Data-driven course trajectories in primary care patients with major depressive disorder

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

Telephone support vs. self-guidance in an Internet-based self-administered psychological program for the treatment of depression: Protocol for a hybrid type 1 effectiveness-implementation randomized controlled trial

Background

Depression is already the leading psychological disability around the world, impairing daily life, well-being, and social functioning and leading to personal and social costs. Despite the effectiveness of Evidence-Based Psychological Practices (EBPP), a significant percentage of depressive individuals remain untreated, especially in Primary Care (PC) settings in Spain. There are numerous barriers that limit access to EBPPs, including high costs, professional training, and adherence problems. Information and Communication Technologies (ICTs) offer a cost-effective way to disseminate and scale EBPPs to address these barriers. The iCBT program Smiling is Fun has been demonstrated to be a cost-effective treatment for depression in various Randomized Control Trials. However, adherence and implementation problems in real-world settings need to be addressed. Implementation research can help evaluate these challenges by identifying facilitators and barriers to the implementation process in PC. In this regard, including human support has been pointed out as a possible key factor in addressing the population's mental health needs and promoting treatment adherence.

Objective

The current study aims to examine the effectiveness, adherence rates, and implementation process of Smiling is Fun to address depression in a PC setting considering the influence of telephone support vs no support.

Methods

The proposed research is a Hybrid Effectiveness-Implementation Type I study, with a two-armed randomized controlled design, which will test a clinical intervention for major depressive disorder while gathering information on its implementation in a real-world setting. The study will include adult patients with mild to moderate symptoms of depression. Participants will be randomly assigned to one of two groups: self-applied psychotherapy or self-applied psychotherapy with psychotherapeutic telephone support. The trial will recruit 110 patient participants, with a loss-to-follow-up rate of 30 %.

Discussion

A study protocol for a hybrid effectiveness-implementation study is presented with the aim to assess the implementation of Smiling is Fun for the treatment of depression in PC. The study evaluates the influence of telephone support during a self-administered intervention compared to unguided self-administration. The main goal is to address the barriers and facilitators of the implementation process and to promote treatment adherence. Ultimately, the results of the study could help in the uptake of sustainable resources so that the population could gain better access to psychological interventions in mental health services.

Registration

ClinicalTrials.gov; NCT06230237.

Long-term depressive symptom trajectories and related baseline characteristics in primary care patients: Analysis of the PsicAP clinical trial

BACKGROUND

There is heterogeneity in the long-term trajectories of depressive symptoms among patients. To date, there has been little effort to inform the long-term trajectory of symptom change and the factors associated with different trajectories. Such knowledge is key to treatment decision-making in primary care, where depression is a common reason for consultation. We aimed to identify distinct long-term trajectories of depressive symptoms and explore pre-treatment characteristics associated with them.

METHODS

A total of 483 patients from the PsicAP clinical trial were included. Growth mixture modeling was used to identify long-term distinct trajectories of depressive symptoms, and multinomial logistic regression models to explore associations between pre-treatment characteristics and trajectories.

RESULTS

Four trajectories were identified that best explained the observed response patterns: "recovery" (64.18%), "late recovery" (10.15%), "relapse" (13.67%), and "chronicity" (12%). There was a higher likelihood of following the recovery trajectory for patients who had received psychological treatment in addition to the treatment as usual. Chronicity was associated with higher depressive severity, comorbidity (generalized anxiety, panic, and somatic symptoms), taking antidepressants, higher emotional suppression, lower levels on life quality, and being older. Relapse was associated with higher depressive severity, somatic symptoms, and having basic education, and late recovery was associated with higher depressive severity, generalized anxiety symptoms, greater disability, and rumination.

CONCLUSIONS

There were different trajectories of depressive course and related prognostic factors among the patients. However, further research is needed before these findings can significantly influence care decisions.

A New Treatment Program: Focused Integrated Team-based Treatment Program for Bipolar Disorder (FITT-BD)

OBJECTIVE

Bipolar disorder (BD) is complicated by a dynamic, chronic course along with multiple comorbid psychiatric and medical conditions, making it challenging for clinicians to treat and patients to thrive. To efficiently manage the complexity of BD and help patients recover, we developed a Focused Integrated Team-based Treatment Program for Bipolar Disorder (FITT-BD). The purpose of this paper is to describe how we developed this clinic and the lessons we learned.

