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Background There is a knowledge gap regarding the treatment patterns of patients with major depressive disorder (MDD) who experience suicidal ideation or a suicide attempt (SI/SA). Methods Patients with SI/SA were identified from a large US-based claims database covering 84 million lives, during 1/1/2014–3/31/2020. Patients with MDD were indexed at their first diagnosis for SI/SA and followed up to 365 days. Treatment patterns were captured at the class level and included procedures of electroconvulsive therapy and transcranial magnetic stimulation, and pharmacotherapy including selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors, tricyclic antidepressants, other antidepressants, anxiolytics, hypnotics/sedatives, antipsychotics, psychostimulants, and lithium. Results There were 42,204 MDD + SI/SA patients identified. In the year prior to the index event &gt; 40% of individuals received an SSRI and more than one-third received an anxiolytic. Within 1 year following, 84.4% received ≥1 of the treatments of interest. Of those, 70.2% went on to a subsequent class-based regimen, 46.3% received a third, and 28.1% received ≥4. More than three-quarters of patients received multiple treatment classes simultaneously. SSRIs were the most common treatments during follow-up (61.9%), followed by other antidepressants (51.3%), anxiolytics (50.8%) and anticonvulsants (43.6%). Conclusions There was a large amount of variability and polypharmacy in the treatments received by MDD patients with SI/SA, and is much more complex than what has been previously observed in the general MDD population. Within one-year, many patients received four or more unique class-based regimens and most patients received treatments from multiple classes simultaneously, indicating the high unmet medical need and therapy refractoriness of this patient population.

bmc psychiatry

Treatment patterns of patients diagnosed with major depressive disorder and suicidal ideation or attempt: a U.S. population-based study utilizing real-world data