The goal of this study was to evaluate the relationship between early change in psychosocial function independent of depression severity and longer-term symptomatic remission. Participants of Combining Medications to Enhance Depression Outcomes trial were randomly selected for model selection (n = 334) and validation (n = 331). Changes in psychosocial function (Work and Social Adjustment Scale, WSAS) from baseline to week 6 were assessed and two data-driven sub-groups of WSAS change were identified in the randomly selected model selection half. Results of analyses to predict symptomatic remission at 3 and 7 months were validated for these sub-groups in the second half (validation sample). From baseline to week 6, psychosocial function improved significantly even after adjusting for depression severity at each visit and select baseline variables (age, gender, race, ethnicity, education, income, employment, depression onset before age 18, anxious features, and suicidal ideation), treatment-arm, and WSAS score. The WSAS change patterns identified two (early improvement and gradual change) subgroups. After adjusting for baseline variables and remission status at week 6, participants with early improvement in the second half (validation sample) had greater remission rates than those with gradual change at both 3 (3.3 times) and 7 months (2.3 times) following acute treatment initiation. In conclusion, early improvement in psychosocial function provides a clinically meaningful prediction of longer-term symptomatic remission, independent of depression symptom severity.
Citation: Jha MK, Minhajuddin A, Greer TL, Carmody T, Rush AJ, Trivedi MH (2016) Early Improvement in Psychosocial Function Predicts Longer-Term Symptomatic Remission in Depressed Patients. PLoS ONE 11(12): e0167901. doi:10.1371/journal.pone.0167901
Editor: Branko Aleksic, Nagoya Mental Clinic, JAPAN
Received: May 24, 2016; Accepted: November 18, 2016; Published: December 28, 2016
Copyright: © 2016 Jha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was funded by the National Institute of Mental Health under contract N01 MH-90003 to the University of Texas Southwestern Medical Center at Dallas (MHT and AJR). Forest Pharmaceuticals, GlaxoSmithKline, Organon, and Wyeth Pharmaceuticals provided medications for this trial at no cost. This work was also supported in part through the Center for Depression Research and Clinical Care (MHT) and the Hersh Foundation (MHT). The content of this publication does not necessarily reflect the views or policies of the U.S. Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government. National Institute of Mental Health had no role in the drafting or review of the manuscript or in the collection or analysis of the data.
Competing interests: Drs. Jha, Minhajuddin, and Carmody report no financial relationships with commercial interests. Dr. Greer has received research funding from NARSAD and honoraria and/or consultant fees from H. Lundbeck A/S and Takeda Pharmaceuticals International, Inc. Dr. Rush has received consulting fees from the American Psychiatric Association, Brain Resource Ltd, H. Cyberonics, Eli Lilly, Emmes Corp, Lundbeck A/S, Medavante, Inc; National Institute of Drug Abuse; Santium Inc.; Takeda USA and the University of Colorado; speaking fees from the University of California at San Diego, Hershey Penn State Medical Center, the American Society for Clinical Psychopharmacology; the New York State Psychiatric Institute; Stanford Medical School; royalties from Guilford Publications and the University of Texas Southwestern Medical Center; and research support from Duke-National University of Singapore. Dr. Madhukar H. Trivedi is or has been an advisor/consultant and received fees from: Alkermes Inc., Allergan, AstraZeneca, Brintellix, BMS, Cerecor, Eli Lilly & Company, Forest, Health Research Associates, Johnson & Johnson, Lundbeck, Medscape, MSI Methylation Sciences Inc., Merck, Naurex Inc., Nestle Health Science – Pamlab Inc., One Carbon Therapeutics, Otsuka America Pharmaceuticals Inc., PamLab, Pfizer Inc., Roche, SHIRE Development, Takeda Pharmaceuticals Inc. In addition, he has received grants/research support from: Agency for Healthcare Research and Quality (AHRQ), National Institute of Mental Health (NIMH), Johnson and Johnson, National Institute of Diabetes and Digestive and Kidney diseases (NIDDK) and National Institute on Drug Abuse (NIDA). This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Treatment algorithms largely rely on depressive symptom severity as to inform clinical decisions [1–4], even though symptom severity reflects only a portion of the burden of Major Depressive Disorder (MDD) . Despite its importance to depressed patients as a primary outcome, psychosocial function has received far less attention in either practice or treatment research . Although it is significantly impaired in depressed patients and improves substantially with effective antidepressant treatments [7–13], psychosocial function has been traditionally considered a lagging indicator of improvement rather than a critical domain of interest and treatment focus, apparently due to smaller magnitude and slower onset of change when compared to reduction in depression severity [7, 11, 14–17]. The importance of psychosocial function in patient care is highlighted by evidence that psychosocial impairments often persist after symptomatic remission , are associated with worse long-term clinical outcomes [19–21], and remain a major concern for patients . Functional recovery is distinct from symptomatic recovery and likely requires different treatment approaches [23, 24].
