Wendelin Schramm

Cost effectiveness of combination therapy with pioglitazone for type 2 diabetes mellitus from a german statutory healthcare perspective.

AbstractBackground: Pioglitazone has been approved in Europe for oral combination therapy for type 2 diabetes mellitus. Along with other agents of the thiazolidinedione class, it has a novel intracellular mechanism of action. Clinical trials with pioglitazone have confirmed a strong product profile in terms of control of blood glucose and lipids. However, the drug acquisition cost for pioglitazone is greater than standard medications for type 2 diabetes. Long-term data regarding the cost effectiveness of pioglitazone-based combination therapy are not available. Objective: To evaluate, using a decision analysis model, the cost effectiveness of pioglitazone-based combination therapy compared with relevant alternative medications for the treatment of type 2 diabetes in Germany. Methods: This study compared the clinical effects and costs of pioglitazone 30mg added to metformin in patients who failed metformin monotherapy and pioglitazone added to a sulphonylurea in patients who failed sulphonylurea monotherapy, with the most relevant treatment alternatives. A published and validated Markov model was adapted to reflect the management of type 2 diabetes. This simulated the number of severe complications occurring and the mean life expectancy of a diabetic cohort, which was based on the overweight group of the UK Prospective Diabetes Study at year 6 of follow-up. Drug treatment costs, other costs for general management of type 2 diabetes and the costs of complications were combined to compute overall lifetime treatment costs from the perspective of the German statutory healthcare system in 2002. Results: Combination therapy with pioglitazone/metformin was associated with a higher life expectancy (15.2 years) relative to sulphonylurea/metformin (14.9 years) or acarbose/metformin (14.7 years). Likewise, pioglitazone/sulphonylurea (15.5 years) was superior to metformin/sulphonylurea (14.9 years) and acarbose/sulphonylurea (14.8 years). Undiscounted incremental cost-effectiveness ratios in comparison to the next best strategy were €20 002 per life-year gained (LYG) for pioglitazone/metformin versus sulphonylurea/metformin, and €8707 per LYG for pioglitazone/sulphonylurea versus metformin/sulphonylurea. After discounting costs and life expectancy at 5% per year, the incremental cost-effectiveness ratio was €47 636 per LYG for pioglitazone/metformin versus sulphonylurea/metformin, and €19 745 per LYG for pioglitazone/sulphonylurea versus metformin/ sulphonylurea. Conclusions: In this model, with its underlying assumptions and data, combination therapy with pioglitazone increased life expectancy in overweight type 2 diabetes patients at acceptable cost compared with other well established medications in Germany. These findings should be re-evaluated as soon as additional evidence becomes available from the currently ongoing long-term clinical and economic studies.

Integrated Personalized Diabetes Management (PDM): Design of the ProValue Studies: Prospective, Cluster-Randomized, Controlled, Intervention Trials for Evaluation of the Effectiveness and Benefit of PDM in Patients With Insulin-Treated Type 2 Diabetes

Background: Collaborative use of structured self-monitoring of blood glucose (SMBG) data and data management software, utilized within a 6-step cycle enables integrated Personalized Diabetes Management (PDM). The 2 PDM-ProValue studies shall assess the effectiveness of this approach in improving patient outcomes and practice efficiencies in outpatient settings. Methods: The PDM-ProValue studies are 12-month, prospective, cluster-randomized, multicenter, trials to determine if use of integrated PDM in daily life improves glycemic control in insulin-treated type 2 diabetes patients. Fifty-four general medical practices (GPs) and 36 diabetes-specialized practices (DSPs) across Germany will be recruited. The practices will be randomly assigned to the control groups (CNL) or the intervention groups (INT) via cluster-randomization. CNL practices will continue with their usual care; INT practices will utilize integrated PDM. The sample size is 1,014 patients (n = 540 DSP patients, n = 474 GP patients). Each study is designed to detect a between-group difference in HbA1c change of at least 0.4% at 12 months with a power of 90% and 2-sided significance level of .05. Differences in timing and degree of treatment adaptions, treatment decisions, blood glucose target ranges, hypoglycemia, self-management behaviors, quality of life, patients attitudes, clinician satisfaction, practice processes, and resource consumption will be assessed. Study endpoints will be analyzed for the modified intent-to-treat and per protocol populations. Trial results are expected to be available in late 2016. Discussion: Effective and efficient strategies to optimize diabetes management are needed. These randomized studies will help determine if PDM is beneficial.

