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

Model of integrated mental health video consultations for people with depression or anxiety in primary care (PROVIDE-C): assessor masked, multicentre, randomised controlled trial

OBJECTIVE

To evaluate whether an integrated mental health video consultation approach (PROVIDE model) can improve symptoms compared with usual care in adults with depression and anxiety disorders attending primary care.

DESIGN

Assessor masked, multicentre, randomised controlled trial (PROVIDE-C).

SETTING

In 29 primary care practices in Germany, working remotely online from one trial hub.

PARTICIPANTS

376 adults (18-81 years) who presented to their general practitioner (GP) with depression or anxiety, or both.

INTERVENTION

Participants were randomised (1:1) to receive the PROVIDE model (n=187) or usual care (n=189). Usual care was provided by GPs through interventions such as brief counselling and psychotropic medication prescriptions and may or may not have included referrals to mental health specialists. The PROVIDE model comprised transdiagnostic treatment provided through five real-time video sessions between the patient at the primary care practice and a mental health specialist at an offsite location.

MAIN OUTCOME MEASURES

The primary outcome was the absolute change in the mean severity of depressive and anxiety symptoms measured using the patient health questionnaire anxiety and depression scale (PHQ-ADS) at six months, in the intention-to-treat population. Secondary outcomes, measured at six and 12 months, included PHQ-ADS subscores, psychological distress related to somatic symptoms, recovery, health related quality of life, quality and patient centredness of chronic illness care, and adverse events.

RESULTS

Between 24 March 2020 and 23 November 2021, 376 patients were randomised into treatment groups. Mean age was 45 years (standard deviation (SD) 14), 63% of the participants were female, and mean PHQ-ADS-score was 26 points (SD 7.6). Compared with usual care, the PROVIDE intervention led to improvements in severity of depressive and anxiety symptom (adjusted mean change difference in the PHQ-ADS score -2.4 points (95% confidence interval -4.5 to -0.4), P=0.02) at six months. The effects were sustained at 12 months (-2.9 (-5.0 to -0.7), P<0.01). No serious adverse events were reported in either group.

CONCLUSIONS

Through relatively low intensity treatment, the PROVIDE model led to a decrease in depressive and anxiety symptoms with small effects in the short and long term. Depression and anxiety disorders are prevalent and therefore the small effect might cumulatively impact on population health in this population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04316572.

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.

Discovery of Muscle-Tendon Progenitor Subpopulation in Human Myotendinous Junction at Single-Cell Resolution

The myotendinous junction (MTJ) is a complex and special anatomical area that connects muscles and tendons, and it is also the key to repairing tendons. Nevertheless, the anatomical structure and connection structure of MTJ, the cluster and distribution of cells, and which cells are involved in repairing the tissue are still unclear. Here, we analyzed the cell subtype distribution and function of human MTJ at single-cell level. We identified four main subtypes, including stem cell, muscle, tendon, and muscle-tendon progenitor cells (MTP). The MTP subpopulation, which remains the characteristics of stem cells and also expresses muscle and tendon marker genes simultaneously, may have the potential for bidirectional differentiation. We also found the muscle-tendon progenitor cells were distributed in the shape of a transparent goblet; muscle cells first connect to the MTP and then to the tendon. And after being transplanted in the MTJ injury model, MTP exhibited strong regenerative capability. Finally, we also demonstrated the importance of mTOR signaling for MTP maintenance by in vitro addition of rapamycin and in vivo validation using mTOR-ko mice. Our research conducted a comprehensive analysis of the heterogeneity of myotendinous junction, discovered a special cluster called MTP, provided new insights into the biological significance of myotendinous junction, and laid the foundation for future research on myotendinous junction regeneration and restoration.

Patient reported self-help strategies and the perceived benefits for managing sub-threshold depressive symptoms: A nested qualitative study of Australian primary care attendees

BACKGROUND

Subthreshold depression is common in primary care, but there is little information about the self-help strategies that patients use and the perceived benefits of these.

AIM

This study sought to elicit the self-help strategies that primary care attendees identified as beneficial for the self-management of subthreshold depressive symptoms and the implications for general practitioners.

METHOD

Semi-structured telephone interviews were conducted with 14 people (April-May 2017) from the Target-D randomised controlled trial (RCT). Target-D investigated whether using a patient-centred clinical prediction tool and an e-health platform to match mental health management options to prognosis was beneficial for improving depressive symptoms at 3 months compared to usual care. Interviews were thematically analysed to identify self-help strategies and their perceived benefits.

RESULTS

Four overarching domains for the self-management strategies were identified: social, cognitive, behavioural and restorative. Interviewees reported using strategies across multiple domains, which included undertaking enjoyable, immersive activities, that provided relief from automatic negative thoughts and had a perceived cognitive benefit. Differences in the perceived sense of agency were noted around the self-regulation of mood, which indicated more explicit direction to patient-identified self-help management strategies by general practitioners for some may be of benefit in routine care.

