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Section title 	Original Research Article	
Issue ID	000	
Citation line	Dement Geriatr Cogn Disord	
DOI: 10.1159/000	458148	
Article title	Trajectories of Depression in Late Life: A One-Year Follow-Up Study	
Subtitle		

Author(s)
Example: 	Initials
S.-J.	Given name
Seo-Jin	Surname/Collaboration
Park	
411610	T.	Tom	Borzaa,b	
186561	K.	Knut	Engedalc	
305443	S.	Sverre	Bergha,c	
297619	J.S.	Jūratė Šaltytė	Bentha,d,e	
245348	G.	Geir	Selbæka,c,f	

Institute(s)	aCenter for Old Age Psychiatric Research, Innlandet Hospital Trust, Ottestad, bInstitute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, cNorwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, dInstitute of Clinical Medicine, Campus AHUS, University of Oslo, Oslo, eHØKH, Research Center, Akershus University Hospital, Lørenskog, and fDepartment of Nursing Science, Faculty of Medicine, University of Oslo, Oslo, Norway	

Additional information	Trial registration: ClinicalTrials.gov NCT01952366.	

Received		
Accepted	Accepted: January 24, 2017	
Revised		
Published Online	Published online: ■■■	

Name for correspondence	Tom Borza, MD	
Full address	Center for Old Age Psychiatric Research	
	Innlandet Hospital Trust, Sanderud, PO Box 68	
	NO–2312 Ottestad (Norway)	
(Tel., E-Mail)	E-Mail tom.borza@sykehuset-innlandet.no	

Keywords	Depression
Elderly
Dementia
Cognitive impairment
Prospective study
Trajectories
Clinical assessment	

Abbreviations		

Abstract	
Aims: To investigate the prognosis of depression in late life (DLL) in terms of the course of depression over 1 year and assess clinical factors related to the prognosis. Methods: We performed an observational, multicenter, longitudinal study of 160 patients aged ≥60 years who were admitted to inward treatment of DLL. The patients were followed with 3 assessments: at inclusion (T0), at discharge from the hospital (T1), and after 1 year (T2). Growth mixture modeling was applied to identify patient classes following distinct trajectories of the Montgomery-Åsberg Depression Rating Scale (MADRS) score. Two regression models were estimated to assess clinical factors for the trajectories and for a clinical assessment of the depression course between T1 and T2. Results: Two trajectory classes were identified: one with higher and one with lower MADRS scores. Not being in remission at T1 and a longer hospital stay were associated with higher odds of being in the trajectory class with more severe depression. Early-onset depression (EOD) was associated with higher odds of being in a group with a poorer clinical course between T1 and T2. Conclusion: EOD and not being in remission at discharge were important negative prognostic factors for DLL.	

