Abstract：
BACKGROUND:The large intra-patient variability in type 1 diabetic patients dramatically reduces the ability to achieve adequate blood glucose control. A novel methodology to identify different blood glucose dynamics profiles will allow therapies to be more accurate and tailored according to patient's conditions and to the situations faced by patients (exercise, week-ends, holidays, menstruation, etc). MATERIALS AND METHODS:A clustering methodology based on the normalized compression distance is applied to identify different profiles for diabetic patients. First, the methodology is validated using "in silico" data from 10 patients in 3 different scenarios: days without exercise, poor controlled exercise days and days with well-controlled exercise. Second, we perform a series of in vivo experiments using data from 10 patients assessing the ability of the proposed methodology in real scenarios. RESULTS:In silico experiments show that the methodology is able to identify poor and well-controlled days in theoretical scenarios. In vivo experiments present meaningful profiles for working days, bank days and other situations, where different insulin requirements were detected. CONCLUSIONS:A tool for profiling blood glucose dynamics of patients can be implemented in a short term to enhance existing analysis platforms using combined CGM-CSII systems. Besides coping with the information overload, the tool will assist physicians to adjust and improve insulin therapy and patients in the self-management of the disease.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Contreras I,Quirós C,Giménez M,Conget I,Vehi Jdoi
10.1016/j.cmpb.2016.08.022subject
Has Abstractpub_date
2016-11-01 00:00:00pages
131-41eissn
0169-2607issn
1872-7565pii
S0169-2607(16)30213-9journal_volume
136pub_type
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