Computer Model Might Help Identify Patients at Risk of Not Taking Their Antidepressants

Using electronic health records, researchers have developed a computer program that can predict which patients are at risk of not taking their prescribed antidepressants with about 70% accuracy. Thestudy was published inTranslational Psychiatry.“Treatment discontinuation may reflect a range of features, from depression-associated amotivation and hopelessness to failure to perceive a benefit to concerns about cost,” wrote Melanie Pradier, Ph.D., of Harvard University and colleagues. “However heterogeneous, the consequences of treatmen t discontinuation are substantial, contributing to poor treatment outcomes and depression chronicity.”To build the computer program, Pradier and colleagues analyzed electronic health record data of adult patients aged 18 to 80 who received at least one antidepressant prescription between 2008 and 2014. The study included 51,683 patients who had a diagnosis of a depressive disorder and began treatment with one of nine common antidepressants (bupropion, citalopram, duloxetine, escitalopram, fluoxetine, mirtazapine, paroxetine, sertraline, or venlafaxine) and at least one follow-up visit 90 days or more after their first prescription.The final sample included 70,121 prescription initiations (as many patients switched antidepressants during treatment). Of these prescriptions, 23.77% were associated with a discontinuation of treatment (for example, no prescription refill and no evidence in the medical record of any nonpharmacological depressio...
Source: Psychiatr News - Category: Psychiatry Tags: antidepressant antidepressant nonadherence bupropion citalopram duloxetine escitalopram fluoxetine machine learning medication discontinuation mirtazapine paroxetine sertraline Venlafaxine Source Type: research