Body
Introduction
Depression in late life (DLL) has been associated with considerable functional disability, increased prevalence of dementia, higher risk for suicide, and overall mortality [1–6]. Thus, DLL can have vast consequences for the individual patient and society, posing a global challenge [7].
In Norway, most patients with DLL are treated by general practitioners. However, patients who do not respond to treatment by general practitioners and those with high medical complexity, diminished ability to take care of themselves, complicated by dementia, or suffering from severe depression (e.g., depression with psychosis or a high risk of suicide of depression) can be referred for treatment to departments of old age psychiatry in specialist health care as inpatients. Such departments of old age psychiatry are well established throughout the country and typically offer services to catchment areas with 15,000–30,000 older people above the age of 65 years. The number of beds at these departments varies, but is recommended to be between 0.8 and 1.2 per 1,000 older people [8].
The prognosis of DLL is often poor, with reports of 50% or even more patients showing a chronic or relapsing course [9–11]. In a systematic review of comparative studies on the prognosis of DLL compared to depression in middle age, Mitchell and Subramaniam [12] reported similar response and remission rates among the groups, but among patients in late life, relapse rates seemed to be higher. Later studies have confirmed these findings [10].
Patients with bipolar disorder and dementia have generally been excluded from observational longitudinal studies on the course of DLL [9, 13–15]. Symptoms of depression and dementia may overlap and often should be considered together [6, 16–21]. Hence, observational longitudinal studies on the outcome of DLL in different clinical settings are still warranted [4, 10, 22].
Different patients with DLL respond differently to treatment [23] and can also show heterogeneous courses after a depressive episode [13]. To plan for better treatment and follow-up, clinicians can be guided by risk factors for poorer outcomes of DLL. More tailored interventions for patients with DLL are emerging and are needed to improve treatment [10].
Growth mixture modeling in longitudinal studies provides an opportunity to identify classes of patients following a distinct trajectory over time. Previous studies have reported results from trajectory analyses in different settings for DLL [1, 13–15, 24–29]. However, few such studies have included hospitalized older patients in specialist health care services for old age psychiatry.
We carried out a multicenter observational study of hospitalized patients aged ≥60 years, also including patients with dementia and bipolar disorder. Our main aims were to investigate their prognoses in terms of the course of depression over 1 year and to assess clinical factors related to the prognoses. As part of the analysis, we aimed to identify possible classes of patients following distinct trajectories (using growth mixture modeling) as well as clinical parameters characterizing these classes. Based on previous research, we hypothesized that the presence of dementia and first onset of depression early in a patient’s lifetime (<60 years) would be associated with worse outcomes for the course of depression [30, 31].
Patients and Methods
Design
PRODE (Prognosis of depression in the elderly) is a Norwegian observational, multicenter, longitudinal study. Nine departments of old age psychiatry participated in the study. Health professionals working in these departments collected data for this paper from December 2009 to March 2014. Standardized assessment scales were used. Assessors received standardized training prior to the study period as well as twice yearly during the study period in order to secure reliable data.
Patients
Patients were eligible for inclusion if they were aged ≥60 years and had been referred to inward treatment for depression in an old age psychiatry department within specialist psychiatric health services. Patients with life-threatening diseases as well as patients with severe aphasia were excluded from the study.
The PRODE study sample’s procedures, attrition, and representativeness have been described previously [30]. This particular study included 160 inpatients.
Measurements
The patients were assessed at 3 time points: at inclusion into the study (T0), i.e., as soon as possible after admittance to the department of old age psychiatry, at discharge from the hospital (T1), and 1 year later (between 11 and 15 months after inclusion) (T2). Discharge from the department of old age psychiatry was determined according to the clinical procedure of the individual department and not according to a standard procedure. Length of hospital stay was defined as the number of days from T0 to T1. Depression and dementia were diagnosed according to the research criteria from the Tenth Revision of the International Classification of Diseases and Related Health Problems (ICD-10) [32]. The diagnoses of depression and dementia were established both during the hospital stay and at T2.
Information about the patients’ prior depressive and psychiatric history, including their age at the onset of their first depressive episode, and their demographic characteristics was obtained from structured interviews with the patients and family carers at T0. In line with other studies, age at onset of the first depressive episode in the patients’ lifetime was dichotomized to 59 or earlier (early-onset depression [EOD]) and 60 or later (late-onset depression [LOD]) [33]. Marital status was dichotomized to not single (including patients who were married or living with a partner) and single (including singles, widow[er]s, or patients who were divorced or separated).
The severity of depression was rated with the Montgomery-Åsberg Depression Rating Scale (MADRS) at the study’s junctures. The MADRS consists of 10 items, each rated from 0 points (no symptoms) to 6 points (severe symptoms), with a higher sum score denoting more severe depression [34]. The MADRS has shown good validity in patients with DLL [17, 35, 36].
We used the Mini-Mental State Examination (MMSE) (score range 0–30) to evaluate patients’ cognitive function at the study’s 3 junctures. A higher sum score indicates better cognition [37].
The patients’ physical health was rated using the General Medical Health Rating Scale (GMHR), a 4-point (excellent, good, fair, and poor) global rating scale for overall medical comorbidity. The GMHR takes into account each patient’s number of general medical conditions, the severity of those conditions, and the patient’s use of medications [38]. The patients were assessed with the GMHR at T0 and T2. For the analyses, the GMHR was dichotomized to good (excellent/good) and poor (fair/poor).
The patients’ use of medications was classified according to the Anatomical Therapeutic Chemical (ATC) classification system [39].
Definitions
In line with results from previous studies and based on recommendations, we defined remission as a score of <10 on the MADRS [40, 41]. For the 13 patients with missing MADRS score at T1, we were able to collect supplemental information from case notes, the Cornell Scale for Depression in Dementia [42], and the Clinical Global Impression severity and improvement scales [43] to evaluate whether the patients were in remission.
It was possible to carry out a clinical examination in 121 of the 146 patients assessed at T2. For the remaining 25, data were collected by telephone interview with the patients, relevant case notes from the local study center, and supplemental information from family carers and professional caregivers.
At T2, the patients’ course of depression between T1 and T2 was rated by the participating centers using all available information. The rating was based on outcomes in Cole and Bellavance’s meta-analysis of longitudinal studies on the prognosis of DLL [9]. We categorized the outcomes into 3 course groups: (1) good = no signs of relapse (return of depression within 4 months of remission) or recurrence (development of a new depressive episode after 4 months of remission) [40], (2) residual = reports of residual symptoms, and (3) poor = rating of continuous depression or reports of relapse or recurrence that required hospital admission or involved a suicide attempt.