To identify those patients in need of additional efforts to improve psychosocial function, assessment of function should be distinct and not simply reflect depressive symptom severity. Patterns of improvement with antidepressant treatment may further help in targeting adjunctive treatments needed to attain functional recovery. Using latent class analysis, Uher et al.  previously found that sub-groups of depressed patients had two distinct trajectories (rapid-initial and gradual) of depressive symptom improvement  with potential treatment implications; patients on a noradrenergic antidepressant (nortriptyline) were more likely to display rapid-initial improvement as compared to those on a serotonergic antidepressant (escitalopram) . Hence, this data-driven approach can be extended to identify sub-groups of patients with different trajectories of change in psychosocial function early in the course of antidepressant treatment. We have found that work productivity (absence from work and/or reduced productivity while at work) improvements early in the course of antidepressant treatment were not only independent of change in depression severity, but patterns of this improvement could also be used to predict long-term clinical outcomes . These findings are applicable only to employed persons
Previous reports based on findings from data-driven model-based approaches have significant limitations because findings are typically specific to the sample in which they are developed and replication in separate sample(s) have been rare . Even beyond the need to replicate model-based analyses, there has been a growing appreciation of the need for reproducible published research reports . In a recent report that replicated 97 previously published psychology studies with positive findings, only a third of the replications were positive . While neuroimaging studies routinely incorporate replication paradigms , to our knowledge no previous study has tried to replicate findings of data-driven latent class (trajectory) analyses for changes on psychosocial function.
This study evaluates whether changes in psychosocial function that occur early (within the first 6 weeks) in the course of antidepressant treatment, independent of change in depression severity, and can be used to predict future clinical course. All participants (n = 665) of the Combining Medications to Enhance Depression Outcomes (CO-MED) trial were divided randomly in two halves, model selection (n = 334) and validation (n = 331) samples, to generate two separate non-overlapping groups so that inferences can be drawn on model selection sample and reproduced in the validation sample. In the model selection sample, model-based latent class analyses were used to identify sub-groups of participants with different trajectories of change in psychosocial function. In the validation sample, model parameters from the model selection sample were used to assign sub-groups of changes in psychosocial function. Improvement in psychosocial function and differential rates of remission at 3 and 7 months based on sub-groups of changes in psychosocial function were tested.
Data for this report were obtained from the CO-MED trial as an unplanned secondary analysis . This study recruited 665 participants who were stratified by clinical sites and assigned to one of the treatment-arms in a 1:1:1 ratio. The three treatment-arms in this study were: 1) escitalopram plus placebo (selective serotonin reuptake inhibitor [SSRI] monotherapy), 2) sustained-release (SR) bupropion plus escitalopram (bupropion-SSRI combination), and 3) extended-release (XR) venlafaxine plus mirtazapine (venlafaxine-mirtazapine combination).
Study visits were conducted at baseline and weeks 1, 2, 4, 6, 8, 10, and 12 for acute-phase and weeks 16, 20, 24, and 28 for continuation-phase. Participation in continuation-phase treatment (after 3 months) was restricted to participants who had either 1) received an acceptable benefit by 3 months as defined by score of 9 or less on 16-item Quick Inventory of Depressive Symptomatology–Clinician rated (QIDS-C)  or 2) had reached a score of 10–13 on QIDS-C and both physician and participant decided to continue treatment because of substantial benefit to study medications. At each visit, study physicians used Measurement Based Care (MBC) (1) to personally tailor dosage adjustments based on QIDS-C scores and scores on the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale . The QIDS-C items were extracted from the Inventory of Depressive Symptomatology–Clinician-Rated (IDS-C) scale .
Broad inclusion and minimal exclusion criteria were used to ensure a reasonably representative participant group . Patients seeking treatment at participating clinical sites and planning to continue living in the area of that clinical site for the duration of the study were eligible for enrollment. Clinical sites included six primary and nine psychiatric care clinics across the United States (full list available at https://clinicaltrials.gov/ct2/show/NCT00590863) that were selected based on previous performance in the Sequenced Treatment Alternatives to Relieve Depression trial in order to ensure adequate minority representation. To be included in the study, participants had to meet clinical criteria for nonpsychotic MDD, recurrent (greater than 1 previous episode) or chronic (current episode greater than 2 years), as defined by a clinical interview and confirmed by the MINI International Neuropsychiatric Interview (MINI) ; have at least 2 months’ duration of current episode; and score 16 or greater on the 17-item Hamilton Rating Scale for Depression (HRSD-17) . Exclusion criteria included: pregnant, breast feeding or planning to be pregnant during the course of trial, lifetime history of psychotic disorder or presence of current psychotic symptoms, diagnosis of anorexia or bulimia within last 2 years, current primary diagnosis of obsessive compulsive disorder, current substance dependence that requires inpatient detoxification or treatment, current psychiatric disorder that requires inpatient hospitalization, lifetime history or allergy to protocol antidepressants, history of non-response to antidepressant during current episode or non-response to protocol antidepressants ever in lifetime, taking any of the protocol antidepressants at screening visit, taking fluoxetine or monoamine oxidase inhibitors in 4 weeks prior to study entry, presence of unstable general medical condition that requires hospitalization, require medications for general medications that contraindicate protocol antidepressant, presence of epilepsy, lifetime history of seizure or other condition requiring anticonvulsant, presence of narrow angle glaucoma, hypothyroidism (unless stable on medication for last 3 months), and treatment with somatic antidepressants, contraindicated medications, or depression specific psychotherapy, also see the clinicaltrials.gov website (https://clinicaltrials.gov/ct2/show/NCT00590863). Consent was offered to 832 treatment-seeking depressed outpatients at participating clinical sites, of whom 731 were screened and 61 were excluded from randomization . The target sample size for CO-MED trial was 660 to allow detection of 15% difference in remission rates between combinations of antidepressants and SSRI monotherapy .
