Now, an online test to predict depression

December 3rd, 2008 - 2:17 pm ICT by ANI  

Washington, Dec 3 (ANI): An online test, called predictD, has been developed by University College London researchers which can tell whether or not a person is suffering from depression.

PredictD is a universal tool that could eventually be used by family doctors and local clinics to identify those at risk of depression for whom prevention might be most useful.

A team, led by UCL Professors Michael King and Irwin Nazareth, developed the risk algorithm, which was tested in 6,000 people visiting their family doctor in six countries in Europe (UK, Spain, Portugal, the Netherlands, Slovenia and Estonia).

The accuracy of the test was also tested in nearly 3,000 GP attendees in a further country, Chile, in South America. The participants in the study were followed up at six and 12 months.

The technique was modelled on risk indices for heart disease, which provide a percentage risk estimate over a given time period. The algorithm was as accurate at predicting future episodes of depression as similar instruments developed in Europe to predict future risk of heart problems.

And the scientists have even set up a website for the risk algorithm

Further testing of the tool as an early detector of depression is planned in randomised trials of prevention in Europe.

The team are also exploring the feasibility of using the instrument in China, with plans to set up a study on the prediction of depression in a Chinese community setting. This would be the first ever research initiative of its kind within Asia.

Professor Michael King, UCL Department of Mental Health Sciences, said: Depression is a common problem throughout the world, but although we know how to treat it, we know very little about how to prevent its onset. We have ways of predicting the onset of heart disease or stroke, but none for predicting peoples risk of major depression. Our study is one of the first to develop a risk algorithm for just this purpose.

Risk tools such as ours are needed to focus more effort on preventing depression. For example, people identified as at risk by an online tool could be flagged on a GPs computer. Recognition of those at risk could help with watchful waiting or active support, such as restarting treatment in patients with a history of depression. Patients could also be advised on the nature of depression or on cognitive behaviour therapies to help reduce their risk of developing major depression.

The next stage of our research will be to establish how GPs could use our tool to help prevent the onset of depression. We are hoping to run a large-scale trial to explore the tools use in prevention.

The study is published in the Archives of General Psychiatry. (ANI)

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