Change in functioning outcomes as a predictor of the course of depression: a 12-month longitudinal study
Por:
Forero, CG, Olariu, E, Alvarez, P, Castro-Rodriguez, JI, Blasco, MJ, Vilagut, G, Perez, V, Alonso, J, Barbaglia, G., INSAyD Investigators
Publicada:
1 ago 2018
Resumen:
Functioning is a necessary diagnostic criterion for depression, and thus routinely assessed in depressive patients. While it is highly informative of disorder severity, its change has not been tested for prognostic purposes. Our study aimed to analyze to what extent early functioning changes predict depression in the mid-term.
Longitudinal study (four occasions: baseline, 1, 3, and 12 months) of 243 patients with depressive symptomatology at three different services (primary care, outpatients, and hospital). Functioning was assessed on the first three occasions using the Global Assessment of Functioning (GAF), the WHODAS-2.0, and a self-reported functioning (SRF) rating scale. Growth mixture modeling of initial assessments served to estimate individual person-change parameters of each outcome. Person-growth parameters were used as predictors of major depressive episode at 12 months in a logistic regression model, adjusted by sex, age, healthcare level, and depression clinical status at third month. Predictive accuracy of all measures was assessed with area under the receiver operating curve (AUC).
Of the 179 patients who completed all assessments, 58% had an active depression episode at baseline and 20% at 12 months (64% non-recoveries and 36% new onsets). Individual trends of change in functioning significantly predicted patient depression status a year later (AUC(WHODAS) = 0.76; AUC(GAF) = 0.92; AUC(SRF) = 0.93).
Longitudinal modeling of functioning was highly predictive of patients' clinical status after 1 year. Although clinical and patient-reported assessment had high prognostic value, the use of very simple patient-reported outcome measures could improve case management outside specialized psychiatric services.
Filiaciones:
Forero, CG:
CIBER Epidemiol & Salud Publ CIBERESP, Madrid, Spain
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Olariu, E:
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Alvarez, P:
Inst Neuropsychiat & Addict INAD, Parc Salut Mar, Barcelona, Spain
Castro-Rodriguez, JI:
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Inst Neuropsychiat & Addict INAD, Parc Salut Mar, Barcelona, Spain
Blasco, MJ:
CIBER Epidemiol & Salud Publ CIBERESP, Madrid, Spain
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Vilagut, G:
CIBER Epidemiol & Salud Publ CIBERESP, Madrid, Spain
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Perez, V:
Inst Neuropsychiat & Addict INAD, Parc Salut Mar, Barcelona, Spain
CIBER Salud Mental CIBERSAM, Madrid, Spain
Alonso, J:
CIBER Epidemiol & Salud Publ CIBERESP, Madrid, Spain
IMIM Inst Hosp Mar Invest Med, Hlth Serv Res Unit, Carrer Doctor Aiguader 88,Edifici PRBB, Barcelona 08003, Spain
Univ Pompeu Fabra UPF, Dept Expt & Hlth Sci, Barcelona, Spain
Barbaglia, G.:
Agència de Salut Pública de Barcelona, Barcelona, Spain
Green Accepted
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