METHODS

We developed FITT-BD by integrating strategies from stepped care, collaborative care, and learning health care systems. We describe the rationale, details, and lessons learned in developing FITT-BD.

RESULTS

By integrating stepped care, collaborative care, and a learning health care system approach, FITT-BD aims to reduce barriers to care, leverage the expertise of a multidisciplinary treatment team, ensure patient-centeredness, and use assessments to inform and continuously improve outcomes in real time. We learned that there are challenges in the creation of a web-based application that tracks the treatment of patients within a network of hospitals.

CONCLUSIONS

The success of FITT-BD will be determined by the degree to which it can increase treatment access, improve treatment adherence, and help individuals with BD achieve their treatment goals. We expect that FITT-BD will improve outcomes in the context of ongoing clinical care.

PUBLIC HEALTH SIGNIFICANCE

The treatment of BD is challenging and complex. We propose a new treatment model for BD: FITT-BD. We expect that this program will be a patient-centered approach that improves outcomes in the context of ongoing clinical care for patients with BD.

Prognostic Value of Pathological Personality Traits for Treatment Outcome in Anxiety and Depressive Disorders: The Leiden Routine Outcome Monitoring Study

ABSTRACT

Previous studies have failed to take baseline severity into account when assessing the effects of pathological personality traits (PPT) on treatment outcome. This study assessed the prognostic value of PPT (Dimensional Assessment of Personality Pathology-Short Form) on treatment outcome (Brief Symptom Inventory [BSI-posttreatment]) among patients with depressive and/or anxiety disorders ( N = 5689). Baseline symptom level (BSI-pretreatment) was taken into account as a mediator or moderator variable. Results showed significant effects of PPT on outcome, of which Emotional Dysregulation demonstrated the largest association ( β = 0.43, p < 0.001). When including baseline BSI score as a mediator variable, a direct effect ( β = 0.11, p < 0.001) remained approximately one-third of the total effect. The effects of Emotional Dysregulation (interaction effect β = 0.061, p < 0.001) and Inhibition (interaction effect β = 0.062, p < 0.001), but not Compulsivity or Dissocial Behavior, were moderated by the baseline symptom level. PPT predicts higher symptom levels, both before and after treatment, but yields relatively small direct effects on symptom decline when the effect of pretreatment severity is taken into account.

Transdiagnostic development of internalizing psychopathology throughout the life course up to age 45: a World Mental Health Surveys report

BACKGROUND

Depressive and anxiety disorders are highly comorbid, which has been theorized to be due to an underlying internalizing vulnerability. We aimed to identify groups of participants with differing vulnerabilities by examining the course of internalizing psychopathology up to age 45.

METHODS

We used data from 24158 participants (aged 45+) in 23 population-based cross-sectional World Mental Health Surveys. Internalizing disorders were assessed with the Composite International Diagnostic Interview (CIDI). We applied latent class growth analysis (LCGA) and investigated the characteristics of identified classes using logistic or linear regression.

RESULTS

The best-fitting LCGA solution identified eight classes: a healthy class (81.9%), three childhood-onset classes with mild (3.7%), moderate (2.0%), or severe (1.1%) internalizing comorbidity, two puberty-onset classes with mild (4.0%) or moderate (1.4%) comorbidity, and two adult-onset classes with mild comorbidity (2.7% and 3.2%). The childhood-onset severe class had particularly unfavorable sociodemographic outcomes compared to the healthy class, with increased risks of being never or previously married (OR = 2.2 and 2.0, p < 0.001), not being employed (OR = 3.5, p < 0.001), and having a low/low-average income (OR = 2.2, p < 0.001). Moderate or severe (v. mild) comorbidity was associated with 12-month internalizing disorders (OR = 1.9 and 4.8, p < 0.001), disability (B = 1.1-2.3, p < 0.001), and suicidal ideation (OR = 4.2, p < 0.001 for severe comorbidity only). Adult (v. childhood) onset was associated with lower rates of 12-month internalizing disorders (OR = 0.2, p < 0.001).

CONCLUSIONS

We identified eight transdiagnostic trajectories of internalizing psychopathology. Unfavorable outcomes were concentrated in the 1% of participants with childhood onset and severe comorbidity. Early identification of this group may offer opportunities for preventive interventions.

