For many years glycosylated hemoglobin A1C is one of the universal indicators of metabolic control in patients with diabetes. The results of A1C levels reflect the average blood glucose levels from the period of three months. A1C is one of the most important information for diabetes team, it allows to determine the effects of treatment. In the long-term analysis, it is a predictor of the complication risk. A1C value is affected by many factors: the method of treatment, type of insulin, self-control, numbers of hypoglycemia etc. In the case of similar methods of treatment, ability to cope with diabetes self-management, ability of correct interpretation of glycemic values and adaptation of insulin doses appears to be the factor, which determines A1C levels. But, there is still a lack of objective methods to assess effective coping with the requirements of the illness - adherence. Currently it is based primarily on a subjective feeling of diabetes team.~The aim of the research is to develop a mathematical model called ADHERENCE index, and to evaluate its effectiveness.~Methods~Participants:~Participants will be recruited from the Outpatient Diabetes Clinic in the Institute of Mother and Child. Patient should be treated by intensive insulinotherapy using Continuous Subcutaneous Infusion Set (CSII). Number of participants: 200.~Study plan - data collecting:~Patients will attend diabetes clinic as usual, with one additional visit in the middle of the 3-months period. During those visits, data from glucometers and insulin pumps will be downloaded.~Visit 1 (V0) - getting agreement to participate in the study, giving information about the study, collecting patient's in initial interview. HbA1c measurement by DCA Vantage Analyzer (Siemens), control the settings of time and date in insulin pumps and glucometer(s), ask for using one kind of glucometer, and if it's impossible - to set time and date in every glucometer, and to take them all for the next visits. Setting terms of next visits (after 6 ±1 and 12 ±2 weeks)~Visit 2 (V1) - after 6 ±1 weeks after V0 - downloading data from the insulin pump and glucometer(s) by Diasend and CareLink software. Collecting information about some accidents: ketoacidosis, severe hypoglycemia, ketonuria, illness treated with antibiotics. Reminding about the next visit (V2).~Visit 3 (V2) - after 6 ±1 weeks after V1 - the last visit; downloading data from the insulin pump and glucometer(s) by Diasend and CareLink software. Collecting information about some accidents: ketoacidosis, severe hypoglycemia, ketonuria, illness treated with antibiotics; the A1C measurement.~After data collection they will be taken under analysis. Downloaded data will be compared to A1C. After that, factors, which are crucial for A1C will be found. Last part of the research is an attempt to create a mathematical model.~Data important for mathematical model:~Downloaded:~BG values - average, standard deviation, number of values per day, values above goal (>180 mg/dl), values within goal (70-180 mg/dl), values below goal (<70 mg/dl), percentage of measurements fall within the recommended range (70-180 mg/dl); Low Blood Glucose Index (LBGI) and High Blood Glucose Index (HBGI).~Insulin pumps data - average total daily dose, average daily basal, average daily bolus, percentage of daily basal and bolus doses, number of boluses per day, number of extended boluses per day, using of temporary basal rate, number of stoppings, number of days with one infusion set.~Collected by the interview:~Number and time of ketoacidosis, ketonuria, severe hypoglycemia, illness treated with antibiotics. Interview during the V0 visit: sex, weight, height, age, suffering from diabetes in age, A1C level, reported total daily dose, type of insulin (Humalog, Novorapid, Apidra), number of places in body for infusion, reported number of days with one infusion set, place of residence (village, town, city).~Expected outcomes~Obtaining a mathematical model - an ADHERENCE index - will allow to assess an adherence to diabetes and self-management. It will be an objective indicator, which could allow to identify the most impacting factor which determines A1C levels and to identify this aspects of diabetes self- management, which should be improved by diabetes education or motivation. Using of ADHERENCE index should allow to identify patients with poor self-management earlier, than it could be symbolized by high A1C level. It could also allow to draw conclusions about the most important aspects of diabetes self-management.
The aim of the research is to develop a mathematical model called ADHERENCE index, and to evaluate its effectiveness.