Subsequently, the course of depression during the follow-up period was assessed in 2 complementary analyses: (1) Identification of possible classes of patients following distinct but unobserved trajectories (based on MADRS scores on the assessments at T0, T1, and T2) and clinical variables characterizing the patients within each class (n = 160). (2) Categorization of the patients into 3 course groups (based on the clinical assessment of the period T1–T2 that took place at T2) and identification of predictors for the course groups (n = 146).
Treatment
All patients received treatment, but were not assigned to a particular treatment protocol. The treatment depended on the patients’ needs as well as practices at the local study center. At T0, T1, and T2, psychotropic drugs were used daily by 91.3, 95.0, and 93.7% of patients, respectively. The corresponding figures for use of antidepressants were 71.3, 83.8, and 82.5%, and for use of antipsychotics 23.8, 26.3, and 24.0%, respectively. Virtually all patients received psychotherapy, including support-based therapy, during their hospital stays, and 50% received psychotherapeutic treatment between T1 and T2. Electroconvulsive therapy (ECT) was given to 25.6% during their hospital stay. Among those who completed the follow-up period, 12.5% received ECT treatment between T1 and T2 (as maintenance treatment or as a new series).
Ethical and Legal Considerations
The participating patients and caregivers were given oral and written information about the study, and they subsequently gave written consent to participate. For patients without the capacity to give consent, their next of kin had to give written consent on behalf of the patient. The study was approved by the Regional Committee of Medical Research Ethics and the Privacy and Data Protection Officer at Oslo University Hospital. Trial registration: ClinicalTrials.gov NCT01952366.
Statistics
Among patients with <50% missing values on the MMSE scale, missing values on MMSE items were imputed with a randomly drawn number from empirical distribution for each relevant item. Subsequently, we imputed MMSE items for 3 patients at T0, 2 patients at T1, and 5 patients at T2.
Demographic and clinical characteristics at T0, T1, and T2 were presented as mean ± standard deviation (SD) or percentage, as appropriate. The independent groups (e.g., completers versus noncompleters) were compared by independent-samples t test or χ2 test. The comparison of dichotomous variables at different assessment points was carried out using the McNemar test.
Trend in the MADRS score through the 3 assessment points was assessed by a linear mixed model with fixed effects for the time component up to second-order and random effects for study centers and patients nested within the center. Such a model correctly accounts for possible intraclass correlations due to both repeated measurements and center belonging.
Next, a growth mixture model was estimated with an attempt to identify 2 or more unobserved classes of patients, each following a distinct trajectory. The Akaike information criterion was applied when identifying trajectories. In addition, we aimed at an average class probability >0.7 and nonoverlapping 95% confidence intervals (CIs) for trajectories. A logistic regression model with class membership as its outcome was estimated to identify characteristics of the patients describing the classes.
The course of depression from T1 to T2 was defined as a categorical variable with the categories good, residual, and poor. An ordinal regression model was estimated to assess possible predictors for the course of depression from T1 to T2.
For both regression models the covariates (MMSE score [at T0], diagnosis of dementia [at T1], GMHR score, age at onset of the first lifetime depressive episode, remission at T1, depression with psychosis, bipolar disorder, age, sex, marital status, years of education, and length of hospital stay/follow-up days between T1 and T2) were selected based on previous studies, their clinical value, the uniqueness of the present dataset, and the number of variables our regression models could handle. Treatment variables such as use of ECT and psychotropic drugs were not included in the analyses. Correlation analysis among relevant covariates and between each covariate and the dependent variables was performed. Age (higher) at onset of the first depressive episode correlated strongly with (no) previous depressive episode(s) (Pearson correlation coefficient = 0.63). The effects of previous depressive episode(s) and age at onset of the first depressive episode can thus be similar, and in the present analyses we used only age at onset of the first depressive episode, as this variable showed the strongest correlation with both outcomes.
No intracenter correlation was present in the outcomes. The parallel odds assumption in the ordinal regression model was assessed by a test of parallel lines, and no violations were detected. Both regression models were reduced by applying the Akaike information criterion, where a smaller value implies a better model. The results were presented as odds ratios with the corresponding 95% CI. Trajectories were presented graphically.
Statistical analyses were performed in STATA version 14 and SPSS version 22. Results with p values <0.05 were considered statistically significant.
Results
Of the 160 patients included in the study, 116 (72.5%) were female. The mean age for all participants was 76.1 ± 6.8 years. The mean ages for women and men were 76.5 ± 7.1 and 75.1 ± 6.0 years, respectively (p = 0.231). Further demographic and clinical characteristics of the study population at T0, T1, and T2 are displayed in Table 1. At T1, 50.6% of the patients were in remission. At T2, 7 patients had died and 7 were unwilling to be assessed. Compared to the completers, the patients who did not complete the follow-up period had worse physical health, were more cognitively impaired, were more often single at T0, more often had bipolar disorder, and were less likely to be in remission at T1. Figure 1 describes the attrition from T0 to T2.
At T2 as compared to T0, more patients were in the GMHR group of very poor/poor (63.0 vs. 49.3%, p = 0.003), more patients resided in nursing homes (11.0 vs. 0.7%, p < 0.0001), and more patients had a diagnosis of dementia (15.8 vs. 8.8%, p = 0.003).
We were able to obtain an MADRS score for 157 patients at T0, 147 patients at T1, and 126 patients at T2. According to an unadjusted linear mixed model, the mean MADRS score was 26.0, with a 95% CI of 24.7–27.4 at T0. There was a decrease in the MADRS score to 10.5 (8.8–12.3) at T1 (p < 0.001), but there was no significant change from T1 to T2 (p = 0.503), with the mean MADRS score at T2 equal to 11.2 (9.3–13.0).
There were no differences in the mean MADRS scores at T0 (p = 0.772), T1 (p = 0.111), and T2 (p = 0.279) between the EOD and LOD groups. Fewer patients were in remission at T1 in the EOD group compared to the LOD group (p = 0.013).
Trajectory Classes
Growth mixture modeling of the MADRS scores resulted in 2 trajectory classes: class A and class B (Fig. 2). Class A (n = 127) had a high MADRS score at T0, which decreased towards T1 and stayed low at T2. For class B (n = 33), the MADRS score was higher at T0 than it was among class A. The score in class B also declined between T0 and T1, but to a lesser extent, and had increased at T2. The CIs of the trajectory curves did not overlap, and the average class probabilities were >0.70, indicating well-separated classes.
Table 2 presents the results of the logistic regression model with trajectory class as the outcome variable. In the bivariate analysis, poorer physical health (p = 0.037), not being in remission at discharge from hospital (p = 0.001), and a longer hospital stay (p = 0.003) were associated with higher odds of belonging to class B (most severe depression). In the multivariable analysis, remission status and length of hospital stay were associated with class membership (p = 0.011 and p = 0.005, respectively).