The 9-year clinical course of depressive and anxiety disorders: New NESDA findings

BACKGROUND

In longitudinal research, switching between diagnoses should be considered when examining patients with depression and anxiety. We investigated course trajectories of affective disorders over a nine-year period, comparing a categorical approach using diagnoses to a dimensional approach using symptom severity.

METHOD

Patients with a current depressive and/or anxiety disorder at baseline (N = 1701) were selected from the Netherlands Study of Depression and Anxiety (NESDA). Using psychiatric diagnoses, we described 'consistently recovered,' 'intermittently recovered,' 'intermittently recurrent', and 'consistently chronic' at two-, four-, six-, and nine-year follow-up. Additionally, latent class growth analysis (LCGA) using depressive, anxiety, fear, and worry symptom severity scores was used to identify distinct classes.

RESULTS

Considering the categorical approach, 8.5% were chronic, 32.9% were intermittently recurrent, 37.6% were intermittently recovered, and 21.0% remained consistently recovered from any affective disorder at nine-year follow-up. In the dimensional approach, 66.6% were chronic, 25.9% showed partial recovery, and 7.6% had recovered.

LIMITATIONS

30.6% of patients were lost to follow-up. Diagnoses were rated by the interviewer and questionnaires were completed by the participant.

CONCLUSIONS

Using diagnoses alone as discrete categories to describe clinical course fails to fully capture the persistence of affective symptoms that were observed when using a dimensional approach. The enduring, fluctuating presence of subthreshold affective symptoms likely predisposes patients to frequent relapse. The commonness of subthreshold symptoms and their adverse impact on long-term prognoses deserve continuous clinical attention in mental health care as well further research.

Individual and common patterns in the order of symptom improvement during outpatient treatment for major depression

BACKGROUND

Research so far provided few clues on the order in which depressive symptoms typically remit during treatment. This study examined which depressive symptoms improve first, and whether symptoms changed before, simultaneous with, or after the core symptoms of depression (i.e., sad mood, loss of pleasure, and loss of interest).

METHODS

Participants were 176 patients with Major Depressive Disorder (MDD) receiving outpatient treatment (a combination of pharmacotherapy and psychological interventions) for depression. Participants filled out the Inventory of Depressive Symptomatology - Self Report (IDS-SR) for 16 to 20 consecutive weeks. For each symptom, the timing of onset of a persistent improvement was determined for each single-subject separately.

RESULTS

Which symptoms improved first differed markedly across patients. The core depression symptoms improved 1.5 to 2 times more often before (48% - 60%) than after (19% -28%) depressive cognitions ('view of myself' and 'view of the future'), anxiety symptoms ('feeling irritable' and 'feeling anxious / tense') and vegetative symptoms ('loss of energy', 'slowed down', and 'physical energy'). Only improvements in suicidal thoughts were more likely to occur before (46% - 48%) than after (29%) improvements in the depression core symptoms.

LIMITATIONS

Not all 'core depression-non-core symptom' combinations could be tested because some symptoms did not improve in a sufficient number of patients.

CONCLUSIONS

Which improvements mark the start of symptom remission differed between patients. Improvements in the core depression symptoms 'sad mood', 'loss of interest', and 'loss of pleasure' were more likely to occur before than after improvements in non-core symptoms.

Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach

The World Health Organization estimates that approximately 10 million people are newly diagnosed with dementia each year and a global prevalence of nearly 50 million persons with dementia (PwD). The vast majority of PwD living at home receive the majority of their care from informal familial caregivers. The quality of life (QOL) of familial caregivers may be significantly impacted by their caregiving responsibilities and resultant caregiver burden. A major contributor to caregiver burden is the random occurrence of agitation in PwD and familial caregivers' lack of preparedness to manage these episodes. Caregiver burden may be reduced if it is possible to forecast impending agitation episodes. In this study, we leverage data-driven deep learning models to predict agitation episodes in PwD. We used Long Short-Term Memory (LSTM), a deep learning class of algorithms, to forecast agitations up to 30 min before actual agitation events. In particular, we managed the missing data by estimating the missing values and compensated for the class imbalance challenge by down-sampling the majority class. The simulations were based on real-world data from Alzheimer's disease (AD) caregivers and PwD dyads home environments, including ambient noise level, illumination, room temperature, atmospheric pressure (Pa), and relative humidity. Our results show the efficacy of data-driven deep learning models in predicting agitation episodes in community-dwelling AD dyads with accuracy of 98.6% and recall (sensitivity) of 84.8%.

