Estimating probability of sustained recovery from mild to moderate depression in primary care: evidence from the THREAD study

Development and validation of a prediction score to assess the risk of depression in primary care

BACKGROUND

Major depression is the most frequent psychiatric disorder and primary care is a crucial setting for its early recognition. This study aimed to develop and validate the DEP-HScore as a tool to predict depression risk in primary care and increase awareness and investigation of this condition among General Practitioners (GPs).

METHODS

The DEP-HScore was developed using data from the Italian Health Search Database (HSD). A cohort of 903,748 patients aged 18 years or older was selected and followed until the occurrence of depression, death or end of data availability (December 2019). Demographics, somatic signs/symptoms and psychiatric/medical comorbidities were entered in a multivariate Cox regression to predict the occurrence of depression. The coefficients formed the DEP-HScore for individual patients. Explained variance (pseudo-R2), discrimination (AUC) and calibration (slope estimating predicted-observed risk relationship) assessed the prediction accuracy.

RESULTS

The DEP-HScore explained 18.1 % of the variation in occurrence of depression and the discrimination value was equal to 67 %. With an event horizon of three months, the slope and intercept were not significantly different from the ideal calibration.

LIMITATIONS

The DEP-HScore has not been tested in other settings. Furthermore, the model was characterized by limited calibration performance when the risk of depression was estimated at the 1-year follow-up.

CONCLUSIONS

The DEP-HScore is reliable tool that could be implemented in primary care settings to evaluate the risk of depression, thus enabling prompt and suitable investigations to verify the presence of this condition.

The Effects of Patient Preference on Clinical Outcome, Satisfaction and Adherence Within the Treatment of Anxiety and Depression: A Meta-Analysis

BACKGROUND

Taking patient preference into consideration has received increased attention in the last decades. We conducted a meta-analysis to estimate the effects of patient preference on clinical outcome, satisfaction and adherence regarding treatment of depression and anxiety.

METHODS

Pubmed, Embase, PsycINFO and Scopus were searched for (cluster) randomized controlled trials. Twenty-six randomized controlled clinical trials were included, comprising 3670 participants, examining the effect of patient preference regarding treatment of anxiety and depression on clinical outcome, satisfaction and/or adherence.

RESULTS

No effect of patient preference was found on clinical outcome [d = 0.06, 95% CI = (-0.03, 0.15), p = 0.16, n = 23 studies]. A small effect of patient preference was found on treatment satisfaction [d = 0.33, 95% CI = (0.08, 0.59), p = 0.01, n = 6 studies] and on treatment adherence [OR = 1.55, 95% CI = (1.28, 1.87), p < 0.001, n = 22 studies].

LIMITATIONS

Patient preference is a heterogeneous concept, future studies should strive to equalize operationalization of preference. Subgroup analyses within this study should be interpreted with caution because the amount of studies per analysed subgroup was generally low. Most studies included in this meta-analysis focused on patients with depression. The small number of studies (n = 6) on satisfaction, prevents us from drawing firm conclusions.

CONCLUSIONS

While this meta-analysis did not find a positive effect of considering patient preference on clinical outcome, it was associated with slightly better treatment satisfaction and adherence. Accommodating preference of patients with anxiety and depression can improve treatment.

TRIAL REGISTRATION

PROSPERO: CRD42020172556.

Predicting clinically significant response to primary care treatment for depression from electronic health records of veterans

OBJECTIVE

To reduce delays in referral to specialty mental health care, we evaluated clinical prediction models estimating the likelihood of response to primary care treatment of depression in the VA healthcare system.

METHODS

We included patients with a primary care depression diagnosis between October 1, 2015 and December 31, 2017, an initial PHQ-9 score ≥ 10 within 30 days, a follow-up PHQ-9 score within 2-8 months, and no specialty mental health care within three months prior to depression diagnosis. We evaluated eight ordinary least squares regression models, each with a different procedure for selecting predictors of percentage change in PHQ-9 score from baseline to follow-up. Predictors included patient characteristics from electronic health records and neighborhood characteristics from US census data. We repeated each modeling procedure 1,000 times, using different training and validation sets of patients. We used R², RMSE, and MAE to evaluate model performance.

