Mayo Clinic algorithm shows potential in individualizing treatment for depression

Mayo Clinic algorithm shows potential in individualizing treatment for depression

By
Susan Murphy

February 27, 2021

Finding an effective antidepressant medication for people diagnosed with depression, also called major depressive disorder, is often a long and complex process of "try and try again" ― going from one prescription to the next until achieving a therapeutic response.

This complex disease, which affects more than 16 million people in the U.S., can cause symptoms of persistent emotional and physical problems, including sadness, irritability and loss of interest. In severe cases, suicidal thoughts are a risk.

Now, a computer algorithm developed by researchers within Mayo Clinic's Center for Individualized Medicine and the University of Illinois at Urbana-Champaign could help clinicians accurately and efficiently predict whether a patient with depression will respond to an antidepressant.

The new research, published in Neuropsychopharmacology, represents a possible step forward in individualizing treatment for major depressive disorder. It also demonstrates a collaboration between computer scientists and clinicians who are using large datasets to address challenges of individualizing medicine practices of globally devastating diseases.

_{^{Overall, the algorithm was tested on 1,996 patients with depression, and the outcomes of whether patients would respond favorably to therapy were correctly predicted in over 72% of the patients.}}

Harnessing teamwork to enhance patient outcomes

In this study, Arjun Athreya, Ph.D., a computer scientist within Mayo Clinic’s Molecular Pharmacology and Experimental Therapeutics, and William Bobo, M.D., chair of Mayo Clinic Florida's Psychiatry and Psychology, closely collaborated to find ways in which a clinical challenge of global significance could be modeled. They worked together to derive insights to augment routine clinical decision-making to enhance patient outcomes.

"The idea was to create a technology that serves as a reliable companion to a clinician at the point of care, as opposed to a technology that replaces their judgment," explains Dr. Athreya. "This meant I had to embed myself in the practice enough to learn the challenges clinicians face as well as the needs of the patient to collectively transform those needs and challenges into an engineering technology."

Putting artificial intelligence (AI) to the test

The new approach uses an AI framework, called Almond, to find patterns and unique characteristics in a patient's genomic and clinical data. This allows for the right treatment to be selected or enables a treatment to be changed relatively soon after it is started, if the algorithm predicts a poor response.

^{_{"We used the algorithm to identify the speciﬁc depressive symptoms and thresholds of improvement that were predictive of antidepressant response by four weeks for a patient to achieve remission or response, or a nonresponse, by eight weeks." - Dr. Arjun Athreya}}

For the study, Drs. Athreya and Bobo, and their team, trained the algorithm by creating symptom profiles for nearly 1,000 patients with major depressive disorder who were starting treatment with selective serotonin reuptake inhibitors, often called SSRIs. These are the most commonly prescribed first-line antidepressants.

The team first stratified patients according to their depression severity to construct a graph. Then they identified the different ways that their depression changed after starting treatment. They found that some depressive symptoms were more helpful in predicting treatment outcomes than others. They also identified the levels of improvement needed in each treatment to have a good outcome.

"We used the algorithm to identify the speciﬁc depressive symptoms and thresholds of improvement that were predictive of antidepressant response by four weeks for a patient to achieve remission or response, or a nonresponse, by eight weeks," explains Dr. Athreya.

Overall, the algorithm was tested on 1,996 patients with depression, and the outcomes of whether patients would respond favorably to therapy were correctly predicted in over 72% of the patients.

Algorithm allows health care providers, patients to individualize care

Dr. Bobo says that providing interpretable predictions could enhance clinical treatment of depression and reduce the time associated with multiple trials of ineffective antidepressants.

^{_{"The model generates output in a way that clinicians are able to easily assimilate, interpret and potentially use in the limited time they have in clinical visits with the patients." - Dr. William Bobo.}}

"Typically, clinicians initiate a therapy and patients come back after four weeks," Dr. Bobo explains. "Then, based on the clinician's judgment of improvement, they make their best guess as to what the outcome would be at eight weeks, and decide to either change the therapy or stay the course."

Dr. Bobo says the study highlights the essential partnership between computer scientists and clinicians.

"The logic that clinicians apply follows certain steps, and this is what really intrigued Dr. Athreya, who abstracted this as both a computational problem and an engineering problem. We were coming at the same idea with different viewpoints that ultimately had a high degree of synergy," Dr. Bobo explains.

"What we then did with the study was to 'algorithmize' the clinician's thinking logic using probabilistic graphical models to formalize the predictions of eight-week outcomes by how severe the disease was prior to treatment and how much specific depressive symptoms had to have improved between baseline and four weeks," Dr. Bobo says. "This way, the model generates output in a way that clinicians are able to easily assimilate, interpret and potentially use in the limited time they have in clinical visits with the patients."

The researchers say the model could be beneficial in busy primary care practices and may hasten referrals for specialty mental health consultation if the predicted outcomes of treatment are nonresponses.

"We hope this study will help us pave the way forward for developing electronic tools that can help clinicians and patients make better decisions about their treatment at the earliest point in time possible after starting it," says Dr. Athreya.

For a disease that is marked with high degrees of variability in treatment outcomes across patients, this accuracy marks a step in the right direction of individualizing therapy for depression ― with the opportunity to augment clinical measures with biological measures such as genomics, which the team already is working on as part of a broader study supported by the National Science Foundation.

Also, the team is prospectively validating its findings in routing practice at Mayo Clinic in Rochester and Mayo Clinic in Florida through a Mayo Clinic Transformational Center Award.