Trajectories of symptoms of anxiety and depression among people on sick leave with mood or anxiety disorders: Secondary analysis from a randomized controlled trial

BACKGROUND

Depression and anxiety are heterogenous disorders often combined into one entity in studies. Few studies have compared trajectories of depression and anxiety among clinically ill. We aimed to identify specific trajectories of depression, and anxiety and predictors of trajectory membership.

METHODS

Latent growth mixture modelling was carried out on data from the IPS-MA trial (n = 261), a supported employment intervention for people with mood or anxiety, to identify trajectories of depression and anxiety. Logistic regression was used to estimate predictors for trajectory membership. Associations between trajectory class and remission of comorbid depression or anxiety and return to work were also tested.

RESULTS

We identified three trajectories of depression and anxiety symptoms respectively; moderate-decreasing (60%), moderate-stable (26%), and low-stable (14%) depression and mild-decreasing (59%), moderate-decreasing (33%), and moderate-stable (8%) anxiety. The depression model showed low precision in class separation (entropy 0.66), hence, predictors of class membership were not estimated. For anxiety, lower age and higher levels of depressive symptoms were associated with a less desirable trajectory. Remission of comorbid depressive symptoms after two years differed significantly between classes (p < 0.000). Fewer had returned to work in the two moderate classes compared to the mild-decreasing anxiety class.

LIMITATIONS

Depression model not reliable. Only 80% of participants from original study included. Not able to distinguish between anxiety disorders.

CONCLUSION

Trajectories of anxiety confirm that, even after two years, a rather large proportion in the moderate-stable class had symptoms of moderate anxiety, moderate comorbid depressive symptoms, and less probability of having returned to work.

TRIAL REGISTRATION

ClinicalTrials.govNCT01721824.

Identifying crime victims vulnerable to persistent depressive symptoms: Results from a secondary analysis

BACKGROUND

Although depression has been extensively studied in relations to Intimate Partner Violence (IPV) and Childhood Maltreatment (CM), we still know little about its relationship with other types of violent crimes (e.g., physical assault, mugging) or how symptoms evolve over time.

METHODS

Using a clinical sample of 132 crime victims, this study sought to map out group-based symptoms trajectories using Latent Growth Curve Analysis (LCGA) over the course of one year.

RESULTS

Less than one month after having been assaulted, 30% of the sample reached the severe depression threshold. Symptoms tended to improve over time, with only 8% of participants reporting severe depression one year later. Analyses indicate the presence of three distinct profiles: one marked by severe and stable symptomatology over time (17%), the second characterized by a moderate level of symptoms that decreases exponentially over time (57%) and a third one describing a pattern of minimal depression throughout the year. People who suffered from persistent depression reported significantly lower levels of social support, scored higher on the ASDI and were also more likely to report some type of maltreatment in childhood but not IPV in adulthood, and appeared non-responsive to treatment.

LIMITATIONS

Results must be nuanced by the size (i.e., LCGA requirements and attrition) and nature of the sample (i.e., help-seeking victims only).

CONCLUSIONS

Although violence is a risk factor for depression, some individuals, especially those with a history of CM, appear to be more vulnerable to severe, persistent and treatment-resistant depression than others.

Measuring Primary Health Care Clinicians' Skills for Depression Management

Introduction: Primary health care clinicians play an important role in the management of depression. Thus, it is very important to have a valid and reliable assessment of the competences needed to manage depression in primary health care, with the use of clinical simulation providing such an opportunity. Objective: The present study describes the assessment of primary health care clinicians' depression-related skills through a series of objective structured clinical examination stations. Material and Methods: Clinicians from multi-professional teams for the management of depression at two primary health care clinics in Santiago, Chile, went through seven objective structured clinical examination stations, lasting 10 to 20 min each, to assess their depression-related skills. The clinical and communicative skills measured were in accordance with clinical guidelines. Standardized patients portrayed cases usually encountered in clinical practice, while expert raters evaluated clinicians' performance with standardized checklists. Results: Psychosocial clinicians performed better than biomedical clinicians in the assessed skills. The most notable results were as follows: a high level of accomplishment in the relationship with patient, medical anamnesis, health checkup, and lab test requests; heterogeneous performance in patient management according to screening results, feedback to the patient, and registration in clinical records; and major deficiencies in the differential diagnosis of bipolar disorder. Discussion: The objective structured clinical examinations administered provided an opportunity to perform an in-depth examination of the depression-related skills of primary health care clinicians, where flaws in the screening and diagnosis procedures used by biomedical clinicians were detected. Given the significant involvement of these types of clinicians in depression management, undergraduate-level and continuing health education opportunities are needed.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