Analyses of Course Groups during T1 and T2
Based on the clinical assessment of the patients’ course of depression and depressive symptoms between T1 and T2, 51 of the 146 patients (34.9%) had a good course, 43 (29.4%) had residual symptoms, and 52 (35.6%) had a poor course.
Table 3 presents the results of the ordinal regression model with course groups (good, residual, poor) between T1 and T2 as the outcome variable. In the bivariate analysis, age <60 years at onset of the first depressive episode (p = 0.026), not being in remission at discharge from the hospital (p = 0.024), and a shorter interval between T1 and T2 (p = 0.006) were associated with higher odds of being in a group with a poorer outcome. In the multivariable analysis, age <60 years at onset of the first depressive episode and a shorter interval between T1 and T2 were associated with higher odds of being in a group with a poorer course (p = 0.017 and p = 0.003, respectively).
Combining Growth Mixture Modeling and Course Group Analysis
Of the 146 patients who completed the study, 50 (34.2%) were both members of trajectory class A and were in the course group with a good course between T1 and T2. Correspondingly, 19 patients (13.0%) were both members to trajectory class B and were in the course group with a poor course between T1 and T2. We compared the patients who were both members of class A and were in the good course group between T1 and T2 to those who were both members of class B and were in the poor course group between T1 and T2 for relevant covariates. In those who were both members to class B and were in the poor course group between T1 and T2, more patients had dementia (p = 0.025) and were not in remission at T1 (p = 0.001).
Discussion
Our hypothesis that EOD was associated with a worse outcome for the course of DLL was partly confirmed. Dementia was not determined to be a significant factor for the course of DLL.
Trajectories
We identified 2 distinct trajectory classes based on MADRS scores over the course of the study period: 1 with higher and 1 with lower MADRS scores. The majority of patients were in the class with lower MADRS scores. Other studies assessing trajectory classes have identified other types of trajectories for DLL. Direct comparisons to other studies are somewhat difficult to make for several reasons. These earlier studies often included assessments at fixed time points and more regular assessments than the design of the present study permitted. For example, McCusker et al. [24] applied hierarchical clustering, a method different from ours, using 4 assessments (at inclusion and 3, 6, and 12 months after inclusion) with the Hamilton Rating Scale for Depression to group depressed medical inpatients into 3 trajectories according to severity (minimal, mild, and moderate/severe). The study by McCusker et al. found that the course of depressive symptoms was largely stable within the 3 different trajectories. In a recent article, Hybels et al. [13] studied trajectory recoveries in older patients with major depression and followed them for up to 3 years. The authors found 4 trajectory classes: a quick recovery class, a persistent moderate symptom class, a persistent high symptom class, and a slow recovery class. Both these studies and others [14, 27] excluded patients with dementia and significant cognitive impairment. Additionally, the study by Hybels et al. is one of just a very few studies on DLL trajectories that also included inpatients from specialist health care services of old age psychiatry.
Clinical Factors and the Prognosis of Depression
Not being in remission at discharge was the factor most strongly associated with belonging to the trajectory class with most severe depression as measured by the MADRS; it was also a significant predictor for being in a course group with poorer outcome between T1 and T2 in the bivariate ordinal analysis, but not in the multivariable analysis. Our findings concur with those of others, highlighting the importance of remission for further outcomes during the course of depression [40, 44]. It can be challenging for patients with DLL to attain remission due to psychiatric and medical comorbidities, and an inability to attain remission from DLL has been associated with a greater risk of relapse and recurrence, more functional disability, and worse quality of life [26, 44, 45]. There has been debate in the literature on the most appropriate way to define remission. According to some authors, remission should be based on the absence of the diagnostic criteria for depression, but cutoff values on various depression rating scales are also used [40, 45]. Our definition of remission (MADRS score <10) was in line with the results and recommendations from previous studies [40, 41]. Approximately 50% of the patients we studied were in remission at discharge from the hospital under this definition. A broader or narrower definition might have an impact on our results, but it is not clear as to how, particularly as remission was a covariate and not an outcome variable in our study.
A longer hospital stay was associated with higher odds of belonging to the trajectory class with most severe depression. This may be due to the fact that patients who stayed longer in the hospital might have had depression that was more difficult to treat. At the 1-year follow-up, less follow-up days between discharge and the 1-year follow-up were associated with being in a course group with poorer outcome. This may reflect that patients with longer stay in the hospital, and correspondingly having a depression more difficult to treat, also had a shorter interval between discharge and the 1-year follow-up.
The patients were not assessed with the MADRS between T1 and T2. However, we complemented the growth mixture analysis with a comprehensive clinical assessment of the period between T1 and T2. We used a definition of relapse and recurrence with high clinical relevance in that relapse and recurrence had to include admission to a hospital or a suicide attempt. Even though our definition was stringent, over one-third of the patients had evidence of a relapse or recurrence or were rated as continuously ill from T1 to T2, highlighting the poor prognosis of DLL [9–11].
We used the same clinical covariates in the ordinal regression model with depression course groups from T1 to T2 as the outcome, as in the regression model for the characteristics of the trajectory classes. The former analysis showed that age <60 years at onset of the first depressive episode in the patients’ lifetime was associated with a poorer outcome between T1 and T2. This finding is in line with other studies that reported poorer outcomes in elderly patients with EOD [10, 33]. Other papers found that the association between age at the patients’ first depressive episode and outcome was less straightforward, e.g., that patients with LOD can be linked to greater medical comorbidity, which further complicates the picture [12]. In the present study, age at the first onset of depression was not significantly associated with trajectory class membership. Possible explanations for the inconsistent findings regarding age at the first onset of depression as a covariate may include differences in the observation period and estimation of different regression models. The trajectory analysis was based on MADRS scores, and we found no significant differences in the mean MADRS scores at the 3 time points between the LOD and the EOD groups. The ordinal model may have identified and put more emphasis on different aspects of the depression course than the trajectory class model did. Some EOD patients might have had a more complicated and severe depression in terms of suicidality than the MADRS score indicated. Only 1 of the 10 items on the MADRS assesses suicidality. Other studies have found EOD to be associated with more severe depression, including more suicidality [31, 33].
Cognition