The Institutional Review Boards at UT Southwestern Medical Center at Dallas, the University of Pittsburgh Data Coordinating Center, each participating regional center, and all relevant clinical sites reviewed and approved the study protocol and all consent documents and study procedures. All participants provided written informed consent prior to entering the study. An independent Data Safety and Monitoring Board also monitored the study. Further details of CO-MED study have been described by Rush et al.  (clinicaltrials.gov identifier NCT00590863).
Participants in all three treatment-arms (SSRI monotherapy, bupropion-SSRI combination, and venlafaxine-mirtazapine combination) received two types of pills. The first medication was known to both participants and study personnel while the second medication was known only to the study personnel (single blind). All dose adjustments described below were made only if side effects were tolerable .
Participants in SSRI monotherapy treatment-arm were started on 10 mg/day dose of escitalopram with option to increase the dose to 20 mg/day at week 4 visit or later if QIDS-C score was greater than 5. Pill placebo was added at week 2 in single-blind fashion with option to increase the dose at week 4 visit or later if QIDS-C score was greater than 5. At the end of 12 weeks, mean escitalopram dose was 17.6 mg/day and mean placebo dose was 1.4 pills/day.
Participants in bupropion-SSRI combination treatment-arm were started on bupropion SR 150 mg/day and the dose was increased to 300 mg/day at week 1 visit. At week 2 visit, escitalopram 10 mg/day was started in single-blind fashion. At week 4 visit, bupropion SR was increased to 400 mg/day and/or escitalopram was increased to 20 mg/day if QIDS-C score was greater than 5. For visits at week 6 and later, doses could be increased to 400 mg/day of bupropion SR and 20 mg/day of escitalopram if QIDS-C score was greater than 5. At the end of 12 weeks, mean bupropion SR dose was 324.0 mg/day and mean escitalopram dose was 14.0 mg /day.
Participants in venlafaxine-mirtazapine combination treatment-arm were started on venlafaxine XR 37.5 mg/day for 3 days and then increased to 75 mg/day. At week 1 visit, venlafaxine XR was increased to 150 mg/day. At week 2 visit, if the score on QIDS-C was greater than 5 then mirtazapine 15 mg/day was added in single-blind fashion. At week 4 visit, if QIDS-C was greater than 5 then venlafaxine XR dose was increased to 225 mg/day and/or mirtazapine was increased to 30 mg/day. At week 6, if QIDS-C was greater than 5 then mirtazapine could be raised to 45 mg/day. At week 8, if QIDS was greater than 5 then venlafaxine XR could be raised to 300 mg/day. At the end of 12 weeks, mean venlafaxine XR dose was 207.6 mg/day and mean mirtazapine dose was 25.3 mg /day.
At baseline, participants provided sociodemographic information. At baseline and all treatment visits (weeks 1, 2, 4, 6, 8, 10, 12, 16, 20, 24, and 28), participants completed the 16-item Quick Inventory of Depressive Symptomatology—Self-Report (SR) scale [31, 36, 37] and the 5-item Work and Social Adjustment Scale (WSAS)  at identical assessment time points.
Quick Inventory of Depressive Symptomatology—Self-Report (QIDS-SR): Of the 16 items (each item has 4 choices which are scored from 0–3) in QIDS-SR, 9 items are used to calculate the total score which can range from 0–27 . The Pearson moment correlation between QIDS-SR and HRSD-17 has been previously reported as 0.86 . The Cronbach’s α of QIDS-SR ranged from 0.86–0.87 in previous reports [31, 36, 37].
Work and Social Adjustment Scale (WSAS): The WSAS is a 5-item self-report measure designed to identify functional impairment that is attributed to an identified problem or condition, and it has been used to study the treatment of depression and anxiety . Each question is rated on a 0 to 8 scale, with 0 indicating no impairment, and 8 indicating very severe impairment. WSAS scores above 10 suggest significant functional impairment. Cronbach’s α measure of internal consistency ranges from 0.80 to 0.94, with test-retest reliability of 0.73. The WSAS also has good convergent and discriminant validity, and is sensitive to patient differences in severity, as well as treatment-related change [38, 39].