OBJECTIVE

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use.

METHODS

A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks.

RESULTS

Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured.

CONCLUSION

Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.

Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors.

METHODS

For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes.

RESULTS

LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years.

CONCLUSION

Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice.

Variation in the 12-Month Treatment Trajectories of Children and Adolescents After a Diagnosis of Depression

IMPORTANCE

Depression during childhood and adolescence is heterogeneous. Treatment patterns are often examined in aggregate, yet there is substantial variability across individual treatment trajectories. Understanding this variability can help identify treatment gaps among youths with depression.

OBJECTIVE

To characterize heterogeneity in 12-month trajectories of psychotherapy and antidepressant treatment in youths with depression.

DESIGN, SETTING, AND PARTICIPANTS

This is a longitudinal-cohort study of youths 18 years or younger with a new diagnosis of depression and at least 12 months of follow-up following diagnosis, as determined from commercial insurance claims filed from 2007 to 2014. Latent class models were fit to summary measures of psychotherapy and antidepressant use in the 12 months following the index diagnosis. We examined variation in baseline health, health care utilization, and health outcomes across classes with similar patterns of psychotherapy and antidepressant use. Data analysis took place between June 2016 and March 2017.

MAIN OUTCOMES AND MEASURES

Psychotherapy and antidepressant use.

RESULTS

The cohort included 84 909 individuals with a mean (SD) age at index diagnosis of 15.0 (2.6) years, of whom 49 995 (59%) were female. Attention-deficit/hyperactivity disorder (n = 14 625; 17%) and anxiety (n = 12 358; 15%) were the most common comorbid diagnoses. During the assessment period, 59 023 individuals (70%) received psychotherapy at any point, and 33 997 individuals (40%) were dispensed antidepressants at any point. Eight classes with distinct treatment trajectories were identified, which we classified into 4 broad groups: 3 classes that received dual therapy (n = 18 710; 22%), 2 classes that received antidepressant monotherapy (n = 15 287; 18%), 2 classes that received psychotherapy monotherapy (n = 40 313; 48%) and 1 class that received no treatment (n = 10 599; 13%). The most common class received psychotherapy monotherapy (n = 35 243; 42%) and had the lowest incidence of attempted suicide (0.8 per 100 person-years [PY]) and inpatient hospitalization (3.5 per 100 PY) during the assessment period and postassessment period (0.5 per 100 PY and 1.3 per 100 PY, respectively). The group receiving dual therapy had the highest incidence of attempted suicide during the assessment period (4.7-7.1 per 100 PY, depending on the class) and postassessment period (1.5-1.7 per 100 PY).

CONCLUSIONS AND RELEVANCE

In our sample, 13% of youths received no treatment, and 18% received antidepressants without concomitant psychotherapy. Summary measures of treatment can mask informative patterns of psychotherapy and antidepressant use. Latent class analysis can be used to identify subgroups of individuals with similar treatment trajectories and help identify treatment gaps under current practice patterns.

Prognosis research ideally should measure time-varying predictors at their intended moment of use

BACKGROUND

Prognosis research studies (e.g. those deriving prognostic models or examining potential predictors of outcome) often collect information on time-varying predictors after their intended moment of use, sometimes using a measurement method different to that which would be used. We aimed to illustrate how estimates of predictor-outcome associations and prognostic model performance obtained from such studies may differ to those at the earlier, intended moment of use.

