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Predicting Response to Depression Treatment (PReDicT)
Predicting Response to Depression Treatment
(PReDicT)
Date du début: 1 oct. 2015,
Date de fin: 30 sept. 2018
PROJET
TERMINÉ
The objective of the Predicting Response to Depression Treatment (PReDicT) project is to conduct clinical, health economic and commercial research to validate the clinical performance and to complete the pre-commercial development of the eHealth P1vital® Oxford Emotional Test Battery (eH-ETB), a novel medical device to improve the treatment and management of depression in clinical practice. On completion of the PReDicT project, the eH-ETB will be ready for market launch. Depression is a common, serious and in some cases life‐threatening condition, affecting around 350 million people in the world and 11% of EU citizens suffer from depression at some point in their life. It takes 4-6 weeks after starting antidepressant treatment before a physician can detect whether the treatment is working, the majority of patients with depression do not respond to the first treatment they are prescribed and it can take several months before an effective treatment is identified. During this time a patients’ ability to work and function socially is severely impaired, and they can spend many weeks or months absent from work placing a substantial burden on the economy and healthcare resources. The eH-ETB addresses this problem by identifying within 1 week of starting treatment whether an antidepressant is working or not which can significantly reduce the time required to identify an effective treatment. Patients benefit from this by returning to good mental health sooner. The aggregated economic cost of depression in the EU is €92 billion, amounting to 1% of the total European gross domestic product. It is estimated that the eH-ETB has the potential to reduce these healthcare and economic costs by more than 10%. Thus the PReDicT project focuses on a patient-centric, enabling technology that not only improves efficiency and outcomes for patients but also significantly reduces socio-economic costs.
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