Data from all 665 CO-MED participants were included in this report. To address these issues with replication of findings of model-based analyses, we decided to divide all CO-MED participants in two separate groups (model selection and validation samples) in a random fashion so as to draw inferences in the model selection sample and test for the consistency of these inferences in the validation sample. We compared baseline clinical and sociodemographic variables in both samples (model selection and validation) using chi-square test for categorical variables and analysis of variance (ANOVA) for categorical variables. To identify sub-groups of participants with different trajectories of changes in WSAS during the first 6 weeks of the trial, we used latent class growth analysis  using PROC TRAJ as implemented in SAS to estimate discrete mixture models with censored normal distribution [41, 42] in the model selection sample (maximum of 5 latent classes). We used the methods described in previous publications [25, 27, 43, 44] to select the final model based on model fit [Bayesian information criteria (BIC) and Akaike information criteria (AIC), with the lower values of BIC and AIC indicating better model fit ], parsimony (number of groups), membership size (percentage of sample in each trajectory group), and interpretability [statistical significance of modeling terms (for example, intercept only, one-, two-, three-, or four-degree polynomial)]. We then assigned participants in the validation sample to sub-groups based on trajectories of WSAS change previously estimated in the model selection sample. We calculated the estimated score for each trajectory using the following formula: (intercept) + (estimate of one-degree polynomial from final model selected in selection sample)*(week) + (estimate of two-degree polynomial from final model selected in selection sample)*(week)2 + (estimate of three-degree polynomial from final model selected in selection sample)*(week)3. For each individual participant, we used the observed score available at each visit and subtracted the estimated score of each trajectory from this observed score. We then squared the difference to calculate squared-error, giving us a squared-error value for each trajectory at each visit. We then calculated the mean of this squared-error across all visits for each trajectory in first 6 weeks for each participant and obtained a mean squared-error value for each trajectory. We assigned the participant in validation sample to the sub-group which had the trajectory with lowest mean squared-error.
Consistent with Rush et al.’s previous report of numerically lower values of WSAS at 3 and 7 months as compared to baseline , we hypothesized lower values at week 6 as compared to baseline. To evaluate whether reductions in WSAS during the first 6 weeks of treatment were statistically significant even after controlling for the select baseline covariates (listed below) and levels of QIDS-SR at each study visit, we used repeated measures mixed model analyses with visit as the within subject factor and all other variables as between subject factors. We calculated correlation coefficient between WSAS and QIDS using PROC MIXED as implemented in SAS  as we had repeated observations for each subject . To evaluate whether participants in these sub-groups with different trajectories of WSAS change had different rates of remission, we used separate univariate logistic regression analyses followed by multivariate (select baseline covariates along with remission at week 6 and treatment-arm) analyses with backwards elimination, and remission at 3 and 7 months as dependent variables.
Based on literature review, we included the following baseline clinical and sociodemographic features as covariates for our analyses: age, education (< 12 years, 12–15 years, and ≥16 years), employment status (employed or unemployed), monthly income (< $2000, $2000 to $4000, and > $4000), gender, race (white, black, other), Hispanic ethnicity, anxious features, depression onset before age of 18, presence of suicidal ideations, QIDS-SR score, and WSAS score [14, 16, 47]. No interaction terms were included in the model. Even though treatment-arms do not differ as shown in the primary report on CO-MED , we included treatment-arm in our analyses to account for any variability due to treatment-arm assignment.
We set the threshold of significance at 0.05 and used SAS 9.3 (SAS Inc., Cary, NC) for all analyses.
We found no differences in the participants in model selection (n = 334) and validation (n = 331) samples (See Table 1). Using repeated observations of QIDS-SR and WSAS in both model selection and validation samples, we found that the correlation coefficient between WSAS and QIDS-SR was 0.69 in both samples.
Model development in the selection sample
As shown in Table 2, we systematically increased the number of classes and order of polynomials in the model selection sample and found that models with two sub-groups had the lowest BIC and AIC as compared to models with three or more sub-groups. We found that the model with best fit (lowest BIC and AIC) had two sub-groups with two- and zero-degree polynomials as order of changes in the trajectory. Next, to maximize the interpretability of our model, we examined the statistical significance of higher order polynomials in all two sub-group models, and found that the model with three- and one-degree polynomial order had significant p values for both these orders. Hence, we selected this model (three- and one-degree polynomial) over the model (two- and zero-degree polynomial) with lowest BIC and AIC in the model selection sample. See Table 3 for estimates of three- and one-degree polynomial parameters of the selected two sub-groups. Participants in the first sub-group (41.98%) had early improvement in WSAS whereas participants in the second sub-group (58.02%) experienced gradual change.
Improvements in psychosocial function during first six weeks of antidepressant treatment
We found that reduction in WSAS from baseline to week 6 were significant in the validation sample (F = 6.75 df = 3, p <0.001) even after controlling for QIDS-SR at each visit and adjusting for above-mentioned baseline clinical and sociodemographic variables in repeated measures mixed model analyses (also see S1 Table for results of mixed model analyses in both model selection and validation samples).
Trajectories of changes in WSAS during the first 6 weeks in the validation sample
In the validation sample, 46.83% exhibited early improvement in WSAS whereas 53.17% experienced gradual change. The estimated means for the two sub-groups generated by PROC TRAJ procedure are presented in panel A of Fig 1. Even though the participants with early improvement had on average lower baseline WSAS scores, there was substantial overlap in WSAS scores between the two sub-groups at baseline but from week 2 onwards the two sub-groups sharply diverged; see panels B and C of Fig 1, which present observations from 20 randomly selected participants from each sub-group in the validation sample. From baseline to week 6, participants with early improvement had significantly lower WSAS as compared to those with gradual change (estimate = 6.9, standard error = 1.1) with an effect size of 0.69. We have presented the mean WSAS scores for the two sub-groups in Fig 2 to depict the course of change in psychosocial function during the CO-MED trial.
