Identification of blood glucose patterns in patients with type 1 diabetes using continuous glucose monitoring and clustering technique.

OBJECTIVE: To show that statistical techniques allow for obtaining a reduced number of four-hour glucose profiles that can identify any glucose behavior in patients with type 1 diabetes mellitus. PATIENTS AND METHODS: A retrospective study of 10 patients with type 1 diabetes mellitus was conducted using data collected by continuous glucose monitoring. A data mining technique based on decision trees called CHAID (Chi-square Automatic Interaction Detection) was used to classify glucose profiles into groups using two decision criteria. These were 1, the seven days of the week and 2, different time slots, the day being divided into six sections of four hours each. Clustering was performed according to the glucose levels recorded using the statistically significant differences found. RESULTS: Significant differences (P-value <.05) and dependencies were seen between the glucose profiles classified depending on the independent variables 'day of the week' and 'time slot'. The relationships found were different for each patient, showing the need for individualized studies. CONCLUSIONS: The results obtained will facilitate mathematical modeling of glucose, and can be used to develop an individualized classifier for each patient that categorizes glucose profiles based on the day of the week and time slot variables. Using this classifier, it will be possible to predict the glucose levels of the patient knowing on which day of the week and in which time slot he/she is, leading to more precise models. Healthcare professionals will also be able to improve patient habits and therapies.

Identification of blood glucose patterns in patients with type1 diabetes using continuous glucose monitoring and clustering techniques. / Identificación de patrones de glucemia en pacientes con diabetes tipo1 mediante monitorización continua de glucosa y técnicas de clusterización.

OBJECTIVE: To show that statistical techniques allow for obtaining a reduced number of four-hour glucose profiles that can identify any glucose behavior in patients with type1 diabetes mellitus. MATERIAL AND METHODS: A retrospective study of 10 patients with type1 diabetes mellitus was conducted using data collected by continuous glucose monitoring. A data mining technique based on decision trees called CHAID (Chi-square Automatic Interaction Detection) was used to classify glucose profiles into groups using two decision criteria. These were: 1, the seven days of the week, and 2, different time slots, the day being divided into six sections of four hours each. Clustering was performed according to the glucose levels recorded using the statistically significant differences found. RESULTS: Significant differences (P<.05) and dependencies were seen between the glucose profiles classified depending on the independent variables 'day of the week' and 'time slot'. The relationships found were different for each patient, showing the need for individualized studies. CONCLUSIONS: The results obtained will facilitate mathematical modeling of glucose, and can be used to develop an individualized classifier for each patient that categorizes glucose profiles based on the day of the week and time slot variables. Using this classifier, it will be possible to predict the glucose levels of the patient knowing on which day of the week and in which time slot he/she is, leading to more precise models. Healthcare professionals will also be able to improve patient habits and therapies.