Literature DB >> 23108435

Factors predicting the long-term illness course in a cohort of depressed inpatients.

Christine Kuehner1, Silke Huffziger.   

Abstract

Depressed psychiatric inpatients show particularly high rates of recurrence and chronicity. To identify predictors of their long-term illness course is of high importance for tertiary prevention. We followed up 68 unipolarly depressed psychiatric inpatients 1, 6, 42, and 66 months after discharge. Outcomes included time spent in episodes, time to relapse/recurrence, and psychosocial functioning during the prospective 5.5-year interval. Predictors included demographic, clinical, cognitive vulnerability, and social support variables assessed at baseline. During the total observation period, 12 % of patients spent <50 % of time in remission, and 55 % of those remitted at post-discharge baseline suffered a relapse or recurrence. Mean psychosocial functioning was below normal functioning at all assessments. Depression levels after discharge and unsatisfactory relationships within the support network predicted all outcomes. Furthermore, early onset, longer episode duration prior to study entry, and state orientation predicted time spent in episodes. Further predictors of time to recurrence included number of previous hospitalizations and lack of psychotherapy after discharge. Long-term psychosocial functioning was additionally predicted by previous hospitalizations, duration of index episode, and state orientation. Our results demonstrate a highly recurrent and chronic illness course in depressed psychiatric inpatients. Identified predictors point to the need of treating depressive episodes to full remission. Psychological interventions should explicitly address patients' interpersonal needs and possible deficits.

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Year:  2012        PMID: 23108435     DOI: 10.1007/s00406-012-0379-7

Source DB:  PubMed          Journal:  Eur Arch Psychiatry Clin Neurosci        ISSN: 0940-1334            Impact factor:   5.270


  58 in total

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Journal:  PLoS One       Date:  2017-06-02       Impact factor: 3.240

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  9 in total

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