Profiling intra-patient type I diabetes behaviors. (November 2016)

• Record Type: Journal Article
• Authors: Contreras, Iván; Quirós, Carmen; Giménez, Marga; Conget, Ignacio; Vehi, Josep
• Is Part Of: Computer methods and programs in biomedicine. Volume 136(2016)
• Page Start: 131
• Page End: 141
• Publication Date: 2016-11
• Subjects: Type 1 diabetes -- Continuous glucose monitoring -- Insulin pumps -- Time series prediction -- Clustering; AUC180 the area under the curve above 180 -- AUC70 the area under the curve below 70 -- BG blood glucose -- BGV the blood glucose variability -- CGM continuous glucose monitors -- CHO the daily carbohydrates -- CSII continuous subcutaneous insulin infusion -- I:C the insulin to carbohydrates ratio -- ISIG the average interstitial signal for Enlite sensors -- ISIGV the ISIG variability -- NCD normalized compression distance -- T1D type 1 diabetes -- TDD the total diary dose of insulin; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.08.022
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The methodology helps to identify the most common situations affecting blood glucose control for every single patient. Adjusting insulin therapy to the identified situations leads to more accurate blood glucose control. The insights provided by the method allow doctors and patients to understand and act properly in the daily life situations. The methodology can be easily integrated in the existing commercial platforms based on CGM-CSII systems. 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.