METHODS

We analysed data from two primary care cohorts of patients consulting for non-inflammatory musculoskeletal conditions: the Prognostic Research Study (PROG-RES: n = 296, aged >50 years) and the Primary care Osteoarthritis Screening Trial (POST: n = 756, >45 years). Both cohorts had collected comparable information on a potentially important time-varying predictor (current pain intensity: 0-10 numerical rating scale), other predictors (age, gender, practice) and outcome (patient-perceived non-recovery at 6 months). Using logistic regression models, we compared the direction and magnitude of predictor-outcome associations and model performance measures under two scenarios: (i) current pain intensity ascertained by the treating general practitioner in the consultation (the intended moment of use) and (ii) current pain intensity ascertained by a questionnaire mailed several days after the consultation.

RESULTS

In both cohorts, the predictor-outcome association was substantially weaker for pain measured at the consultation (OR (95% CI): PROG-RES 1.06 (0.95, 1.18); POST 1.04 (0.96, 1.12)) than for pain measured in the questionnaire (PROG-RES 1.34 (1.20, 1.48); POST 1.26 (1.18, 1.34)). The c-statistic of the multivariable model was lower when pain was measured at the consultation (c-statistic (95% CI): PROG-RES 0.57 (0.51, 0.64); POST 0.66 (0.62, 0.70)) than when pain was measured in the questionnaire (PROG-RES 0.69 (0.63, 0.75); POST 0.72 (0.68, 0.76)), reflecting the lower OR for pain at the consultation.

CONCLUSIONS

Prognostic research studies ideally should measure time-varying predictors at their intended moment of use and using the intended measurement method. Otherwise, they may produce substantially different estimates of predictor-outcome associations and model performance. Researchers should report when, how and where predictors were measured and identify any significant departures from their intended use that may limit the applicability of findings in practice.

TRIAL REGISTRATION

The protocol for the PROG-RES cohort data collection and primary analysis has been published in an open-access journal (Mallen et al., BMC Musculoskelet Disord 7:84, 2006). The POST trial was registered (ISRCTN40721988; date of registration: 21 June 2011; date of enrolment of the first participant: 3 October 2011) and had a pre-specified protocol covering primary analysis. There was no published protocol for the current secondary analyses presented in this manuscript.

Early Improvement in Psychosocial Function Predicts Longer-Term Symptomatic Remission in Depressed Patients

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.

Defined symptom-change trajectories during acute-phase cognitive therapy for depression predict better longitudinal outcomes

BACKGROUND

Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but responders experience varying post-acute outcomes (e.g., relapse vs. recovery). Responders' symptom-change trajectories during response to acute-phase CT may predict longer term outcomes.

METHOD

We studied adult outpatients (N = 220) with recurrent MDD who responded to CT but had residual symptoms. Responders with linear (steady improvement), log-linear (quicker improvement earlier and slower later), one-step (a single, relatively large, stable improvement between adjacent assessments), or undefined (not linear, log-linear, or one-step) symptom trajectories were assessed every 4 months for 32 additional months.

RESULTS

Defined (linear, log-linear, one-step) versus undefined acute-phase trajectories predicted lower depressive symptoms (d = 0.36), lower weekly probability of being in a major depressive episode (OR = 0.46), higher weekly probabilities of remission (OR = 1.93) and recovery (OR = 2.35), less hopelessness (d = 0.41), fewer dysfunctional attitudes (d = 0.31), and better social adjustment (d = 0.32) for 32 months after acute-phase CT. Differences among defined trajectory groups were nonsignificant.

CONCLUSIONS

Responding to acute-phase CT with a defined trajectory (orderly pattern) of symptom reduction predicts better longer term outcomes, but which defined trajectory (linear, log-linear, or one-step) appears unimportant. Frequent measurement of depressive symptoms to identify un/defined CT response trajectories may clarify need for continued clinical monitoring and treatment.

Predicting Response Trajectories during Cognitive-Behavioural Therapy for Panic Disorder: No Association with the BDNF Gene or Childhood Maltreatment

BACKGROUND

Anxiety disorders are highly prevalent and result in low quality of life and a high social and economic cost. The efficacy of cognitive-behavioural therapy (CBT) for anxiety disorders is well established, but a substantial proportion of patients do not respond to this treatment. Understanding which genetic and environmental factors are responsible for this differential response to treatment is a key step towards "personalized medicine". Based on previous research, our objective was to test whether the BDNF Val66Met polymorphism and/or childhood maltreatment are associated with response trajectories during exposure-based CBT for panic disorder (PD).