A. Estimated means of the two sub-groups, gradual change (solid line) and early improvement (broken line or dash), identified by latent class analysis of Work and Social Adjustment Scale (WSAS) changes during the first 6 weeks of the Combining Medications to Enhance Depression Outcomes (CO-MED) trial. B. Estimated mean along with observed values of 20 randomly selected participants from early improvement sub-group of the validation sample (n = 331). C. Estimated mean along with observed values of 20 randomly selected participants from gradual change sub-group of the validation sample (n = 331).
Sub-groups (early improvement and gradual change) of participants with different trajectories of Work and Social Adjustment Scale (WSAS) change were identified using latent class analyses during the first 6 weeks (marked by vertical line). Score of WSAS greater than 10 suggest significant functional impairment  (horizontal line). * Continuation Phase was limited to CO-MED trial participants who had a clinical response.
Remission rates at 3 and 7 months in the validation sample based on trajectories of WSAS change
As compared to participants with gradual change in WSAS, those with early improvement had significantly higher unadjusted rates of remission at both 3 months (OR = 4.94; 95% CI = 2.78,8.80) and 7 months (OR = 3.30; 95% CI = 2.14,5.08). Even after controlling for the above-mentioned baseline variables and remission status at week 6, rates of remission in participants with early improvement of WSAS continued to be higher at both 3 months (OR = 3.29; 95% CI = 1.65,6.52) and 7 months (OR = 2.31; 95% CI = 1.37,3.90) as compared to those with gradual change. These findings were similar to those in the model selection sample, see S2 Table.
To the best of our knowledge, this is the first report that used randomly selected model selection and validation samples to assess and replicate the prognostication of longer-term clinical outcomes based on early changes in psychosocial function in a large cohort of MDD patients treated with antidepressant medications. We found that psychosocial function, as measured by WSAS, improved significantly during the first 6 weeks of antidepressant treatment, independent of change in depressive symptom severity. We also found that when divided into two distinct sub-groups based on trajectories of change in psychosocial function during first 6 weeks of antidepressant treatment (early improvement and gradual change), participants with early improvement in psychosocial function were 3–6 times more likely to be in remission at 3 months and 2–3 times more likely at 7 months, even after controlling for remission status at week 6, baseline symptom severity, and other variables.
These findings extend previous studies reporting substantial improvement in psychosocial function with a variety of treatments [7–13, 48]. They also add to an emerging literature that suggests that at least a subgroup of depressed individuals realize functional improvements early in the course of antidepressant treatment, and these early changes in work productivity and psychosocial function predict future clinical course . Our finding that change in depression severity did not fully account for improved psychosocial function supports the notion that burden of depressive illness and improvement with treatment are not just restricted to core depressive symptoms . These results also suggest the need to direct additional treatment modalities for those with inadequate psychosocial improvement early in the course of treatment, as well as routine measurement of psychosocial function to ensure the goal of recovery is achieved.
Strengths of our study include the adaptation of the commonly used data-driven trajectory-based approach using latent class analyses [49–51] to identify sub-groups of MDD patients with different patterns (linear or non-linear) of changes in psychosocial function that can be missed by traditional linear mixed model analyses . We have previously used a similar data-driven approach to identify sub-groups of participants with different trajectories of change in work productivity outcomes (absence from work and/or reduced productivity while at work)  for those who were employed. In the context of model-based analysis to understand the relationship of psychosocial function and depression severity, two recent studies that used structural equation modeling (SEM) had conflicting findings. While Dunn et al. found that improvement in psychosocial function predicted subsequent improvement in depression severity , Lin et al. failed to replicate this finding using similar SEM analyses . To our knowledge, no one has previously either tried to replicate findings from latent class analysis of longitudinal data or address the limitation of model-overfitting. Hence, we designed our analyses to be conducted first in a model selection sample and then be repeated in a validation sample. We divided the sample randomly to ensure that both samples (model selection and validation) were similar on baseline sociodemographic and illness characteristics. We developed data-driven trajectory models using latent class analyses in the model selection sample, selected a final model, and assigned participants in the validation sample to sub-groups with the same mean estimates of WSAS change as those in the final selected model of the model selection sample. Our method of assigning sub-groups based on different trajectories of WSAS change in the validation sample, which uses a simple mathematical equation along with the model-estimates from the model selection sample, is easy to use in future studies. This is highly significant in light of widespread attention on non-replication of previously reported findings . Hence, the consistent finding of higher remission rates at 3 and 7 months based on early improvement in psychosocial function in both the model selection and validation samples is a major strength of our study.
