Experience With Pharmacogenetic Testing Improves Confidence Among Clinicians

Clinicians expressed greater comfort with and confidence in pharmacogenetic testing (PGx) after participation in a clinical trial that employed PGx for depressed patients, according to a report published today in Psychiatric Services.

Some clinicians were uncertain about which patients might be best served by these tests, while others expressed concerns about cost. But there was a general perception that part of the test’s value was its potential to help patients with buy-in and confidence regarding medication treatment.

“Exposure to this novel practice is necessary to help providers understand its potential usefulness and how they may apply testing results in their clinical management of patients with depression,” wrote Bonnie M. Vest, Ph.D., of the State University of New York–University at Buffalo, and colleagues.

The Precision Medicine in Mental Health Care (PRIME Care) study was a randomized controlled trial to assess whether using a commercially available PGx test improved outcomes in patients with depression. All participating clinicians completed a baseline survey between July 2017 and January 2021. The survey assessed demographic information as well as comfort with and perceptions of PGx testing rated on a five-point Likert scale.

A follow-up survey was conducted between December 2020 and March 2021 after PRIME Care concluded. A total of 217 clinicians completed both surveys, and 61 also took part in qualitative interviews. Overall, 72% of those who completed the survey and 80% of those who participated in interviews worked in specialty mental health clinics; 62% and 72%, respectively, completed medical training.

Following the trial, 31% of clinicians strongly agreed with the statement “I feel comfortable ordering a pharmacogenetic test to predict risk of adverse events or the likelihood of a treatment response” compared with 15% before the study. Further, 38% strongly agreed with the statement “I feel well informed about the role of pharmacogenetic testing in choosing a psychotropic medication” compared with 21% before the trial. Mental health clinicians were much more likely than primary care ones to provide positive answers.

Qualitative interviews revealed more nuance. One provider responded: “I do feel patients have felt more confident about trying medications with that information, so I think there is some positive value.... I wouldn’t say it’s game changing.” Another provider said: “If it’s cost-effective enough to do it at the beginning, it might be worth it just to eliminate a lot of the guesswork…. I think cost would be prohibitive as far as just doing it on every patient.”

The authors concluded: “A pressing need exists for further research, including cost-benefit analyses, to inform decisions about PGx implementation. Specifically, our findings highlight providers’ concerns about patient-level criteria and when during treatment PGx testing is most beneficial.”

For related information, see the Psychiatric News article “Pharmacogenomic Testing May Help Achieve Better Patient Outcomes, Less Toxicity.”

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Evidence Base for Pharmacogenetic Tests Still Lacking, APA Workgroup Finds

There is still not enough evidence to support the use of pharmacogenetic tests in the treatment of depression, according to updated recommendations from APA’s Workgroup on Biomarkers and Novel Treatments. The recommendations were published in AJP in Advance.

Pharmacogenetic tests analyze an individual’s genes (obtained via blood, saliva, or cheek swabs) to find genetic variants that may influence how fast antidepressants are metabolized or how well they attach to their receptors. Using special algorithms, the tests then calculate the combined impact of all the variants and offer readouts of antidepressants that might be effective and others to avoid.

In 2018, APA’s Council on Research organized a workgroup to examine the available data on pharmacogenetic tests for depression; the workgroup concluded that there was insufficient evidence to support the widespread use of pharmacogenetic tools in clinical practice. Subsequently, both the FDA and International Society of Psychiatric Genetics voiced concerns about these tests.

“Despite expert opinions, warnings, and policy statements regarding their limitations for predicting antidepressant treatment response, the popularity of [pharmacogenetic] testing products has grown, with at least 35 U.S. commercial entities providing them by 2020,” wrote the APA workgroup members in their updated recommendations.

The workgroup examined data from 11 pharmacogenetic clinical trials conducted between 2017 and 2022, as well as six meta-analyses that combined individual results. “The main new contribution of these studies is one of numbers: several trials have included relatively large sample sizes, and >4,000 patients have now participated in [pharmacogenetic] studies,” they wrote.

