ECHO-T2DM

Developer: The Swedish Institute for Health Economics

 

Contact: Michael Willis

 

Participated in following Mt Hood Diabetes Challenge Meetings: 2010, 2012, 2014, and 2016  

 

​Publicly accessible? ECHO-T2DM is propriety.  Detailed information about model structure and model validity is publicly available, however (see “Key Publications”)

 

Is the model continuing to be developed? Yes

 

Brief Description of the model: The Economic and Health Outcomes Model of T2DM (ECHO-T2DM) is a stochastic, micro-simulation (patient-level) model, suitable for estimating long-term cost-effectiveness of the treatment of T2DM.  The physiology of T2DM is captured using Markov health states for micro- and macrovascular complications and death.  The cycle length is 1 year and the time horizon is user-definable.  ECHO-T2DM accounts explicitly for both first-order and second-order uncertainty and is programmed in R with an Excel interface.

 

ECHO-T2DM generates a user-defined number of hypothetical patients at simulation start based on user-defined probability distributions of age, sex, ethnicity, disease duration, biomarker values like HbA1c and systolic blood pressure (SBP), smoking status, and health complications.Patient characteristics are updated each cycle.

 

Chronic kidney disease (CKD), neuropathy, and retinopathy are modeled in parallel.Progression rates, adjusted for HbA1c, T2DM duration and other biomarkers in line with current clinical understanding, steer transition between the different health states.Macrovascular complications consist of ischemic heart disease (IHD), myocardial infarction (MI), stroke, and heart failure (HF).Four sets of macrovascular risk equations are supported:UKPDS68, UKPDS82, ADVANCE, and the Swedish National Diabetes Registry.ECHO-T2DM supports UKPDS68 and UKPDS82 mortality risk equations, and mortality is a competing risk for all other events.

 

Treatment comparisons consist of initial treatments (multiple comparisons are supported), treatment intensification sequences, HbA1c target values, and treatment algorithms for hypertension, dyslipidemia, and excess weight.Anti-hyperglycemic drug profiles include initial biomarker changes (HbA1c, SBP, BMI, cholesterol, eGFR, and heart rate) and subsequent rate of biomarker evolution (i.e., “drift”), AE rates (e.g., hypoglycemia), relative risks for complications, treatment compliance, and discontinuation rules related to poor HbA1c control, AEs, contraindications, and/or reaching user-defined maximum treatment duration.Simpler profiles are supported for treating hypertension, dyslipidemia, and excess weight.

 

Unit costs for treatments, AEs, micro- and macrovascular complications (event costs and annual follow-up costs), revascularization procedures, and depression can be assigned.Macrovascular costs vary by fatal or non-fatal.Indirect costs are supported.Baseline utility and disutility decrements for specific patient characteristics and health complications can be assigned.

 

ECHO-T2DM reports outcomes including cumulative incidences and rates (RRRs) of each health outcome of health complications, AE rates (HRs), LYs and QALYs, inferred cause of death and sources of disutility, biomarker evolution curves, mean time to rescue treatment, and a host of cost and cost-effectiveness metrics.

 

​Funding source for model development:  Janssen Global Services, LLC

 

Key Publications:

Willis M, Johansen P, Nilsson A, Asseburg C. Validation of the Economic and Health Outcomes Model of T2DM (ECHO-T2DM). PharmacoEconomics 2017;35:375-396. DOI: https://doi.org/10.1007/s40273-016-0471-3

 

Sabapathy S, Neslusan C, Yoong K, Teschemaker A, Johansen P, Willis M. Cost-effectiveness of Canagliflozin versus Sitagliptin when Added to Metformin and Sulfonylurea in Type 2 Diabetes in Canada. J Popul Ther Clin Pharmacol. 2016;23(2):151-168. http://www.jptcp.com/volume-issue.php?volume=23&issue=2&year=2016&journal=jptcp

 

Neslusan C, Teschemaker A, Johansen P, Willis M, Valencia-Mendoza A, Puig A. Cost-Effectiveness of Canagliflozin versus Sitagliptin as Add-on to Metformin Patients with Type 2 Diabetes Mellitus in Mexico. Value in Health Regional Issues 2015; 8C:8-19. DOI: http://dx.doi.org/10.1016/j.vhri.2015.01.002  

Willis M, Asseburg C, He J. Validation of Economic and Health Outcomes Simulation Model of Type 2 Diabetes Mellitus (ECHO-T2DM). Journal of Medical Economics 2013; 16(8): 1007-1021. DOI: https://doi.org/10.3111/13696998.2013.809352

Gupta V, Willis M, Johansen P, Nilsson A, Shah M, Mane A, Neslusan C. Long-Term Clinical Benefits of Canagliflozin 100 mg versus Sulfonylurea in Patients with Type 2 Diabetes Mellitus Inadequately Controlled with Metformin in India. Value in Health Regional Issues 2019; 18: 65-73. DOI: https://doi.org/10.1016/j.vhri.2018.06.002

Neslusan C, Teschemaker A, Willis M, Johansen P, Vo L. Cost-Effectiveness Analysis of Canagliflozin 300 mg Versus Dapagliflozin 10 mg Added to Metformin in Patients with Type 2 Diabetes in the United States. Diabetes Ther 2018; 9(2): 565-581. DOI: https://doi.org/10.1007/s13300-018-0371-y

Willis M, Asseburg C, Neslusan C. Conducting and Interpreting Results of Network Meta-Analyses in Type 2 Diabetes Mellitus: A Review of Network Meta-Analyses That Include Sodium Glucose Co-transporter 2 Inhibitors. Diabetes Research and Clinical Practice 2019; epub ahead of print. DOI: https://doi.org/10.1016/j.diabres.2019.01.005

Willis M, Asseburg C, Nilsson A, Neslusan C. Challenges and Opportunities Associated with Incorporating New Evidence of Drug-Mediated Cardioprotection in the Economic Modeling of Type 2 Diabetes: A Literature Review. Diabetes therapy 2019; [Epub ahead of print]. DOI: 10.1007/s13300-019-00681-4

LINK: https://link.springer.com/article/10.1007%2Fs13300-019-00681-4

The values below are simulated Quality Adjusted life Years (QALYs) for a set of reference simulations

Reference simulation

The values below are simulated Quality Adjusted life Years (QALYs) for a set of reference simulations