RESULTS

The final cohort included 3,464 patients. The two best performing models included multiple iterations of backwards stepwise variable selection with R² of 0.07, RMSE of 41.45, MAE of 33.30; and R² of 0.07, RMSE of 41.39, MAE of 33.28.

LIMITATIONS

Wide follow-up interval, possibility of misclassification error due to use of EHR data.

CONCLUSIONS

Model performance did not suggest its use as a guide in clinical decision-making. Future research should explore whether obtaining additional risk factor data from patients (e.g., duration of symptoms) or modeling PHQ-9 scores over a narrower time interval improves performance of clinical risk prediction tools for depression.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults

BACKGROUND

Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence.

OBJECTIVES

To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery.

SEARCH METHODS

We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status .

SELECTION CRITERIA

We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews.

MAIN RESULTS

We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model.

AUTHORS' CONCLUSIONS

Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Association of Patient Treatment Preference With Dropout and Clinical Outcomes in Adult Psychosocial Mental Health Interventions: A Systematic Review and Meta-analysis

IMPORTANCE

Receiving a preferred treatment has previously been associated with lower dropout rates and better clinical outcomes, but this scenario has not been investigated specifically for psychosocial interventions for patients with a mental health diagnosis.

OBJECTIVE

To assess the association of patient treatment preference with dropout and clinical outcomes in adult psychosocial mental health interventions via a systematic review and meta-analysis.

DATA SOURCES

The Cochrane Library, Embase, PubMed, PsychINFO, Scopus, Web of Science, Nice HDAS (Healthcare Databases Advanced Search), Google Scholar, BASE (Bielefeld Academic Search Engine), Semantic Scholar, and OpenGrey were searched from inception to July 20, 2018, and updated on June 10, 2019.

STUDY SELECTION

Studies were eligible if they (1) were a randomized clinical trial; (2) involved participants older than 18 years; (3) involved participants with mental health diagnoses; (4) reported data from a group of participants who received their preferred treatment and a group who received their nonpreferred treatment or who were not given a choice; and (5) offered at least 1 psychosocial intervention.

DATA EXTRACTION AND SYNTHESIS

Two researchers extracted study data for attendance, dropout, and clinical outcomes independently. Both assessed the risk of bias according to the Cochrane tool. Data were pooled using random-effects meta-analyses.

MAIN OUTCOMES AND MEASURES

The following 7 outcomes were examined: attendance, dropout, therapeutic alliance, depression and anxiety outcomes, global outcomes, treatment satisfaction, and remission.

RESULTS

A total of 7341 articles were identified, with 34 eligible for inclusion. Twenty-nine articles were included in the meta-analyses comprising 5294 participants. Receiving a preferred psychosocial mental health treatment had a medium positive association with dropout rates (relative risk, 0.62; 0.48-0.80; P < .001; I2 = 44.6%) and therapeutic alliance (Cohen d = 0.48; 0.15-0.82; P = .01; I2 = 20.4%). There was no evidence of a significant association with other outcomes.

CONCLUSIONS AND RELEVANCE

This is the first review, to our knowledge, examining the association of receiving a preferred psychosocial mental health treatment with both engagement and outcomes for patients with a mental health diagnosis. Patients with mental health diagnoses who received their preferred treatment demonstrated a lower dropout rate from treatment and higher therapeutic alliance scores. These findings underline the need to accommodate patient preference in mental health services to maximize treatment uptake and reduce financial costs of premature dropout and disengagement.

Tree analysis modeling of the associations between PHQ-9 depressive symptoms and doctor diagnosis of depression in primary care

BACKGROUND

The aim of this study was to explore the relationship between patient self-reported Patient Health Questionnaire-9 (PHQ-9) symptoms and doctor diagnosis of depression using a tree analysis approach.

METHODS

This was a secondary analysis on a dataset obtained from 10 179 adult primary care patients and 59 primary care physicians (PCPs) across Hong Kong. Patients completed a waiting room survey collecting data on socio-demographics and the PHQ-9. Blinded doctors documented whether they thought the patient had depression. Data were analyzed using multiple logistic regression and conditional inference decision tree modeling.