Our study has several limitations. The analyses conducted in this report are secondary in nature. The model developed and validated in this report was based on participants from a clinical trial, though one with broad inclusion and minimal exclusion criteria. However, despite the fact that CO-MED participants were recruited from real-world outpatient clinics with broad inclusion criteria, treatment was delivered using high quality MBC as outlined by Trivedi et al. . Future studies that prospectively identify trajectories of change in psychosocial function and their association with different remission rates would further support our findings. However, we have outlined how one can replicate these findings to see if such an approach is viable. The measure of psychosocial function in our report, WSAS, is a brief instrument which may not comprehensively evaluate all dimensions of psychosocial function that may be relevant to assessing functional recovery.
In conclusion, we found that psychosocial function, as captured by the WSAS, is not merely a proxy of depressive symptom severity and can improve early in the course of antidepressant treatment. Those MDD patients who have early improvement of psychosocial function have higher rates of longer-term remission (3 and 7 months) than those with gradual change.
S1 Table. Results of mixed model analyses with WSAS at week 6 as the dependent variable.
S2 Table. Prediction of longer-term remission based on changes in WSAS in model selection sample (n = 334).
The authors thank the clinical staff at each clinical site including site investigators and co-investigators for their assistance with this project; all of the study participants; and Savitha Kalidas, Ph.D. for administrative support.
- Conceptualization: MKJ AM AJR MHT.
- Data curation: AM MHT.
- Formal analysis: AM.
- Funding acquisition: MHT AJR.
- Investigation: AJR MHT.
- Methodology: MKJ AM AJR MHT.
- Project administration: AJR MHT.
- Resources: AJR MHT.
- Software: AM.
- Supervision: MHT.
- Validation: AM TC.
- Visualization: MKJ AM MHT.
- Writing – original draft: MKJ AM MHT.
- Writing – review & editing: MKJ AM TLG TC AJR MHT.
- 1. Trivedi MH, Daly EJ. Measurement-based care for refractory depression: a clinical decision Fort model for clinical research and practice. Drug Alcohol Depend. 2007;88 Suppl 2:S61–71.
- 2. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40. doi: 10.1176/appi.ajp.163.1.28. pmid:16390886
- 3. Guo T, Xiang Y-T, Xiao L, Hu C-Q, Chiu HFK, Ungvari GS, et al. Measurement-Based Care Versus Standard Care for Major Depression: A Randomized Controlled Trial With Blind Raters. Am J Psychiatry. 2015;172(10):1004–13. doi: 10.1176/appi.ajp.2015.14050652. pmid:26315978
- 4. Trivedi MH. Tools and strategies for ongoing assessment of depression: a measurement-based approach to remission. J Clin Psychiatry. 2009;70 Suppl 6:26–31.
- 5. Cohen RM, Greenberg JM, IsHak WW. Incorporating multidimensional patient-reported outcomes of symptom severity, functioning, and quality of life in the Individual Burden of Illness Index for Depression to measure treatment impact and recovery in MDD. JAMA Psychiatry. 2013;70(3):343–50. doi: 10.1001/jamapsychiatry.2013.286. pmid:23303512
- 6. Greer TL, Kurian BT, Trivedi MH. Defining and Measuring Functional Recovery from Depression. CNS Drugs. 2010;24(4):267–84. doi: 10.2165/11530230-000000000-00000. pmid:20297853
- 7. Hirschfeld RM, Dunner DL, Keitner G, Klein DN, Koran LM, Kornstein SG, et al. Does psychosocial functioning improve independent of depressive symptoms? A comparison of nefazodone, psychotherapy, and their combination. Biol Psychiatry. 2002;51(2):123–33. pmid:11822991
- 8. Miller IW, Keitner GI, Schatzberg AF, Klein DN, Thase ME, Rush AJ, et al. The treatment of chronic depression, part 3: psychosocial functioning before and after treatment with sertraline or imipramine. J Clin Psychiatry. 1998;59(11):608–19. pmid:9862607
- 9. Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M, Kurian BT, et al. Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study. Am J Psychiatry. 2011;168(7):689–701. doi: 10.1176/appi.ajp.2011.10111645. pmid:21536692
- 10. Shelton RC HK, Rapaport MH, Kiev A, Smith WT, Hirschfeld RM, Lydiard RB, Zajecka JM, Dunner DL. A randomized, double-blind, active-control study of sertraline versus venlafaxine XR in major depressive disorder. J Clin Psychiatry. 2006;67(11):1674–81. pmid:17196045
- 11. Trivedi MH, Corey-Lisle PK, Guo Z, Lennox RD, Pikalov A, Kim E. Remission, response without remission, and nonresponse in major depressive disorder: impact on functioning. Int Clin Psychopharmacol. 2009;24(3):133–8. doi: 10.1097/YIC.0b013e3283277614. pmid:19318972
- 12. Trivedi MH, Dunner DL, Kornstein SG, Thase ME, Zajecka JM, Rothschild AJ, et al. Psychosocial outcomes in patients with recurrent major depressive disorder during 2 years of maintenance treatment with venlafaxine extended release. J Affect Disord. 2010;126(3):420–9. doi: 10.1016/j.jad.2010.04.011. pmid:20510459
- 13. Trivedi MH, Pigotti TA, Perera P, Dillingham KE, Carfagno ML, Pitts CD. Effectiveness of low doses of paroxetine controlled release in the treatment of major depressive disorder. J Clin Psychiatry. 2004;65(10):1356–64. pmid:15491239
- 14. Daly EJ TM, Wisniewski SR, Nierenberg AA, Gaynes BN, Warden D, Morris DW, Luther JF, Farabaugh A, Cook I, Rush AJ. Health-related quality of life in depression: a STAR*D report. Ann Clin Psychiatry. 2010;22(1):43–55. pmid:20196982
- 15. Denninger JW, van Nieuwenhuizen AO, Wisniewski SR, Luther JF, Trivedi MH, Rush AJ, et al. Changes in depressive symptoms and social functioning in the sequenced treatment alternatives to relieve depression study. J Nerv Ment Dis. 2011;199(10):807–10. doi: 10.1097/NMD.0b013e31822fcbe2. pmid:21964277
- 16. Trivedi MH, Rush AJ, Wisniewski SR, Warden D, McKinney W, Downing M, et al. Factors associated with health-related quality of life among outpatients with major depressive disorder: a STAR*D report. J Clin Psychiatry. 2006;67(2):185–95. pmid:16566612
- 17. Dunn TW, Vittengl JR, Clark LA, Carmody T, Thase ME, Jarrett RB. Change in psychosocial functioning and depressive symptoms during acute-phase cognitive therapy for depression. Psychol Med. 2012;42(02):317–26.
