Correlation Engine 2.0
Clear Search sequence regions

  • antidepressants (5)
  • cases (2)
  • hamilton (1)
  • humans (1)
  • measures (1)
  • patient (7)
  • Sizes of these terms reflect their relevance to your search.

    In real-world pragmatic administrative databases, patient reported remission is often missing. We evaluate if, in administrative data, five features of antidepressant use patterns can replace patient-reported symptom remission. We re-examined data from Sequence Treatment Alternatives to Relieve Depression (STAR*D) study. Remission was measured using 50% reduction in Hamilton index. Pattern of antidepressant use was examined through five variables: (a) number of prior ineffective antidepressants, (b) duration of taking current antidepressant, (c) receiving therapeutic dose of the medication, and (d) switching to another medication, or (e) augmenting with another antidepressant. The likelihood ratio (LR) associated with each of these predictors was assessed in 90% of data (3329 cases) and evaluated in 10% of data (350 cases) set-aside for evaluation. The accuracy of predictions was calculated using Area under the Receiver Operating Curve (AROC). Patients who took antidepressants for 14 weeks (LR = 2.007) were more likely to have symptom remission. Prior use of 3 antidepressants reduced the odds of remission (LR = 0.771). Patients who received antidepressants below therapeutic dose were 5 times less likely to experience remission (LR = 0.204). Antidepressant that were augment or switched, almost never led to remission (LR = 0.008, LR = 0.002 respectively). Patterns of antidepressant use accurately (AROC = 0.93) predicted symptom remission. Within the first 100 days, antidepressants use patterns could serve as a surrogate measure for patient-reported remission of symptoms.


    Farrokh Alemi, Mai Aljuaid, Naren Durbha, Melanie Yousefi, Hua Min, Louisa G Sylvia, Andrew A Nierenberg. A surrogate measure for patient reported symptom remission in administrative data. BMC psychiatry. 2021 Mar 04;21(1):121

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 33663440

    View Full Text