Early renin-angiotensin system intervention is more beneficial than late intervention in delaying end-stage renal disease in patients with type 2 diabetes.

AIMS: To develop and validate a model to simulate progression of diabetic kidney disease (DKD) from early onset until end-stage renal disease (ESRD), and to assess the effect of renin-angiotensin system (RAS) intervention in early, intermediate and advanced stages of DKD. METHODS: We used data from the BENEDICT, IRMA-2, RENAAL and IDNT trials that assessed effects of RAS intervention in patients with type 2 diabetes. We built a model with discrete disease stages based on albuminuria and estimated glomerular filtration rate (eGFR). Using survival analyses, we assessed the effect of RAS intervention on delaying ESRD in early [eGFR>60 ml/min/1.73 m(2) and albumin:creatinine ratio (ACR) <30 mg/g], intermediate (eGFR 30-60 ml/min/1.73 m(2) or ACR 30-300 mg/g) and advanced (eGFR <30 ml/min/1.73 m(2) or ACR >300 mg/g) stages of DKD for patients in different age groups. RESULTS: For patients at early, intermediate and advanced stage of disease, whose mean age was 60 years and who received placebo, the median time to ESRD was 21.4, 10.8 and 4.7 years, respectively. RAS intervention delayed the predicted time to ESRD by 4.2, 3.6 and 1.4 years, respectively. The benefit of early RAS intervention was more pronounced in younger patients; for example, for patients with a mean age of 45 years, RAS intervention at early, intermediate or advanced stage delayed ESRD by 5.9, 4.0 and 1.1 years versus placebo. CONCLUSIONS: RAS intervention early in the course of proteinuric DKD is more beneficial than late intervention in delaying ESRD.

AdViSHE: A Validation-Assessment Tool of Health-Economic Models for Decision Makers and Model Users.

BACKGROUND: A trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool providing model users with a structured view into the validation status of HE decision models, to address this trade-off. METHODS: A Delphi panel was organized, and was completed by a workshop during an international conference. The proposed tool was constructed iteratively based on comments from, and the discussion amongst, panellists. During the Delphi process, comments were solicited on the importance and feasibility of possible validation techniques for modellers, their relevance for decision makers, and the overall structure and formulation in the tool. RESULTS: The panel consisted of 47 experts in HE modelling and HE decision making from various professional and international backgrounds. In addition, 50 discussants actively engaged in the discussion at the conference workshop and returned 19 questionnaires with additional comments. The final version consists of 13 items covering all relevant aspects of HE decision models: the conceptual model, the input data, the implemented software program, and the model outcomes. CONCLUSIONS: Assessment of the Validation Status of Health-Economic decision models (AdViSHE) is a validation-assessment tool in which model developers report in a systematic way both on validation efforts performed and on their outcomes. Subsequently, model users can establish whether confidence in the model is justified or whether additional validation efforts should be undertaken. In this way, AdViSHE enhances transparency of the validation status of HE models and supports efficient model validation.

Health-related quality of life and productivity losses in patients with depression and anxiety disorders.

OBJECTIVES: To assess the explanatory power of disease severity and health-related quality of life (HRQOL) on absenteeism and presenteeism in a working population suffering from depression and/or anxiety disorders. METHODS: We used data of a large, multicenter, randomized trial (n = 644). Pearson chi-squared tests, analysis of variance, and multinomial logistic regression analyses were performed to explore associations of the type of the disorder and HRQOL with different types of productivity losses. Multivariate regression analyses were performed to assess associations with the duration of absenteeism. RESULTS: The type of the disorder, disease severity, and HRQOL were associated with different types of productivity losses. Health-related quality of life and age were significantly associated with the duration of absenteeism. CONCLUSIONS: Our findings indicate that HRQOL may significantly explain the type of productivity loss as well as the duration of absenteeism.