A method for using real world data in breast cancer modeling

OBJECTIVES Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model. METHODS We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab. RESULTS The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence. CONCLUSIONS Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data.

Self-Monitoring of Blood Glucose: One STeP Forward?

Introduction: In times of short health care budgets, reimbursement for self-monitoring of blood glucose (SMBG) in diabetes patients without insulin treatment is subject to debate. The Structured Testing Program (STeP) trial found a positive correlation of test frequency and improved hemoglobin A1c (HbA1c) levels in poorly controlled type 2 diabetes patients not treated with insulin. Methods: A structured literature search for other clinical studies reporting on SMBG frequency was performed. Results: There is scarce evidence: Three trials, including STeP, noted a significant and relevant correlation between testing frequency and improved HbA1c levels (FA effect), whereas two studies did not. The comparability between the identified studies is problematic. Conclusion: Future research should consider correlations between testing frequency and level of glycemic control. More emphasis should be placed on a structured approach to use SMBG and to address adherence to testing and therapy.

Integrated personalized diabetes management improves glycemic control in patients with insulin-treated type 2 diabetes: Results of the PDM-ProValue study program

AIMS Globally, many patients with insulin-treated type-2 diabetes are suboptimally controlled. The PDM-ProValue study program evaluated whether integrated personalized diabetes management (iPDM) has the potential to improve clinical outcomes. METHODS 101 practices with 907 patients participated in the 12-month, prospective, controlled, cluster-randomized study program. HbA1c levels, therapy changes, frequency of hypoglycemic episodes, patient reported outcomes, and physician satisfaction were assessed. RESULTS iPDM led to a greater reduction in HbA1c after 12 months vs. usual care (-0.5%, p < 0.0001 vs. -0.3%, p < 0.0001), (Diff. 0.2%, p = 0.0324). Most of the HbA1c reduction occurred after 3 months and remained stable thereafter. The percentage of patients with therapy adjustments was higher in the iPDM group at all visits (p < 0.01 at week 3, month 3, month 6). Patient adherence at month 12 was higher in the iPDM group compared to baseline (Odds ratio = 2.39; p = 0.0003); also, patient treatment satisfaction (DTSQc: 12.2 vs. 10.4, δ = 1.78, p = 0.004; DTSQs: 31.0 vs. 30.0, δ = 0.924, p = 0.02), and physician satisfaction was higher in the intervention group. CONCLUSIONS iPDM improved the use of diagnostic data leading to better glycemic control, more timely treatment adjustments (indicating reduced clinical inertia), and increased patient adherence and treatment satisfaction among patients and physicians.

Transition probabilities of HER2-positive and HER2-negative breast cancer patients treated with Trastuzumab obtained from a clinical cancer registry dataset.

Records of female breast cancer patients were selected from a clinical cancer registry and separated into three cohorts according to HER2-status (human epidermal growth factor receptor 2) and treatment with or without Trastuzumab (a humanized monoclonal antibody). Propensity score matching was used to balance the cohorts. Afterwards, documented information about disease events (recurrence of cancer, metastases, remission of local/regional recurrences, remission of metastases and death) found in the dataset was leveraged to calculate the annual transition probabilities for every cohort.

Digital Diabetes Self-Management: A Trilateral Serial

The way diabetes patients cope with their disease in day-to-day routines is decisive for the development or the prevention of medical complications. Smartphones have created the ubiquitous environment to support health care with mobile applications (mHealth). This article comments on the publication by Offringa et al in JDST, which is one of few studies that tries to isolate the effects of a diabetes app. At the same time, it is a good example to discuss general aspects of mHealth in diabetes care. Treatment context, eHealth literacy, interoperability, and efficiency will determine the success of diabetes apps. The development has not yet reached its end. A triple quality feedback loop linking persons with diabetes, health care providers, and mHealth providers is suggested.

