A simple questionnaire filled out by depressed patients can help identify whether a particular medication is likely to help them, according to a new study by Yale University researchers.
Data mined from clinical trials may soon help doctors tailor antidepressant therapy to their patients, the authors say. Currently, only about 30% of patients get relief from the first drug they are prescribed, and it can often take a year or more before doctors find the right medication to alleviate symptoms of depression.
The Yale team analyzed data from a large clinical trial on depression and pinpointed 25 questions that best predicted the patients’ response to a particular antidepressant.
Using these questions, they developed a mathematical model to predict whether a patient will respond to Celexa after three months of treatment.
“These are questions any patient can fill out in 5 or 10 minutes, on any laptop or smartphone, and get a prediction immediately,” explained Adam Chekroud, Ph.D. candidate in the Human Neuroscience Lab and lead author of the paper.
The model performed better at predicting whether a patient would get better by using that drug than a group of practicing psychiatrists. The researchers then tested the model by predicting outcomes in a second clinical trial conducted years later.
They found the model worked well in predicting patient responses to similar drugs (Lexapro, as well as a combination of Lexapro and Wellbutrin). However, the model did not predict response to drugs that have a different mechanism in the brain (Effexor and Remeron).
Once more data is analyzed from new and existing clinical trials, predictions about other treatment options will become available, suggested Philip Corlett, assistant professor of psychiatry and senior author of the study.
“And there is no reason why we can’t predict other important outcomes for our patients, like suicide or return to work,” Corlett added.
Reference:
Adam Mourad Chekroud, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry DOI: 10.1016/S2215-0366(15)00471-X
Last Updated on March 4, 2024