RESULTS

PCPs diagnosed 594 patients with depression. Logistic regression identified gender, age, employment status, past history of depression, family history of mental illness and recent doctor visit as factors associated with a depression diagnosis. Tree analyses revealed different pathways of association between PHQ-9 symptoms and depression diagnosis for patients with and without past depression. The PHQ-9 symptom model revealed low mood, sense of worthlessness, fatigue, sleep disturbance and functional impairment as early classifiers. The PHQ-9 total score model revealed cut-off scores of &gt;12 and &gt;15 were most frequently associated with depression diagnoses in patients with and without past depression.

CONCLUSIONS

A past history of depression is the most significant factor associated with the diagnosis of depression. PCPs appear to utilize a hypothetical-deductive problem-solving approach incorporating pre-test probability, with different associated factors for patients with and without past depression. Diagnostic thresholds may be too low for patients with past depression and too high for those without, potentially leading to over and under diagnosis of depression.

Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study

BACKGROUND

Depression trajectories among primary care patients are highly variable, making it difficult to identify patients that require intensive treatments or those that are likely to spontaneously remit. Currently, there are no easily implementable tools clinicians can use to stratify patients with depressive symptoms into different treatments according to their likely depression trajectory. We aimed to develop a prognostic tool to predict future depression severity among primary care patients with current depressive symptoms at three months.

METHODS

Patient-reported data from the diamond study, a prospective cohort of 593 primary care patients with depressive symptoms attending 30 Australian general practices. Participants responded affirmatively to at least one of the first two PHQ-9 items. Twenty predictors were pre-selected by expert consensus based on reliability, ease of administration, likely patient acceptability, and international applicability. Multivariable mixed effects linear regression was used to build the model.

RESULTS

The prognostic model included eight baseline predictors: sex, depressive symptoms, anxiety, history of depression, self-rated health, chronic physical illness, living alone, and perceived ability to manage on available income. Discrimination (c-statistic =0.74; 95% CI: 0.70-0.78) and calibration (agreement between predicted and observed symptom scores) were acceptable and comparable to other prognostic models in primary care.

LIMITATIONS

More complex model was not feasible because of modest sample size. Validation studies needed to confirm model performance in new primary care attendees.

CONCLUSION

A brief, easily administered algorithm predicting the severity of depressive symptoms has potential to assist clinicians to tailor treatment for adult primary care patients with current depressive symptoms.

Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial

BACKGROUND

Depression is a highly prevalent and costly disorder. Effective treatments are available but are not always delivered to the right person at the right time, with both under- and over-treatment a problem. Up to half the patients presenting to general practice report symptoms of depression, but general practitioners have no systematic way of efficiently identifying level of need and allocating treatment accordingly. Therefore, our team developed a new clinical prediction tool (CPT) to assist with this task. The CPT predicts depressive symptom severity in three months' time and based on these scores classifies individuals into three groups (minimal/mild, moderate, severe), then provides a matched treatment recommendation. This study aims to test whether using the CPT reduces depressive symptoms at three months compared with usual care.

METHODS

The Target-D study is an individually randomized controlled trial. Participants will be 1320 general practice patients with depressive symptoms who will be approached in the practice waiting room by a research assistant and invited to complete eligibility screening on an iPad. Eligible patients will provide informed consent and complete the CPT on a purpose-built website. A computer-generated allocation sequence stratified by practice and depressive symptom severity group, will randomly assign participants to intervention (treatment recommendation matched to predicted depressive symptom severity group) or comparison (usual care plus Target-D attention control) arms. Follow-up assessments will be completed online at three and 12 months. The primary outcome is depressive symptom severity at three months. Secondary outcomes include anxiety, mental health self-efficacy, quality of life, and cost-effectiveness. Intention-to-treat analyses will test for differences in outcome means between study arms overall and by depressive symptom severity group.

