

IMPALA

Innovative Monitoring in Paediatrics in Low-resource settings: an Aid to save lives?

 

The number of children dying in African hospitals remains too high. A large part may be prevented if children can be observed more closely allowing timely life-saving treatments. Continuous monitoring of vital signs such as heart rate and oxygen saturation is applied for this reason in high income countries but these techniques have not been adapted for low resource settings. New techniques of monitoring may make it possible to predict potential deterioration (and not just detect), these include new vital signs sensors, bedside blood tests (biomarkers) and artificial intelligence/machine learning.

We shall using an observational cohort study of 1000 children (28 days-60 months) admitted to the high dependency areas of Queen Elizabeth Central Hospital (QECH) and Zomba Central Hospital (ZCH) evaluate the predictive potential of a the machine learning algorithms among critically ill Malawian children for improved healthcare using a continuous vital signs monitoring system specifically improved for use in low-resource settings. We shall also assess if biomarkers and demographic data can predict critical illness events alongside vital signs; and assess if study site and age affects the composition and performance of the predictive potential of the model.

EDCTP
Research Council of Norway

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