P45 Internal, External, and Cross-Validation of the DEDUCE Model, a Cost-Utility Tool Using Patient-Level Microsimulation to Evaluate Sensor-Based Glucose Monitoring Systems in Type 1 and Type 2 Diabetes

Cost–utility analysis of a flash continuous glucose monitoring system in the management of people with type 2 diabetes mellitus on basal insulin therapy—An Italian healthcare system perspective

“…A strength of the new DEDUCE microsimulation model is the use of updated risk equations for T2DM (based on the ACCORD trial rather than the older UK Prospective Diabetes Study), compared with previous models. 21,22 However, although the risk equations used are the most up-to-date available, the T2DM treatment landscape is constantly changing, for example, through recent guidelines recommending glucagon-like peptide-1 receptor agonists for many people with T2DM. 7 Further research is needed to establish the risks of complications and the outcomes associated with flash CGM use among patients using newer, non-insulin therapies.…”

“…This analysis used DEDUCE (DEtermination of Diabetes Utilities, Costs, and Effects), a recently developed, validated, patient‐level microsimulation model 21 . The DEDUCE model assigns costs and utilities according to the complications and acute diabetic events (ADEs; severe hypoglycaemic events [SHEs], non‐severe hypoglycaemic events [NSHEs] and diabetic ketoacidosis [DKA]) experienced by each simulated patient, with the incidence and history of complications updated each cycle.…”

“…The DEDUCE model assigns costs and utilities according to the complications and acute diabetic events (ADEs; severe hypoglycaemic events [SHEs], non‐severe hypoglycaemic events [NSHEs] and diabetic ketoacidosis [DKA]) experienced by each simulated patient, with the incidence and history of complications updated each cycle. Diabetes‐related complications are modelled in DEDUCE using the RECODe risk engine, which was developed using data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial 21–23 . For each complication, the risk in each year is predicted as a function of patients' age, gender, ethnicity, smoking status, cardiovascular history, systolic blood pressure, medication use, HbA1c, total cholesterol level, high‐density lipoprotein cholesterol level, serum creatinine level, and urine albumin: creatinine ratio (Table 1).…”

“…Diabetes-related complications are modelled in DEDUCE using the RECODe risk engine, which was developed using data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. [21][22][23] For each complication, the risk in each year is predicted as a function of patients' age, gender, ethnicity, smoking status, cardiovascular history, systolic blood pressure, medication use, HbA1c, total cholesterol level, high-density lipoprotein cholesterol level, serum creatinine level, and urine albumin: creatinine ratio (Table 1). 21…”

“…[21][22][23] For each complication, the risk in each year is predicted as a function of patients' age, gender, ethnicity, smoking status, cardiovascular history, systolic blood pressure, medication use, HbA1c, total cholesterol level, high-density lipoprotein cholesterol level, serum creatinine level, and urine albumin: creatinine ratio (Table 1). 21…”