METHOD

We used Growth Mixture Modeling to identify latent classes of change (response trajectories) in patients with PD (N = 97) who underwent group manualized exposure-based CBT. We conducted logistic regression to investigate the effect on these trajectories of the BDNF Val66Met polymorphism and two different types of childhood maltreatment, abuse and neglect.

RESULTS

We identified two response trajectories ("high response" and "low response"), and found that they were not significantly associated with either the genetic (BDNF Val66Met polymorphism) or childhood trauma-related variables of interest, nor with an interaction between these variables.

CONCLUSIONS

We found no evidence to support an effect of the BDNF gene or childhood trauma-related variables on CBT outcome in PD. Future studies in this field may benefit from looking at other genotypes or using different (e.g. whole-genome) approaches.

Heterogeneity in 10-Year Course Trajectories of Moderate to Severe Major Depressive Disorder: A Danish National Register-Based Study

IMPORTANCE

Evidence suggests that long-term trajectories of major depressive disorder (MDD) are heterogeneous. The Danish Psychiatric Central Research Register (DPCRR) provides a rare opportunity to examine patterns and correlates of long-term trajectories in a large sample of patients with moderate to severe MDD.

OBJECTIVE

To characterize patterns and correlates of 10-year course trajectories of MDD in the DPCRR.

DESIGN, SETTING, AND PARTICIPANTS

A cohort containing 11 640 individuals born in Denmark in 1955 or later with their first recorded MDD diagnosis in the DPCRR between 1995 and 2002 was established. Patients were followed for 10 years from the date of their initial MDD diagnosis. Data were obtained from Danish civil and psychiatric national registers in June 2013 and were analyzed from April 4, 2014, to December 17, 2015. Correlates of trajectory class membership were sex, characteristics of the first recorded MDD episode (ie, age, severity, inpatient treatment, and record of suicide attempt or self-harm), and psychiatric diagnoses in parents (ie, depression, bipolar disorder, schizophrenia-spectrum disorders, substance abuse, and anxiety or somatoform disorders).

MAIN OUTCOMES AND MEASURES

The outcome variable was past-year contact at a psychiatric hospital with a main diagnosis of MDD during each of the 10 years following the initial MDD diagnosis. Trajectories were modeled using latent class growth analysis.

RESULTS

The sample included 11 640 individuals (7493 [64.4%] women) aged 18 to 48 years (mean [SD], 31.4 [7.3]) at their first recorded MDD diagnosis. Four trajectory classes were identified: brief contact (77.0%) (characterized by low probability of contact after 2 years); prolonged initial contact (12.8%) (characterized by high decreasing probability of contact during the first 5 years); later reentry (7.1%) (characterized by moderate probability of contact during the second 5 years); and persistent contact (3.1%) (characterized by high or moderate probability of contact throughout). Female sex (odds ratio [OR] range, 1.82-2.22), inpatient treatment (OR range, 1.40-1.50), and severity at first recorded MDD episode (OR range: moderate, 1.61-1.84; severe, 1.93-2.23; and psychotic, 2.73-3.07) were associated with more severe trajectories. Parental anxiety (OR, 1.34 [95% CI, 1.10-1.63]) and depression (OR, 1.63 [95% CI, 1.28-2.09]) were associated with the prolonged initial contact and later reentry classes, respectively. Parental schizophrenia was associated with the persistent contact class (OR range, 2.55-3.04).

CONCLUSIONS AND RELEVANCE

Most people treated for moderate to severe MDD in Danish psychiatric hospitals do not receive additional MDD treatment after 2 years; however, a minority receive specialty treatment for up to a decade. Observable heterogeneity in the course may be indicative of underlying etiologic differences.

Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity

BACKGROUND

Depression heterogeneity has hampered development of adequate prognostic models. Therefore, more homogeneous clinical entities (e.g. dimensions, subtypes) have been developed, but their differentiating potential is limited because neither captures all relevant variation across persons, symptoms and time. To address this, three-mode Principal Component Analysis (3MPCA) was previously applied to capture person-, symptom- and time-level variation in a single model (Monden et al., 2015). This study evaluated the added prognostic value of such an integrated model for longer-term depression outcomes.