The present study included patients with diagnoses of dementia and cognitive impairment. During T0 and T1, a diagnosis of dementia was established in 8.8% of the patients. There was a high conversion rate to dementia from T1 to T2, and at T2, 15.8% of the patients had dementia. Clinicians should therefore be aware of dementia in patients with DLL during the first year after their admission to hospitals’ specialist health care services [2, 4, 16, 19]. Impairment of executive function has been associated with poorer treatment response to medications and a higher risk of relapse and recurrence [4]. Cognitive impairments as measured by the MMSE and dementia were not significant covariates in the present study’s regression models. However, there was a trend revealing that dementia was associated with membership in the trajectory class with worse outcome. The sample size could have been too small to show any significant effect of dementia in the regression models. When we compared the patients who were members of both the best trajectory class and the best course group between T1 and T2 with those who experienced poorest outcomes in both models, there were significantly more patients with dementia among those with the poorest outcomes.
Two previous trajectory class analyses of depressive symptoms among older primary care patients have included cognitive measures in the analyses, but did not find cognition to be a significant predictor of trajectory class membership [15, 26]. Cui et al. [26] also included a specific assessment of executive functions in their study. They argued that the lack of an association between executive dysfunction and trajectory class could be related to a heterogeneous study population in primary care.
Limitations and Strengths
The present study has limitations. Depressive symptoms can fluctuate and the study could have assessed them more often, particularly between T1 and T2. Additionally, the study could have included more assessments for the value of the growth mixture analysis. Some patients had a missing MADRS score at T2 due to death or inability to participate. The trajectory analysis might have resulted in a larger class with higher MADRS scores had all values been available, as it is not unlikely that these patients would have contributed with high MADRS scores.
As described in the statistics section, we chose to include only age at onset of the first depressive episode and not the number of previous depressive episodes in the analyses, as the variables were highly correlated (Pearson correlation coefficient = 0.63) and age at onset of the first depressive episode correlated most strongly with the outcomes. It can be difficult to distinguish between the effect attributable to the number of previous depressive episodes and that attributable to age at the first depressive episode. However, LOD is not the same as a first depressive episode, and even in old age a first depressive episode can have a better prognosis than recurrent depression. In our study, a patient with a first depressive episode would always be categorized as LOD, which could have influenced the results [33, 46].
Few patients had dementia, bipolar disorder, or depression with psychosis, and the sample size could have been too small to show any significant effect of these variables. The study was carried out in a multicenter setting with many different assessors, which could have influenced the results. In addition, the assessors were not blinded for ratings in the study. However, the assessors participated in a structured training on the use of the assessment scales, both prior to the study period as well as twice yearly during the study period. There may have been differences in when the patients were discharged from the hospital at the individual study centers. However, there was no clustering in the MADRS scores due to study centers.
The strengths of this study are its naturalistic and longitudinal design and the use of well-established assessment scales. It included many vulnerable patients from everyday practice in departments of old age psychiatry and will have high clinical relevance in such settings. Missing data can be a challenge for longitudinal studies of frail patients. However, our linear mixed and growth model allowed analyses with all the available information, also from dropouts. We used complementary analyses to thoroughly investigate factors over the study period that are clinically important to the course of DLL.
Conclusion
The present study confirmed that EOD and not obtaining remission can be associated with a poorer prognosis of DLL. Clinicians should therefore be attentive to these factors, as relevant patients may warrant more intensive treatment or more careful evaluation in order to improve the outcome.
Future longitudinal studies on the course of DLL, including trajectory studies, should include biological markers such as magnetic resonance imaging measurements and inflammatory factors and integrate these biological markers and clinical data. Additionally, patients with various severities of cognitive impairment and from different clinical settings should be included in the studies.
Acknowledgment
This study was funded by unrestricted grants from the South-Eastern Norway Regional Health Authority and Innlandet Hospital Trust. These institutions had no further role in the study design, in the data collection, in the analysis and interpretation of data, in the writing of the paper, and in the decision to submit the paper for publication.
The authors would like to thank the following study centers for recruiting patients and for carrying out the follow-ups: Innlandet Hospital Trust; Sanderud and Reinsvoll, Vestre Viken Hospital Trust; Lier, St. Olav Hospital; University Hospital of Trondheim, Oslo University Hospital; Vardåsen (Ullevål) and Aker, Haukeland University Hospital; Bergen, Diakonhjemmet Hospital; Oslo, and Stavanger University Hospital.
Disclosure Statement
None of the authors has any conflict of interest to disclose.
Author Contributions
T. Borza participated in the design of the study, participated in the data collection, formulated the research questions, participated in the analysis and interpretation of the results, drafted the article, participated in the critical revision of the article for intellectual content, and approved the final version of the manuscript. K. Engedal participated in the design of the study, formulated the research questions, supervised the data collection, participated in the analysis and interpretation of the results, critically revised the article for intellectual content, and approved the final version of the manuscript. S. Bergh participated in the analysis and interpretation of the results, critically revised the article for intellectual content, and approved the final version of the manuscript. J.Š. Benth was responsible for the statistical design of the study and for carrying out the statistical analysis, participated in the analysis and interpretation of the results, critically revised the article for intellectual content, and approved the final version of the manuscript. G. Selbæk participated in the design of the study, formulated the research questions, supervised the data collection, participated in the analysis and interpretation of the results, critically revised the article for intellectual content, approved the final version of the manuscript, and was the main supervisor. All authors have read the paper and have agreed to be listed as authors.
References
1	Bogner HR, Morales KH, Reynolds CF 3rd, Cary MS, Bruce ML: Course of depression and mortality among older primary care patients. Am J Geriatr Psychiatry 2012;20:895–903.
2	da Silva J, Goncalves-Pereira M, Xavier M, Mukaetova-Ladinska EB: Affective disorders and risk of developing dementia: systematic review. Br J Psychiatry 2013;202:177–186.
3	Conwell Y, van Orden K, Caine ED: Suicide in older adults. Psychiatr Clin North Am 2011;34:451–468, ix.
4	Thomas A: Depression in older people; in Dening T, Thomas A (eds): Oxford Textbook of Old Age Psychiatry. Oxford, Oxford University Press, 2013, pp 544–569.
5	Schillerstrom JE, Royall DR, Palmer RF: Depression, disability and intermediate pathways: a review of longitudinal studies in elders. J Geriatr Psychiatry Neurol 2008;21:183–197.