- 18. Vittengl JR, Clark LA, Jarrett RB. Improvement in social-interpersonal functioning after cognitive therapy for recurrent depression. Psychol Med. 2004;34(4):643–58. doi: 10.1017/S0033291703001478. pmid:15099419
- 19. Solomon DA, Leon AC, Endicott J, Mueller TI, Coryell W, Shea MT, et al. Psychosocial impairment and recurrence of major depression. Compr Psychiatry. 2004;45(6):423–30. doi: 10.1016/j.comppsych.2004.07.002. pmid:15526252
- 20. Vittengl JR, Clark LA, Dunn TW, Jarrett RB. Reducing relapse and recurrence in unipolar depression: A comparative meta-analysis of cognitive-behavioral therapy's effects. J Consult Clinl Psychol. 2007;75(3):475–88.
- 21. Vittengl JR, Clark LA, Jarrett RB. Deterioration in psychosocial functioning predicts relapse/recurrence after cognitive therapy for depression. J Affect Disord. 2009;112(1):135–43.
- 22. Zimmerman M, McGlinchey JB, Posternak MA, Friedman M, Attiullah N, Boerescu D. How Should Remission From Depression Be Defined? The Depressed Patient’s Perspective. Am J Psychiatry. 2006;163(1):148–50. doi: 10.1176/appi.ajp.163.1.148. pmid:16390903
- 23. Rush AJ. Distinguishing functional from syndromal recovery: implications for clinical care and research. J Clin Psychiatry. 2015;76(6):e832–4. doi: 10.4088/JCP.15com09859. pmid:26132699
- 24. McKnight PE, Kashdan TB. The importance of functional impairment to mental health outcomes: a case for reassessing our goals in depression treatment research. Clin Psychol Rev. 2009;29(3):243–59. doi: 10.1016/j.cpr.2009.01.005. pmid:19269076
- 25. Uher R, Muthen B, Souery D, Mors O, Jaracz J, Placentino A, et al. Trajectories of change in depression severity during treatment with antidepressants. Psychol Med. 2010;40(8):1367–77. doi: 10.1017/S0033291709991528. pmid:19863842
- 26. Uher R, Mors O, Rietschel M, Rajewska-Rager A, Petrovic A, Zobel A, et al. Early and delayed onset of response to antidepressants in individual trajectories of change during treatment of major depression: a secondary analysis of data from the Genome-Based Therapeutic Drugs for Depression (GENDEP) study. J Clin Psychiatry. 2011;72(11):1478–84. doi: 10.4088/JCP.10m06419. pmid:22127194
- 27. Jha M, Minhajuddin A, Greer TL, Carmody T, Rush AJ, Trivedi MH. Early Improvement in Work Productivity Predicts Future Clinical Course in Depressed Outpatients: Findings from the CO-MED Trial. Am J Psychiatry. 2016;Aug 13: appiajp201616020176.
- 28. Ioannidis JPA. Why Most Published Research Findings Are False. PLoS Med. 2005;2(8):e124. doi: 10.1371/journal.pmed.0020124. pmid:16060722
- 29. Collaboration OS. Estimating the reproducibility of psychological science. Science. 2015;349(6251).
- 30. Greenberg T, Chase HW, Almeida JR, Stiffler R, Zevallos CR, Aslam HA, et al. Moderation of the Relationship Between Reward Expectancy and Prediction Error-Related Ventral Striatal Reactivity by Anhedonia in Unmedicated Major Depressive Disorder: Findings From the EMBARC Study. Am J Psychiatry. 2015;172(9):881–91. doi: 10.1176/appi.ajp.2015.14050594. pmid:26183698
- 31. Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54(5):573–83. pmid:12946886
- 32. Wisniewski SR, Rush AJ, Balasubramani GK, Trivedi MH, Nierenberg AA. Self-rated global measure of the frequency, intensity, and burden of side effects. J Psychiatr Pract. 2006;12(2):71–9. pmid:16728903
- 33. Rush AJ, Gullion CM, Basco MR, Jarrett RB, Trivedi MH. The Inventory of Depressive Symptomatology (IDS): psychometric properties. Psychol Med. 1996;26(3):477–86. pmid:8733206
- 34. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998.
