Predicting relapse in major depressive disorder using patient-reported outcomes of depressive symptom severity, functioning, and quality of life in the individual burden of illness index for depression (IBI-D)

AbstractBackground ts with Major Depressive Disorder (MDD) often experience unexpected relapses, despite achieving remission. This study examines the utility of a single multidimensional measure that captures variance in patient-reported Depressive Symptom Severity, Functioning, and Quality of Life (QOL), in predicting MDD relapse. s te data from remitted patients at the completion of 12 weeks of citalopram in the STAR*D study were used to calculate the Individual Burden of Illness index for Depression (IBI-D), and predict subsequent relapse at six (n=956), nine (n=778), and twelve months (n=479) using generalized linear models. s sive Symptom Severity, Functioning, and QOL were all predictors of subsequent relapse. Using Akaike information criteria (AIC), the IBI-D provided a good model for relapse even when Depressive Symptom Severity, Functioning, and QOL were combined in a single model. Specifically, an increase of one in the IBI-D increased the odds ratio of relapse by 2.5 at 6 months (β=0.921±0.194, z=4.76, p<2×10−6), by 2.84 at 9 months (β=1.045±0.22, z=4.74, p<2.2×10−6), and by 4.1 at 12 months (β=1.41±0.29, z=4.79, p<1.7×10−6). tions eport poses a risk to measurement precision. Using highly valid and reliable measures could mitigate this risk. The IBI-D requires time and effort for filling out the scales and index calculation. Technological solutions could help ease these burdens. The sample suffered from attrition. Separate analysis of dropouts would be helpful. sions orating patient-reported outcomes of Functioning and QOL in addition to Depressive Symptom Severity in the IBI-D is useful in assessing the full burden of illness and in adequately predicting relapse, in MDD.