6	Bennett S, Thomas AJ: Depression and dementia: cause, consequence or coincidence? Maturitas 2014;79:184–190.
7	McCall WV, Kintziger KW: Late life depression: a global problem with few resources. Psychiatr Clin North Am 2013;36:475–481.
8	Utvalg for alderspsykiatri Npf, Den norske legeforening: Plandokument for norsk alderspsykiatri 2011–2020 (The Norwegian Document of Geriatric psychiatry 2011–2020, summary in English). Oslo, 2010.
9	Cole MG, Bellavance F: The prognosis of depression in old age. Am J Geriatr Psychiatry 1997;5:4–14.
10	Comijs HC, Nieuwesteeg J, Kok R, van Marwijk HW, van der Mast RC, Naarding P, Voshaar RC, Verhaak P, de Waal MW, Stek ML: The two-year course of late-life depression; results from the Netherlands study of depression in older persons. BMC Psychiatry 2015;15:20.
11	Beekman AT, Geerlings SW, Deeg DJ, Smit JH, Schoevers RS, de Beurs E, Braam AW, Penninx BW, van Tilburg W: The natural history of late-life depression: a 6-year prospective study in the community. Arch Gen Psychiatry 2002;59:605–611.
12	Mitchell AJ, Subramaniam H: Prognosis of depression in old age compared to middle age: a systematic review of comparative studies. Am J Psychiatry 2005;162:1588–1601.
13	Hybels CF, Pieper CF, Blazer DG, Steffens DC: Heterogeneity in the three-year course of major depression among older adults. Int J Geriatr Psychiatry 2016;31:775–782.
14	Andreescu C, Chang CC, Mulsant BH, Ganguli M: Twelve-year depressive symptom trajectories and their predictors in a community sample of older adults. Int Psychogeriatr 2008;20:221–236.
15	Bogner HR, Morales KH, Reynolds CF, Cary MS, Bruce ML: Prognostic factors, course, and outcome of depression among older primary care patients: the PROSPECT study. Aging Ment Health 2012;16:452–461.
16	Blazer DG: Late life depression: a historical perspective upon a maturing field of inquiry. Aging Ment Health 2014;18:538–539.
17	Knapskog AB, Barca ML, Engedal K: A comparison of the Cornell Scale for Depression in Dementia and the Montgomery-Aasberg Depression Rating Scale in a memory clinic population. Dement Geriatr Cogn Disord 2013;35:256–265.
18	Kosteniuk JG, Morgan DG, O’Connell ME, Crossley M, Kirk A, Stewart NJ, Karunanayake CP: Prevalence and covariates of elevated depressive symptoms in rural memory clinic patients with mild cognitive impairment or dementia. Dement Geriatr Cogn Disord Extra 2014;4:209–220.
19	Byers AL, Yaffe K: Depression and risk of developing dementia. Nat Rev Neurol 2011;7:323–331.
20	Iden KR, Engedal K, Hjorleifsson S, Ruths S: Prevalence of depression among recently admitted long-term care patients in Norwegian nursing homes: associations with diagnostic workup and use of antidepressants. Dement Geriatr Cogn Disord 2014;37:154–162.
21	Barca ML, Engedal K, Selbaek G: A reliability and validity study of the Cornell Scale among elderly inpatients, using various clinical criteria. Dement Geriatr Cogn Disord 2010;29:438–447.
22	Kosteniuk JG, Morgan DG, O’Connell ME, Kirk A, Crossley M, Stewart NJ, Karunanayake CP: Trajectories of depressive symptomatology in rural memory clinic patients between baseline diagnosis and 1-year follow-up. Dement Geriatr Cogn Disord Extra 2016;6:161–175.
23	Dew MA, Reynolds CF 3rd, Houck PR, Hall M, Buysse DJ, Frank E, Kupfer DJ: Temporal profiles of the course of depression during treatment. Predictors of pathways toward recovery in the elderly. Arch Gen Psychiatry 1997;54:1016–1024.
24	McCusker J, Cole M, Ciampi A, Latimer E, Windholz S, Elie M, Belzile E: Twelve-month course of depressive symptoms in older medical inpatients. Int J Geriatr Psychiatry 2007;22:411–417.
25	McCusker J, Cole MG, Voyer P, Monette J, Champoux N, Ciampi A, Vu M, Belzile E, Bai C: Six-month trajectories of self-reported depressive symptoms in long-term care. Int Psychogeriatr 2016;28:71–81.
26	Cui X, Lyness JM, Tang W, Tu X, Conwell Y: Outcomes and predictors of late-life depression trajectories in older primary care patients. Am J Geriatr Psychiatry 2008;16:406–415.
27	Kaup AR, Byers AL, Falvey C, Simonsick EM, Satterfield S, Ayonayon HN, Smagula SF, Rubin SM, Yaffe K: Trajectories of depressive symptoms in older adults and risk of dementia. JAMA Psychiatry 2016;73:525–531.
28	Mirza SS, Wolters FJ, Swanson SA, Koudstaal PJ, Hofman A, Tiemeier H, Ikram MA: 10-year trajectories of depressive symptoms and risk of dementia: a population-based study. Lancet Psychiatry 2016;3:628–635.
29	Byers AL, Vittinghoff E, Lui LY, Hoang T, Blazer DG, Covinsky KE, Ensrud KE, Cauley JA, Hillier TA, Fredman L, Yaffe K: Twenty-year depressive trajectories among older women. Arch Gen Psychiatry 2012;69:1073–1079.
30	Borza T, Engedal K, Bergh S, Benth JS, Selbaek G: The course of depression in late life as measured by the Montgomery and Asberg Depression Rating Scale in an observational study of hospitalized patients. BMC Psychiatry 2015;15:191.
31	Sachs-Ericsson N, Corsentino E, Moxley J, Hames JL, Rushing NC, Sawyer K, Joiner T, Selby EA, Zarit S, Gotlib IH, Steffens DC: A longitudinal study of differences in late- and early-onset geriatric depression: depressive symptoms and psychosocial, cognitive, and neurological functioning. Aging Ment Health 2013;17:1–11.
32	World Health Organization (ed): The ICD-10 Classification of Mental and Behavioural Disorders – Diagnostic Criteria for Research. Geneva, WHO, 1993.
33	Reynolds CF 3rd, Dew MA, Frank E, Begley AE, Miller MD, Cornes C, Mazumdar S, Perel JM, Kupfer DJ: Effects of age at onset of first lifetime episode of recurrent major depression on treatment response and illness course in elderly patients. Am J Psychiatry 1998;155:795–799.
34	Montgomery SA, Asberg M: A new depression scale designed to be sensitive to change. Br J Psychiatry 1979;134:382–389.
35	Engedal K, Kvaal K, Korsnes M, Barca ML, Borza T, Selbaek G, Aakhus E: The validity of the Montgomery-Aasberg Depression Rating Scale as a screening tool for depression in later life. J Affect Disord 2012;141:227–232.
36	Knapskog AB, Barca ML, Engedal K: A comparison of the validity of the Cornell Scale and the MADRS in detecting depression among memory clinic patients. Dement Geriatr Cogn Disord 2011;32:287–294.
37	Folstein MF, Folstein SE, McHugh PR: “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198.
38	Lyketsos CG, Galik E, Steele C, Steinberg M, Rosenblatt A, Warren A, Sheppard JM, Baker A, Brandt J: The General Medical Health Rating: a bedside global rating of medical comorbidity in patients with dementia. J Am Geriatr Soc 1999;47:487–491.
39	WHO Collaborating Centre for Drug Statistics Methodology: Guidelines for ATC classification and DDD assignment 2014. Oslo, 2013.
40	Rush AJ, Kraemer HC, Sackeim HA, Fava M, Trivedi MH, Frank E, Ninan PT, Thase ME, Gelenberg AJ, Kupfer DJ, Regier DA, Rosenbaum JF, Ray O, Schatzberg AF, Force AT: Report by the ACNP Task Force on response and remission in major depressive disorder. Neuropsychopharmacology 2006;31:1841–1853.
41	Hawley CJ, Gale TM, Sivakumaran T; Hertfordshire Neuroscience Research Group: Defining remission by cut off score on the MADRS: selecting the optimal value. J Affect Disord 2002;72:177–184.
42	Alexopoulos GS, Abrams RC, Young RC, Shamoian CA: Cornell Scale for Depression in Dementia. Biol Psychiatry 1988;23:271–284.
43	Guy W: ECDEU Assessment Manual for Psychopharmacology. Washington DC, National Institute of Mental Health; 1976: 217–222 & 313–331.
44	Alexopoulos GS, Katz IR, Bruce ML, Heo M, Ten Have T, Raue P, Bogner HR, Schulberg HC, Mulsant BH, Reynolds CF 3rd; PROSPECT Group: Remission in depressed geriatric primary care patients: a report from the PROSPECT study. Am J Psychiatry 2005;162:718–724.
45	Azar AR, Chopra MP, Cho LY, Coakley E, Rudolph JL: Remission in major depression: results from a geriatric primary care population. Int J Geriatr Psychiatry 2011;26:48–55.
46	Driscoll HC, Basinski J, Mulsant BH, Butters MA, Dew MA, Houck PR, Mazumdar S, Miller MD, Pollock BG, Stack JA, Schlernitzauer MA, Reynolds CF 3rd: Late-onset major depression: clinical and treatment-response variability. Int J Geriatr Psychiatry 2005;20:661–667.
Appendix after References (Editorial Comments)

