Mental health

Quick test helps get mental patients on right medications

McMaster University psychiatrists and engineers in Hamilton are working together to find faster ways to get the right drug to the right mental health patients. The study, published online by medical journal Clinical Neurophysiology, shows a common, safe and cheap electroencephalography (EEG) test can predict which patients will do best on various drugs with about 89 percent accuracy.

Currently, mental health patients trial a number of different medications until doctors find the one that works best for them. In some cases, it means a long wait for effective treatment, or needless exposure to dangerous side effects of drugs that don’t work.
Clozapine, for example, is recognized as an effective treatment for chronic medication-resistant schizophrenia but can produce serious side effects such as seizures, cardiac arrhythmias or bone marrow suppression. Some patients can develop blood problems that are life-threatening. Weekly to monthly blood sampling is required.

“Some people can suffer terrible side effects from clozapine,” said Dr. Gary Hasey (pictured at far left), associate professor at McMaster and director of the Transcranial Magnetic Stimulation laboratory at St. Joseph’s Healthcare Mood Disorders Clinic in Hamilton. 
“The logistic difficulties for the patient and treatment team are also substantial. A method to reliably determine, before the onset of therapy, whether a patient will or will not respond to clozapine would greatly assist the clinician in determining whether the risks and logistic complexity of clozapine are outweighed by the potential benefits.”

To conduct the study, EEGs were taken from 23 patients diagnosed with medication-resistant schizophrenia before they began taking clozapine. Twelve were men and 11 were women, all of middle age. The brainwave patterns and response to the clozapine therapy of these patients were used to “train” a computer algorithm to predict whether or not a specific patient will respond to the drug.

The prediction accuracy was approximately 89 per cent. This algorithm showed similar predictive accuracy when it was further tested in a new group of 14 additional patients treated with clozapine.

Having a more accurate way to predict whether it will work helps psychiatrists determine whether the risks are worth the benefits for a particular patient. It also means the patient gets effective treatment faster.

For the next phase of the study, researchers will test patients suffering from depression.

Biomedical engineers teamed up with two psychiatrists to do the study on a shoestring budget of between $30,000 and $40,000 a year for three years. “The computational power available today supports new machine learning methodologies that can help doctors better diagnose and treat illness and disease,” said Prof. James Reilly of McMaster’s Department of Electrical and Computer Engineering, who participated in the study. “Large amounts of data can be processed very quickly to identify patterns or predict outcomes. We’re looking forward to applying the findings to other areas.”

The study was funded by the Natural Sciences and Engineering Research Council of Canada and the Wales-based Magstim Company Ltd, which makes EEG machines. For more information, visit http://www.eng.mcmaster.ca/news/news2010/eeg_schizophrenia.html

Posted August 19, 2010