A surrogate measure for patient reported symptom remission in administrative data

Abstract

Abstract Background In real-world pragmatic administrative databases, patient reported remission is often missing. Objective We evaluate if, in administrative data, five features of antidepressant use patterns can replace patient-reported symptom remission. Method 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). Results 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. Conclusion Within the first 100 days, antidepressants use patterns could serve as a surrogate measure for patient-reported remission of symptoms.