Legend(s)
Fig. 1.	Flowchart describing attrition from T0 (baseline) to T2 (1-year follow-up).
Fig. 2.	Montgomery-Åsberg Depression Rating Scale (MADRS) sum scores by trajectory classes.
Table(s)

Footnote(s)

Table 1. Demographic and clinical characteristics of the study population at inclusion (T0), at discharge from the hospital (T1), and at 1-year follow-up (T2)

	T0 
(n = 160)	T1 
(n = 160)	T2 
(n = 146)	

Age, years	76.1±6.8			
Female	72.5%			
Education, years	0.10±3.0			
Marital status	
Not single, married/living together	40.0%			
Single (single/widow[er]/divorced/living apart)	60.0%			
Cognitive function (MMSE score)	25.9±3.6	26.5±3.3	026.0±4.8	
Diagnosis of dementia		08.8% 	015.8%	
GMHR categories	
Good/fair	48.1%		037.0%	
Poor/very poor	51.9%		063.0%	
Living in nursing home	00.7%		011.0%	
Days of stay T0–T1		60.9±44.6		
Days between T1 and T2			356.0±59.0	
Age at onset of the first depressive episode in categories	
<60 years	49.0%			
≥60 years	51.0%			
Baseline depressive episode as first	30.6%			
Remission1		50.6%		
ICD-10 diagnosis of depression				
Bipolar affective disorder (F31.x in ICD-10)		06.3%		
Depressive episode (F32.x in ICD-10)		29.4%		
Recurrent depressive disorder (F33.x in ICD-10)		61.9%		
Brief depressive reaction (F43.20 in ICD-10)		01.3%		
Organic depressive disorder (F06.32 in ICD-10)		01.3%		
ICD-10 depression with psychosis		09.4%		

