BACKGROUND: Clinical characteristics of depression, age at illness onset, medical burden, disability, cognitive impairment, lack of social support, and poor living conditions may influence the course of depression. This study investigates the timetable of recovery and the role of the above factors in predicting recovery in elderly patients with major depression. METHODS: Recovery was studied in 63 elderly (age >63 years) and 23 younger patients with depression who were followed up for an average of 18.2 months (SD, 13.1 months) under naturalistic treatment conditions. Diagnosis was assigned according to Research Diagnostic Criteria after administration of the Schedule for Affective Disorders and Schizophrenia. The Longitudinal Follow-up Interval Examination was used to identify recovery. RESULTS: The recovery rate of depressed elderly patients was similar to that of younger depressed patients. In the elderly patients, age, antidepressant treatment, age at onset, and chronicity of episode were significantly associated with time to recovery since entry. Among these parameters, late age at onset was the strongest predictor of slow recovery. In younger patients, long time to recovery was predicted by weak social support, younger age, cognitive impairment, and low intensity of antidepressant treatment. In the elderly, the intensity of antidepressant treatment began to decline within 16 weeks from entry and approximately 10 weeks prior to recovery. CONCLUSIONS: These findings challenge the view that geriatric depression has a worse outcome than depression in younger adults. However, depressed patients with onset of first episode in late life may be at higher risk for chronicity. Antidepressant treatment prescribed by clinicians may decline prior to recovery despite evidence that high treatment intensity is effective in preventing relapse.
BACKGROUND: Clinical characteristics of depression, age at illness onset, medical burden, disability, cognitive impairment, lack of social support, and poor living conditions may influence the course of depression. This study investigates the timetable of recovery and the role of the above factors in predicting recovery in elderly patients with major depression. METHODS: Recovery was studied in 63 elderly (age >63 years) and 23 younger patients with depression who were followed up for an average of 18.2 months (SD, 13.1 months) under naturalistic treatment conditions. Diagnosis was assigned according to Research Diagnostic Criteria after administration of the Schedule for Affective Disorders and Schizophrenia. The Longitudinal Follow-up Interval Examination was used to identify recovery. RESULTS: The recovery rate of depressed elderly patients was similar to that of younger depressedpatients. In the elderly patients, age, antidepressant treatment, age at onset, and chronicity of episode were significantly associated with time to recovery since entry. Among these parameters, late age at onset was the strongest predictor of slow recovery. In younger patients, long time to recovery was predicted by weak social support, younger age, cognitive impairment, and low intensity of antidepressant treatment. In the elderly, the intensity of antidepressant treatment began to decline within 16 weeks from entry and approximately 10 weeks prior to recovery. CONCLUSIONS: These findings challenge the view that geriatric depression has a worse outcome than depression in younger adults. However, depressedpatients with onset of first episode in late life may be at higher risk for chronicity. Antidepressant treatment prescribed by clinicians may decline prior to recovery despite evidence that high treatment intensity is effective in preventing relapse.
Authors: Lauren Waterman; Sarah T Stahl; Daniel J Buysse; Eric J Lenze; Daniel Blumberger; Benoit Mulsant; Meryl Butters; Marie Anne Gebara; Charles F Reynolds; Jordan F Karp Journal: Depress Anxiety Date: 2016-09-16 Impact factor: 6.505
Authors: Patrick J Brown; Melanie M Wall; Chen Chen; Morgan E Levine; Kristine Yaffe; Steven P Roose; Bret R Rutherford Journal: J Gerontol A Biol Sci Med Sci Date: 2018-09-11 Impact factor: 6.053
Authors: Jennifer I Lissemore; Apoorva Bhandari; Benoit H Mulsant; Eric J Lenze; Charles F Reynolds; Jordan F Karp; Tarek K Rajji; Yoshihiro Noda; Reza Zomorrodi; Etienne Sibille; Zafiris J Daskalakis; Daniel M Blumberger Journal: Neuropsychopharmacology Date: 2018-05-17 Impact factor: 7.853
Authors: Carol Dillon; Ricardo F Allegri; Cecilia M Serrano; Mónica Iturry; Pablo Salgado; Frank B Glaser; Fernando E Taragano Journal: Neuropsychiatr Dis Treat Date: 2009-10-12 Impact factor: 2.570