Personalizing Antidepressants with a Decision Tool Improves Adherence at 8 Weeks

Finding the right antidepressant for a patient has historically involved a great deal of trial and error. The first medication prescribed works well for roughly half of patients with major depressive disorder. The rest cycle through alternatives, each failed trial adding weeks of suffering and increasing the risk they will give up on treatment altogether. Personalized prescribing - matching drugs to individual patient characteristics from the start - has been a goal for decades. A randomized trial published in JAMA tests whether a specific tool for doing this actually helps.

The tool is called PETRUSHKA. It is a web-based clinical decision-support system that combines clinical and demographic predictors with patient treatment preferences to generate individualized antidepressant recommendations. The trial compared its use against usual care in patients with major depressive disorder.

What the trial found

The primary outcome was medication adherence at eight weeks: were patients still taking their prescribed antidepressant? More patients in the PETRUSHKA group were. Secondary outcomes included depressive and anxiety symptoms at 24 weeks, and both improved more in the PETRUSHKA group than in usual care.

Those are encouraging results. Adherence at eight weeks is clinically meaningful - dropping out of antidepressant treatment before an adequate trial is one of the most common reasons treatment fails. A tool that keeps patients engaged longer has real potential value.

The caveats are significant

The trial's authors are notably candid about its limitations, and those limitations matter. The trial lacked a double-blind design. In clinical research on treatments with subjective outcomes - including mood and symptom reporting - blinding is important because both patients and clinicians who know which arm of a study they are in tend to report results differently. Without blinding, it is difficult to separate the effect of PETRUSHKA's recommendations from the effect of patients and doctors simply paying more attention to treatment choices.

The second major limitation is missing data. A large proportion of data points were absent from the final analysis. Missing data in clinical trials is not unusual, but a large amount of it forces researchers to make assumptions about why it is missing and what the absent responses might have contained. Those assumptions can substantially affect whether results hold up - or don't.

The trial's own summary notes explicitly: "lack of a double-blind design and the large amount of missing data limit the validity of these results." That is an unusually direct statement of uncertainty in a published trial abstract, and it reflects the JAMA editorial standards that require transparent reporting of limitations.

The broader context: why personalized prescribing is hard

Major depressive disorder is not one condition but a heterogeneous syndrome. Two patients who both meet diagnostic criteria may differ substantially in their biology, in the social and psychological factors driving their symptoms, and in how they respond to specific medications. That heterogeneity is why no single antidepressant works for everyone and why the field has been searching for biomarkers, genetic predictors, and clinical algorithms that could match patients to drugs more precisely.

PETRUSHKA takes a pragmatic approach: it uses information that is already collected in routine clinical encounters - demographics, symptom severity, prior treatment history - combined with stated patient preferences about side effects and other factors, and runs that through a predictive model to generate a ranked list of treatment options. No blood tests, no genetics, no specialist referral required.

Whether that approach provides enough signal to meaningfully improve outcomes over what a thoughtful clinician would prescribe anyway is exactly the question this trial was designed to answer. The results suggest it may help - particularly for adherence - but the trial's design limitations mean the field will need larger, better-controlled studies before PETRUSHKA or tools like it can be broadly recommended.

The corresponding author is Andrea Cipriani, MD, PhD, at the University of Oxford (andrea.cipriani@psych.ox.ac.uk). The study was published March 4, 2026 in JAMA.