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Illnesses like infections, cold, influenza and so on in type 1 diabetes mellitus (T1DM) patients, can compromise the daily patient administered diabetes treatment. This in turn may result in fluctuating blood glucose concentrations, especially hyperglycemia for prolonged periods, which over time can cause serious late complications. The illness prediction project at Tromsø Telemedicine Laboratory aims to construct a prediction model that, through use of patient observable parameters, for example, blood glucose, insulin injections and body temperature, can significantly identify risk of developing illnesses, before onset of symptoms and before illness onset.

Such a model could potentially enable T1DM patients to fight the illnesses, and prepare for an adequate change in the T1DM-management earlier on.

 

Objective

To develop an illness prediction model that can predict illness in T1DM patients before onset of symptoms, using the patient’s observable parameters.

Referenced File
Submitted by hparton on

It is well known that diabetic patients are particularly sensitive to infections however no robust diagnostic test for the early detection of infection has been developed to date. Glucose levels  would be an ideal indicator, since diabetics measure their blood glucose (BG) on a daily basis along with insulin intake. At the same time some computerized systems have been developed that collect BG values using sensors and transmit them to a central data repository, such as the Electronic Healthcare Record. Acute infection often results in hyperglycemia, due to release of regulatory hormones and pro-inflammatory cytokines as evidenced by studies on hospitalized patients. Nevertheless the underlying mechanisms of infection-related stress hyperglycemia are not fully understood.

 

Objective

The aim of the study is to assess the correlation between blood glucose levels and infection and to propose the development of a model for the early detection of infections in diabetics.

Referenced File
Submitted by elamb on
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