METHODS

The Beck Depression Inventory (BDI) was administered quarterly for two years to major depressive disorder outpatients participating in a randomized controlled trial. A previously developed 3MPCA model decomposed the data into 2 symptom-components ('somatic-affective', 'cognitive'), 2 time-components ('recovering', 'persisting') and 3 person-components ('severe non-persisting depression', 'somatic depression' and 'cognitive depression'). The predictive value of the 3MPCA model for BDI scores at 3-year (n=136) and 11-year follow-up (n=145) was compared with traditional latent variable models and traditional prognostic factors (e.g. baseline BDI component scores, personality).

RESULTS

3MPCA components predicted 41% and 36% of the BDI variance at 3- and 11-year follow-up, respectively. A latent class model, growth mixture model and other known prognostic variables predicted 4-32% and 3-24% of the BDI variance at 3- and 11-year follow-up, respectively.

LIMITATIONS

Only primary care patients were included. There was no independent validation sample.

CONCLUSION

Accounting for depression heterogeneity at the person-, symptom- and time-level improves longer-term predictions of depression severity, underlining the potential of this approach for developing better prognostic models.

Data-driven atypical profiles of depressive symptoms: Identification and validation in a large cohort

BACKGROUND

Atypical response behavior on depression questionnaires may invalidate depression severity measurements. This study aimed to identify and investigate atypical profiles of depressive symptoms using a data-driven approach based on the item response theory (IRT).

METHODS

A large cohort of participants completed the Inventory of Depressive Symptomatology self-report (IDS-SR) at baseline (n=2329) and two-year follow-up (n=1971). Person-fit statistics were used to quantify how strongly each patient׳s observed symptom profile deviated from the expected profile given the group-based IRT model. Identified atypical profiles were investigated in terms of reported symptoms, external correlates and temporal consistency.

RESULTS

Compared to others, atypical responders (6.8%) showed different symptom profiles, with higher 'mood reactivity' and 'suicidal ideation' and lower levels of mild symptoms like 'sad mood'. Atypical responding was associated with more medication use (especially tricyclic antidepressants: OR=1.5), less somatization (OR=0.8), anxiety severity (OR=0.8) and anxiety diagnoses (OR=0.8-0.9), and was shown relatively stable (29.0%) over time.

LIMITATIONS

This is a methodological proof-of-principal based on the IDS-SR in outpatients. Implementation studies are needed.

CONCLUSION

Person-fit statistics can be used to identify patients who report atypical patterns of depressive symptoms. In research and clinical practice, the extra diagnostic information provided by person-fit statistics could help determine if respondents׳ depression severity scores are interpretable or should be augmented with additional information.

Symptom-specific course trajectories and their determinants in primary care patients with Major Depressive Disorder: Evidence for two etiologically distinct prototypes

BACKGROUND

The course-heterogeneity of Major Depressive Disorder (MDD) hampers development of better prognostic models. Although latent class growth analyses (LCGA) have been used to explain course-heterogeneity, such analyses have failed to also account for symptom-heterogeneity of depressive symptoms. Therefore, the aim was to identify more specific data-driven subgroups based on patterns of course-trajectories on different depressive symptom domains.

METHODS

In primary care MDD patients (n=205), the presence of the MDD criterion symptoms was determined for each week during a year. Weekly 'mood/cognition' (MC) and 'somatic' (SOM) scores were computed and parallel processes-LCGA (PP-LCGA) was used to identify subgroups based on the course on these domains. The classes׳ associations with baseline predictors and 2-/3-year outcomes were investigated.

RESULTS

PP-LCGA identified four classes: quick recovery, persisting SOM, persisting MC, and persisting SOM+MC (chronic). Persisting SOM was specifically predicted by higher baseline somatic symptomatology and somatization, and was associated with more somatic depressive symptomatology at long-term follow-up. Persisting MC was specifically predicted by higher depressive severity, thinking insufficiencies, neuroticism, loneliness and lower self-esteem, and was associated with lower mental health related quality of life and more mood/cognitive depressive symptomatology at follow-up.

LIMITATIONS

The sample was small and contained only primary care MDD patients. The weekly depression assessments were collected retrospectively at 3-month intervals.

CONCLUSIONS

The results indicate that there are two specific prototypes of depression, characterized by either persisting MC or persisting SOM, which have different sets of associated prognostic factors and long-term outcomes, and could have different etiological mechanisms.