TY - JOUR
T1 - Prevalence and prediction of dropout during depression treatment in routine outpatient care
T2 - an observational study
AU - van Dijk, D. A.
AU - Deen, M. L.
AU - van den Boogaard, Th. M.
AU - Ruhé, H. G.
AU - Spijker, J.
AU - Peeters, F. P. M. L.
N1 - Funding Information: Dr. Ruhé had received speaking fees from Janssen and Lundbeck and grants from ZonMW, Horizon 2020 and Hersenstichting. Prof. Peeters receives book royalties from Boom Publishers, Bohn Stafleu van Loghum, and Hogrefe Publishing Group, receives research grants from Zon-MW and the Mitialto Foundation, and received financial compensation as an independent symposium speaker for Janssen-Cilag, and SCEM. The other authors declare that they have no conflict of interest. Publisher Copyright: © 2022, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds [OR = 0.78, 95% CI = (0.70–0.86), p < 0.001]. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness.
AB - Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds [OR = 0.78, 95% CI = (0.70–0.86), p < 0.001]. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness.
KW - Cohort studies
KW - Depressive disorder, major
KW - Dropout
KW - Outpatients
KW - Treatment outcome
UR - http://www.scopus.com/inward/record.url?scp=85139961811&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s00406-022-01499-1
DO - https://doi.org/10.1007/s00406-022-01499-1
M3 - Article
C2 - 36253582
SN - 0940-1334
VL - 273
SP - 1151
EP - 1161
JO - European archives of psychiatry and clinical neuroscience
JF - European archives of psychiatry and clinical neuroscience
IS - 5
ER -