Article type
Year
Abstract
Background: Meta-analysis in reviews systematically results from numerous randomized controlled trials and supplies synthesis information for clinical practice. However, there is no standard procedure to predict the impact of translating these results at a population level.
Objectives: We propose a strategy to simulate public health impact from the results of meta-analyses combined with a risk score applied on a virtual realistic population (VRP) platform. We simulated a preventive strategy for sudden death (SD), a major cardiovascular event, in a French population with type 2 diabetes (T2D). We illustrated how this approach could help to rationalize health public decisions.
Methods:
- Generate a French diabetic VRP.
- Establish a SD risk score.
- Review existing meta-analyses on therapeutic efficacy in preventing SD in T2D.
- Apply these results on the generated VRP with drug combination and risk-based patient selection.
Results: A French diabetic VRP of 176,187 was generated from a 8995-patient sample, giving an median of SD risk of 1.7% at a 5-year time horizon. We estimated the numbers needed to treat (NNTs) by a simultaneous treatment by statin and ACE inhibitor at 221 people for the whole population, and at 108/104 women/men among individuals of the highest 10% predicted SD risk. The corresponding untreated risks of SD were 1.9% on the whole population, and 3.9% and 4.1% in top risk deciles for women and men respectively (Table 1).
Conclusions: Our approach, which gathers effect models (via meta-analyses and risk scores) and VRP simulation, provides a powerful multi-component tool for valuing each evidence-based component, better transposing clinical trial results to practice, and facilitating clinical decision in both public health and individual levels, on both medico-economic aspects.
Key-words: meta-analysis, practical decision making, virtual realistic population, sudden death, health public simulation
Objectives: We propose a strategy to simulate public health impact from the results of meta-analyses combined with a risk score applied on a virtual realistic population (VRP) platform. We simulated a preventive strategy for sudden death (SD), a major cardiovascular event, in a French population with type 2 diabetes (T2D). We illustrated how this approach could help to rationalize health public decisions.
Methods:
- Generate a French diabetic VRP.
- Establish a SD risk score.
- Review existing meta-analyses on therapeutic efficacy in preventing SD in T2D.
- Apply these results on the generated VRP with drug combination and risk-based patient selection.
Results: A French diabetic VRP of 176,187 was generated from a 8995-patient sample, giving an median of SD risk of 1.7% at a 5-year time horizon. We estimated the numbers needed to treat (NNTs) by a simultaneous treatment by statin and ACE inhibitor at 221 people for the whole population, and at 108/104 women/men among individuals of the highest 10% predicted SD risk. The corresponding untreated risks of SD were 1.9% on the whole population, and 3.9% and 4.1% in top risk deciles for women and men respectively (Table 1).
Conclusions: Our approach, which gathers effect models (via meta-analyses and risk scores) and VRP simulation, provides a powerful multi-component tool for valuing each evidence-based component, better transposing clinical trial results to practice, and facilitating clinical decision in both public health and individual levels, on both medico-economic aspects.
Key-words: meta-analysis, practical decision making, virtual realistic population, sudden death, health public simulation