- 35. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. pmid:14399272
- 36. Rush AJ, Bernstein IH, Trivedi MH, Carmody TJ, Wisniewski S, Mundt JC, et al. An Evaluation of the Quick Inventory of Depressive Symptomatology and the Hamilton Rating Scale for Depression: A Sequenced Treatment Alternatives to Relieve Depression Trial Report. Biol Psychiatry. 2006;59(6):493–501. doi: 10.1016/j.biopsych.2005.08.022. pmid:16199008
- 37. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T, et al. The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med. 2004;34(1):73–82. pmid:14971628
- 38. Mundt JC, Marks IM, Shear MK, Greist JH. The Work and Social Adjustment Scale: a simple measure of impairment in functioning. The Br J Psychiatry. 2002;180:461–4. pmid:11983645
- 39. Zahra D, Qureshi A, Henley W, Taylor R, Quinn C, Pooler J, et al. The work and social adjustment scale: reliability, sensitivity and value. Int J Psychiatry Clin Pract. 2014;18(2):131–8. doi: 10.3109/13651501.2014.894072. pmid:24527886
- 40. Ram N, Grimm KJ. Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups. Int J Behav Dev. 2009;33(6):565–76. doi: 10.1177/0165025409343765. pmid:23885133
- 41. Jones BL, Nagin DS. Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them. Sociological Methods & Research. 2007;35(4):542–71.
- 42. Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Meth Res. 2001;29(3):374–93.
- 43. Wardenaar KJ, Conradi H-J, de Jonge P. DATA-DRIVEN COURSE TRAJECTORIES IN PRIMARY CARE PATIENTS WITH MAJOR DEPRESSIVE DISORDER. Depress Anxiety. 2014;31(9):778–86. doi: 10.1002/da.22228. pmid:24390862
- 44. Gilmour H, Stranges S, Kaplan M, Feeny D, McFarland B, Huguet N, et al. Longitudinal trajectories of sleep duration in the general population. Health Rep. 2013;24(11):14–20. pmid:24259200
- 45. Hamlett A, Ryan L, Wolfinger R. On the use of PROC MIXED to estimate correlation in the presence of repeated measures. Proc Statistics and Data Analysis SAS Users Group International http://www2.sas.com/proceedings/sugi29/198-29 pdfhttp://www2.sas.com/proceedings/sugi29/198-29%20pdf Accessed Apr. 2004;19:2006.
- 46. Bland JM, Altman DG. Statistics Notes: Correlation, regression, and repeated data. BMJ. 1994;308(6933):896. pmid:8173371
- 47. Cabello M, Mellor-Marsá B, Sabariego C, Cieza A, Bickenbach J, Ayuso-Mateos JL. Psychosocial features of depression: A systematic literature review. J Affect Disord. 2012;141(1):22–33. doi: 10.1016/j.jad.2011.12.009. pmid:22209189
- 48. Greer TL, Trombello J. M., Rethorst C. D., Carmody T. J., Jha M. K., Liao A., Grannemann B. D., Chambliss H. O., Church T.S., Trivedi M. H. Improvements in psychosocial functioning and health-related quality of life following exercise augmentation in patients with treatment response but non-remitted major depressive disorder: Results from the TREAD study. Depress Anxiety. 2016 Sep;33(9):870–81. doi: 10.1002/da.22521. pmid:27164293
- 49. Carter MA, Dubois L, Tremblay MS, Taljaard M, Jones BL. Trajectories of childhood weight gain: the relative importance of local environment versus individual social and early life factors. PloS One. 2012;7(10):e47065. doi: 10.1371/journal.pone.0047065. pmid:23077545
- 50. Melchior M, Touchette E, Prokofyeva E, Chollet A, Fombonne E, Elidemir G, et al. Negative events in childhood predict trajectories of internalizing symptoms up to young adulthood: an 18-year longitudinal study. PloS One. 2014;9(12):e114526. doi: 10.1371/journal.pone.0114526. pmid:25485875
- 51. Liang J, Xu X, Quinones AR, Bennett JM, Ye W. Multiple trajectories of depressive symptoms in middle and late life: racial/ethnic variations. Psychol Aging. 2011;26(4):761–77. doi: 10.1037/a0023945. pmid:21875216
- 52. Arrandale VH, Koehoorn M, MacNab Y, Kennedy SM. Longitudinal analysis of respiratory symptoms in population studies with a focus on dyspnea in marine transportation workers. Int Arch Occup Environ Health. 2009;82(9):1097–105. doi: 10.1007/s00420-009-0412-y. pmid:19288268
- 53. Lin CH, Chou LS, Chen MC, Chen CC. The relationship between symptom relief and functional improvement during acute fluoxetine treatment for patients with major depressive disorder. J Affect Disord. 2015;182:115–20. doi: 10.1016/j.jad.2015.04.022. pmid:25985380