Project Lead(s): Pascal Lavoie
Early diagnosis of a severe infection in a baby can be difficult as it mimics many other health conditions. Some of these infections can be due to bacteria and failing to treat these infections with antibiotics early can lead to death or major disability. As a result, many infants, even those with non-bacterial infections, are treated unnecessarily with antibiotics, thus wasting scarce resources and potentially creating resistant superbugs.
To address this problem, innovators with the University of British Columbia will build a diagnostic algorithm, incorporated into a mobile phone app, to help doctors identify babies most in need of rapid treatment for a potential bacterial infection by using clinical signs, and risk factors, and subtle variations in infants’ vital signs. The project will use advanced molecular blood tests to accurately detect bacterial infections, in order to improve the diagnostic precision of the algorithm and mobile phone app.
The project will obtain clinical data, including vital sign data captured using a low-cost hand/foot probe connected to a mobile phone, and a small blood sample from 500 newborns at the Kamuzu Central Hospital in Malawi over an eighteen-month period.
The predictor model will be incorporated into a low-cost mobile app and sensor device in partnership with LGT Medical (a Vancouver-based medical technology company, also supported by Grand Challenges Canada) to guide critical life-saving interventions for babies with severe infections around the world.