Values are presented as mean ± standard deviation or percentage. GMHR, General Medical Health Rating Scale; MMSE, Mini-Mental State Examination. 1 Montgomery-Åsberg Depression Rating Scale score <10.	

Table 2. Results of the logistic regression model (GLIMMIX procedure) with trajectory class as outcome variable, n = 146 (class A as reference group)

Covariates	Bivariate analysis		Multivariable analysis	

	OR (95% CI)	p value		OR (95% CI)	p value	

MMSE score	0.91 (0.81–1.01)	0.065				
Diagnosis of dementia3 (no diagnosis as 01)	3.06 (0.97–9.67)	0.057		3.43 (0.96–12.30)	0.058	
GMHR score (very good/good as 01)	2.44 (1.06–5.66) 	0.037				
Age at onset of the first depressive episode in 
categories (≥60 years as 01)	
1.04 (0.47–2.28)	
0.930				
Remission at discharge from the hospital (T1) 
(remission as 01)	
4.91 (1.95–12.38)	
0.001		
3.56 (1.35–9.41)	
0.011	
Depression with psychosis3 (no psychosis as 01)	1.67 (0.47–5.88)	0.424				
Bipolar disorder3 (no diagnosis as 01)	1.20 (0.23–6.34)	0.829				
Age	0.96 (0.90–1.02)	0.166				
Sex (female as 01)	0.57 (0.21–1.52)	0.255				
Marital status (not single as 01)	1.21 (0.54–2.73)	0.641				
Years of education	0.98 (0.86–1.12)	0.739				
Days of stay2	1.014 (1.005–1.022)	0.003		1.014 (1.004–1.023)	0.005	

CI, confidence interval; GMHR, General Medical Health Rating Scale at inclusion (T0); MMSE, Mini-Mental State Examination at T0; OR, odds ratio. 1 Reference category. 2 T0–T1. 3 As evaluated at discharge from hospital (T1).	

Table 3. Results of the ordinal regression model (GLIMMIX procedure) with course groups (good, residual, and poor) between discharge from the hospital (T1) and 1-year follow-up (T2) as outcome variable, n = 135 (good as reference group)

Covariates	Bivariate analysis		Multivariable analysis	

	OR (95% CI)	p value		OR (95% CI)	p value	

MMSE score	0.95 (0.87–1.04) 	0.239				
Diagnosis of dementia2 (no diagnosis as 01)	2.14 (0.70–6.52) 	0.179		2.51 (0.77–8.19)	0.126	
GMHR score (very good/good as 01)	1.30 (0.69–2.43)	0.412				
Age at onset of the first depressive episode in 
categories (≥60 years as 01)	
2.07 (1.10–3.91) 	
0.026		
2.23 (1.16–4.30)	
0.017	
Remission at T1 (remission as 01)	2.09 (1.10–2.43) 	0.024				
Depression with psychosis2 (no psychosis as 01)	0.97 (0.30–3.07)	0.952				
Bipolar disorder2 (no diagnosis as 01)	0.97 (0.25–3.73)	0.960				
Age	1.01 (0.97–1.06) 	0.590				
Sex (female as 01)	1.14 (0.55–2.35)	0.722				
Marital status (not single as 01)	0.87 (0.47–1.64)	0.668				
Years of education	0.94 (0.85–1.05)	0.257		0.92 (0.82–1.03)	0.127	
Follow-up days between T1 and T2	0.992 (0.986–0.998)	0.006		0.990 (0.984–0.997)	0.003	

CI, confidence interval; GMHR, General Medical Health Rating Scale at inclusion (T0); MMSE, Mini-Mental State Examination at T0; OR, odds ratio. 1 Reference category. 2 As evaluated at T1.	

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