Improvements in Patient Care by an Integrated Personalized Diabetes Management (iPDM) Approach May Be Driven by the Structured Process and How Physicians Use Data Sources

Aim: The PDM-ProValue study program showed the benefit of iPDM for people with type 2 diabetes mellitus (T2D) on insulin therapy. Here, we analyze which treatment process conditions may be key for this success. Methods: The study program was conducted over 12 months in a prospective, controlled, cluster-randomized setting. 101 medical practices were randomized to the iPDM arm (n=53) or the control (CNL) arm (n=48). In addition to medical and patient related outcomes, parameters of process quality and data sources for therapy decisions were monitored e.g., by means of Likert-scaled requests. Results: After 12 months, HbA1c reduction vs. baseline was higher for patients in iPDM (0.5%, p Discussion: A more holistic and beneficial use of data sources by implementing iPDM may be key for the improvements observed in this difficult to treat patient group. A more structured treatment process applied by the physicians and better personalization of therapy seem to enhance physician-patient interaction. Physicians’ positive perception of iPDM may facilitate more focused treatment decisions, thereby overcoming clinical inertia. Disclosure L. Heinemann: Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento. Consultant; Self; Roche Diabetes Care Health and Digital Solutions. D. Messinger: Other Relationship; Self; Roche Diagnostics Corporation, Roche Diagnostics Corporation, Roche Pharma, AbbVie Inc., Merck KGaA. W. Schramm: Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. I. Vesper: None. J. Weissmann: Employee; Self; Roche Diabetes Care Deutschland GmbH. B. Kulzer: Research Support; Self; Berlin-Chemie AG. Speaker9s Bureau; Self; Berlin-Chemie AG, Novo Nordisk Inc.. Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions. Speaker9s Bureau; Self; Roche Diabetes Care Health and Digital Solutions. Research Support; Self; Abbott. Speaker9s Bureau; Self; Abbott, Eli Lilly and Company. Advisory Panel; Self; Novo Nordisk Inc., Medtronic, Ascensia Diabetes Care. Speaker9s Bureau; Self; Ascensia Diabetes Care.

Development of a chlamydia infection model for evaluating costs and outcomes of health interventions

Introduction: Chlamydia is a very common bacterial sexual transmitted infection (STI) among young adults. High numbers of asymptomatic cases hamper a timely treatment start, whereas the treatment itself is efficient and cheap. Proactive screening can decrease this mismatch. There are many models which are able to evaluate and simulate different screening options. Most of them though are based on old or insufficient data, are not accessible for everybody, or are not designed in a user-friendly way. Aim: We want to determine the feasibility of developing an easy-to-use chlamydia infection model, which can be easily updated to reflect changes in medical knowledge. Methods: Starting with a literature review, we have set up a chlamydia infection model. This model was refined with the help of STI experts. The model was implemented by using the programming language Java (version 1.6). We validated the model using internal and external validation methods. Results: The implementation of the model allows users to edit all parameters. The model consists of two separate sub-models. One sub-model simulates health effects of chlamydia for individuals, including the different outcomes in males and females. The other sub-model tracks the spreading of chlamydia within the computed cohort and regards heterosexual as well as homosexual partnerships. Both sub-models are independent of each other and therefore easily exchangeable. The overall model can be kept up to date by either updating single parameters of the model or exchanging a sub-model. The model can be operated by graphical user interfaces to enable non-health economists and non-modelling experts to work with this disease model. Discussion: We showed the feasibility of implementing an easy-to-use chlamydia model. This study can be regarded as a step towards developing more user-friendly decision support tools in health economics to assist decision makers in medicine.

Eine seltene Kombination organspezifischer Autoimmunerkrankungen

Der Nachweis organspezifischer Autoimmunerkrankungen gelingt heute durch die methodischen Fortschritte der Immunologie leichter und ist sicherer geworden [4]. Es wird uber eine jetzt 22jahrige Patientin mit der Kombination von zumindest vier organspezifischen Autoimmunerkrankungen berichtet, die fur sich allein gesehen nicht sehr selten vorkommen, aber in dieser Kombination noch nicht publiziert sind: Myasthenia gravis, Thrombozytopenie, Autoimmunthyreoiditis und Anaziditat.