Though most of the trials demonstrated that using a pharmacogenetic test increased the likelihood that a patient would respond to their antidepressant, these new studies did not address previous shortcomings, the workgroup continued. None of the new trials were fully blinded (neither patients nor investigators were aware who was receiving a test), which increases the risk of bias in decision making. Further, the control group in these studies was to provide treatment as usual, but little attention was given to ensuring clinicians were providing the best standards of depression care.

Finally, all studies were fully or heavily supported by the pharmacogenetic industry. “[A]lthough industry support is not in itself problematic and historically has often been integral in completing large, well-designed, definitive trials, its coexistence with the methodological concerns reviewed above augments the concern about bias,” the workgroup wrote.

“Genetic approaches remain promising, and we look forward to future studies and advances in the field,” the APA workgroup concluded. “However, we advise devoting greater attention to implementing study designs consistent with other studies of treatment interventions.”

For related information, see the Psychiatric News story, “Pharmacogenomics Can Inform ‘Big Data’ Projects.”

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Have You Gotten Email Requests from the AMA?

If so, the simple message is please respond. The AMA has sent out weekly reminder emails from PPISurvey@mathematica-mpr.com with the email subject line of “Reminder: The AMA needs your input to support fair and accurate physician payment.” If you have received these emails, it is urgent that you or your office staff respond as it will help the AMA gather accurate data on practice costs and the hours of patient care that physicians provide to support fair and accurate physician payment.

The study relies on financial experts in physician practices to complete the online financial information survey. The number of direct patient care hours is a critical component of the Medicare payment methodology. Participation will ensure that practice expenses and patient care hours are accurately reflected.

Machine Learning Algorithm Successfully Predicts Response to Antidepressant Sertraline

A machine learning program that analyzes patients’ brain imaging data along with many clinical variables of major depression—such as symptom severity—predicted whether patients with major depressive disorder would respond to the antidepressant sertraline, according to a report in AJP in Advance.

Machine learning is a type of artificial intelligence that combines a very large number of patient variables—more than a single physician could collect—to try to reliably predict an outcome of interest for individual patients. With each new piece of data, the computer “learns” to refine its prediction—hence the term “machine learning.

Maarten G. Poirot, M.S., and Henricus G. Ruhé, M.D., Ph.D., of the University of Amsterdam and colleagues used data from 229 patients with major depression who had enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a randomized controlled trial designed to evaluate variables that predict antidepressant response. Brain MRI images and a wide variety of socioeconomic, behavioral, and neuropsychiatric variables were collected before and one week after treatment with sertraline.

The researchers first tested their machine learning program on 105 patients who received sertraline and found the program could predict treatment response after 8 weeks using both pretreatment data (patient baseline variables) or early treatment data (changes after one week) significantly better than random chance; accuracy ratings ranged from 62% to 68%. The machine learning program did not generally perform as well when assessing whether patients in the placebo group responded to treatment, indicating that the prediction tool was specific to sertraline.

Moreover, the analysis was able to pinpoint which variables were most important in the prediction. “The algorithm suggested that blood flow in the anterior cingulate cortex, the area of brain involved in emotion regulation, would be predictive of the efficacy of the drug. And at the second measurement, a week after the start, the severity of their symptoms turned out to be additionally predictive,” said Ruhé in a press release. In the article, the researchers noted that since their program would likely not need input from a second session of MRI scanning to be accurate, the cost and burden on patients would be lowered in clinical practice.

The researchers concluded, “With additional external validation, these findings will contribute toward the use of predictive modeling in individualizing clinical sertraline treatment of patients with MDD.”

For related information, see the Psychiatric News article “Research Using Machine Learning in Psychiatry Expands Rapidly.”

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Researchers Identify Factors That May Predict CBT Response in Youth With OCD

The extent to which youth with obsessive-compulsive disorder (OCD) avoid situations that trigger distress and compulsions and recognize their symptoms may predict how likely they are to respond to cognitive-behavioral therapy (CBT), reports a study in the Journal of the American Academy of Child & Adolescent Psychiatry.

“[E]xtensive avoidance may mask a youth’s symptom severity at baseline … and prevent, or slow, youth’s engagement in [treatment],” wrote Robert R. Selles, Ph.D., of the University of British Columbia and colleagues. The findings point to the importance of accurately assessing these factors in youth with OCD before starting treatment and monitoring changes throughout therapy.