DISCUSSION

To our knowledge, this is the first depressive symptom stratification tool designed for primary care which takes a prognosis-based approach to provide a tailored treatment recommendation. If shown to be effective, this tool could be used to assist general practitioners to implement stepped mental-healthcare models and contribute to a more efficient and effective mental health system.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR 12616000537459 ). Retrospectively registered on 27 April 2016. See Additional file 1 for trial registration data.

Impact of functional and structural social relationships on two year depression outcomes: A multivariate analysis

BACKGROUND

High rates of persistent depression highlight the need to identify the risk factors associated with poor depression outcomes and to provide targeted interventions to people at high risk. Although social relationships have been implicated in depression course, interventions targeting social relationships have been disappointing. Possibly, interventions have targeted the wrong elements of relationships. Alternatively, the statistical association between relationships and depression course is not causal, but due to shared variance with other factors. We investigated whether elements of social relationships predict major depressive episode (MDE) when multiple relevant variables are considered.

METHOD

Data is from a longitudinal study of primary care patients with depressive symptoms. 494 participants completed questionnaires at baseline and a depression measure (PHQ-9) two years later. Baseline measures included functional (i.e. quality) and structural (i.e. quantity) social relationships, depression, neuroticism, chronic illness, alcohol abuse, childhood abuse, partner violence and sociodemographic characteristics. Logistic regression with generalised estimating equations was used to estimate the association between social relationships and MDE.

RESULTS

Both functional and structural social relationships predicted MDE in univariate analysis. Only functional social relationships remained significant in multivariate analysis (OR: 0.87; 95%CI: 0.79-0.97; p=0.01). Other unique predictors of MDE were baseline depression severity, neuroticism, childhood sexual abuse and intimate partner violence.

LIMITATIONS

We did not assess how a person's position in their depression trajectory influenced the association between social relationships and depression.

CONCLUSIONS

Interventions targeting relationship quality may be part of a personalised treatment plan for people at high risk due of persistent depression due to poor social relationships.

Understanding the non-pharmacological correlates of self-reported efficacy of antidepressants

OBJECTIVE

To explore the non-pharmacological correlates of the perceived effectiveness of antidepressants (ADs), thereby enhancing understanding of the mechanisms involved in recovery from depression while taking ADs.

METHOD

An online survey was completed by 1781 New Zealand adults who had taken ADs in the previous 5 years.

RESULTS

All 18 psychosocial variables measured were associated with depression reduction, and 16 with improved quality of life (QoL). Logistic regression models revealed that the quality of the relationship with the prescriber was related to both depression reduction and improved QoL. In addition, depression reduction was related to younger age, higher income, being fully informed about ADs by the prescriber, fewer social causal beliefs for depression and not having lost a loved one in the 2 months prior to prescription. Furthermore, both outcome measures were positively related to belief in 'chemical' rather than 'placebo' effects.

CONCLUSION

There are multiple non-pharmacological processes involved in recovery while taking ADs. Enhancing them, for example focusing on the prescriber-patient relationship and giving more information, may enhance recovery rates, with or without ADs.

Refining cost-effectiveness analyses using the net benefit approach and econometric methods: an example from a trial of anti-depressant treatment

BACKGROUND

Economic evaluation analyses can be enhanced by employing regression methods, allowing for the identification of important sub-groups and to adjust for imperfect randomisation in clinical trials or to analyse non-randomised data.

AIMS

To explore the benefits of combining regression techniques and the standard Bayesian approach to refine cost-effectiveness analyses using data from randomised clinical trials.

METHOD

Data from a randomised trial of anti-depressant treatment were analysed and a regression model was used to explore the factors that have an impact on the net benefit (NB) statistic with the aim of using these findings to adjust the cost-effectiveness acceptability curves. Exploratory sub-samples' analyses were carried out to explore possible differences in cost-effectiveness. Results The analysis found that having suffered a previous similar depression is strongly correlated with a lower NB, independent of the outcome measure or follow-up point. In patients with previous similar depression, adding an selective serotonin reuptake inhibitors (SSRI) to supportive care for mild-to-moderate depression is probably cost-effective at the level used by the English National Institute for Health and Clinical Excellence to make recommendations.

CONCLUSIONS

This analysis highlights the need for incorporation of econometric methods into cost-effectiveness analyses using the NB approach.