CBT that emphasizes exposure and response prevention—encouraging a patient to face thoughts, objects, and situations that trigger obsessions while not engaging in compulsive behavior—is a first-line approach to treating pediatric patients with OCD. To examine factors that may predict how youth with OCD respond to CBT, Selles and colleagues analyzed aggregated data from CBT trials involving 573 youth aged 7 to 19 who had been diagnosed with OCD.

As part of these trials, participants answered questions about avoidance behaviors (for example, how often they avoided doing things, going places, or being with people because of obsessions or compulsions) and insight (for example, if they believed their behaviors were reasonable) before and after receiving CBT. Participants also answered questions about the impact of OCD symptoms on daily activities; these responses were then compared with those given by their parents.

Selles and colleagues found that insight among youth before receiving CBT was not significantly related to response to CBT (defined as ≥35% reduction in Children’s Yale Brown Obsessive-Compulsive Scale score). In contrast, greater baseline avoidance and limited child recognition of impairment predicted reduced likelihood of achieving response to CBT. “Response rates steadily declined with worsening avoidance from a 71.9% (n = 69) response rate for youth with no avoidance down to a 48.3% (n = 14) response rate for youth with extreme avoidance,” they wrote. “Only 46.2% (n = 30) of youth with limited recognition of impairment (relative to the parent’s recognition) responded to treatment, in comparison to 62.3% to 66.8% of youth with either concordant or limited parent impairment recognition.” Insight and avoidance substantially improved with CBT.

“[A]voidance, insight, and parent-child concordance on impairment all appear to be variables relevant to CBT. As a result, it is recommended that clinicians assess and monitor these factors prior to and throughout treatment,” Selles and colleagues concluded. “[T]he use of more comprehensive and frequent assessment of these domains throughout the treatment process is recommended to identify reasons for lack of change in or worsening of insight and/or avoidance over treatment.”

For related information, see the Psychiatric News article “Report Highlights Alternative Treatment Options for OCD.”

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EEG Readings Not Recommended for Predicting Depression-Treatment Response

Quantitative electroencephalography (QEEG) does not appear to be a reliable tool to predict how a person with major depression will respond to treatment, according to a meta-analysis published today in AJP in Advance.

QEEG recordings—which are direct measures of the brain’s electrical waves—have been considered promising biomarkers in psychiatry. Taking a QEEG is easier and less expensive than conducting a full brain scan, another tool being used to find biomarkers for depression. But in their analysis, Alik Widge, M.D., Ph.D., of the University of Minnesota and colleagues indicated that while QEEG is better than random chance, its overall accuracy is not good enough for widespread use.

“This conclusion is likely not surprising to experts in QEEG, who are familiar with the limitations of this literature,” Widge and colleagues wrote. “It is important, however, for practicing psychiatrists to understand the limitations, given the availability of QEEG as a diagnostic test. At present, marketed approaches do not represent evidence-based care.”

Widge and colleagues analyzed 76 studies published between January 2000 and November 2017 involving QEEG readings as biomarkers for predicting response to depression treatment. Fifty-seven studies looked at medication response, 14 looked at transcranial magnetic stimulation (TMS) response, and six looked at other treatments like electrical stimulation.

Overall, the use of QEEG was about 76 percent accurate at discriminating people who would or would not respond to a given depression treatment. There were no significant differences between the treatment types (medication, TMS, other) or in the specific QEEG biomarker that was used. However, the authors found that this accuracy rating was primarily buoyed by a handful of small studies that produced strong results. The authors also noted that most of the studies analyzed did not validate their biomarker tests on independent samples, which likely means the 76 percent rating is an overestimate.

“Our results do not imply that QEEG findings are not real; they call into question the robustness and reliability of links between symptom checklists and specific aspects of resting-state brain activity. If future studies can be conducted with an emphasis on rigorous methods and reporting, and with specific attempts to replicate prior results, QEEG still has much potential,” the authors concluded.

For related information, see the Psychiatric News article “Will Imaging Guide Future Depression Care?”

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