CONCLUSION

Some of the reported self-management strategies aligned with evidence-based approaches such as physical activity and mindfulness for mental health symptom management. These findings can inform low-intensity interventions within stepped care models for mental health in primary care, social prescribing models and, help to guide the management of patients by GPs for subthreshold depression.

Does providing personalized depression risk information lead to increased psychological distress and functional impairment? Results from a mixed-methods randomized controlled trial

BACKGROUND

Multivariable risk algorithms (MVRP) predicting the personal risk of depression will form an important component of personalized preventive interventions. However, it is unknown whether providing personalized depression risk will lead to unintended psychological harms. The objectives of this study were to evaluate the impact of providing personalized depression risk on non-specific psychological distress and functional impairment over 12 months.

METHODS

A mixed-methods randomized controlled trial was conducted in 358 males and 354 females who were at high risk of having a major depressive episode according to sex-specific MVRPs, and who were randomly recruited across Canada. Participants were assessed at baseline, 6 and 12 months.

RESULTS

Over 93% of participants were interested in knowing their depression risk. The intervention group had a greater reduction in K10 score over 12 months than the control group; complete-case analysis found a significant between-group difference in mean K10 change score (d = 1.17, 95% CI 0.12-2.23) at 12 months. Participants in the intervention group also reported significantly less functional impairment in the domains of home and work/school activities, than did those in the control group. A majority of the qualitative interviewees commented that personalized depression risk information does not have a negative impact on physical and mental health.

CONCLUSIONS

This study found no evidence that providing personalized depression risk information will lead to worsening psychological distress, functional impairment, and absenteeism. Provision of personalized depression risk information may have positive impacts on non-specific psychological distress and functioning.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02943876.

Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings

Perinatal depression is highly prevalent in low- and middle-income countries (LMICs) and is associated with adverse maternal and child health consequences. Task-shared psychological and psychosocial interventions for perinatal depression have demonstrated clinical and cost-effectiveness when delivered on a large scale. However, task-sharing approaches, especially in LMICs, require an effective mechanism, whereby clients who are not likely to benefit from such interventions are identified from the outset so that they can benefit from higher intensity treatments. Such a stratified approach can ensure that limited resources are utilized appropriately and effectively. The use of standardized and easy-to-implement algorithmic devices (e.g., nomograms) could help with such targeted dissemination of interventions. The present investigation posits a prognostic model and a nomogram to predict the prognosis of perinatal depression among women in rural Pakistan. The nomogram was developed to deliver stratified model of care in primary care settings by identifying those women who respond well to a non-specialist delivered intervention and those requiring specialist care. This secondary analysis utilized data from 903 pregnant women with depression who participated in a cluster randomized, controlled trial that tested the effectiveness of the Thinking Healthy Program in rural Rawalpindi, Pakistan. The participants were recruited from 40 union councils in two sub-districts of Rawalpindi and randomly assigned to intervention and enhanced usual care. Sixteen sessions of the THP intervention were delivered by trained community health workers to women with depression over pregnancy and the postnatal period. A trained assessment team used the Structured Clinical Interview for DSM-IV current major depressive episode module to diagnose major depressive disorder at baseline and post-intervention. The intervention received by the participants emerged as the most significant predictor in the prognostic model. Among clinical factors, baseline severity of core-emotional symptoms emerged as an essential predictor, followed by atypical symptoms and insomnia. Higher severity of these symptoms was associated with a poorer prognosis. Other important predictors of a favorable prognosis included support from one’s mother or mother-in-law, financial empowerment, higher socioeconomic class, and living in a joint family system. This prognostic model yielded acceptable discrimination (c-statistic = 0.75) and calibration to aid in personalized delivery of the intervention.

Longitudinal trajectory of depression symptom severity and the influence of concussion history and physical function over a 19-year period among former National Football League (NFL) players: an NFL-LONG Study

OBJECTIVE

This study investigated the longitudinal course of depressive symptom severity over 19 years in former American football players and the influence of concussion history, contact sport participation and physical function on observed trajectories.

METHODS

Former American football players completed a general health questionnaire involving demographic information, medical/psychiatric history, concussion/football history and validated measures of depression and physical function at three time points (2001, 2010 and 2019). Parallel process latent growth curve modelling tested associations between concussion history, years of football participation, and overall and change in physical function on the overall level and trajectory of depressive symptoms.

RESULTS

Among the 333 participants (mean(SD) age, 48.95 (9.37) at enrolment), there was a statistically significant, but small increase in depressive symptom severity from 2001 (48.34 (7.75)) to 2019 (49.77 (9.52)), slope=0.079 (SE=0.11), p=0.007. Those with greater concussion history endorsed greater overall depressive symptom severity, B=1.38 (SE=0.33), p<0.001. Concussion history, B<0.001 (SE=0.02), p=0.997 and years of participation, B<0.001 (SE=0.01), p=0.980, were not associated with rate of change (slope factor) over 19 years. Greater decline in physical function, B=-0.71 (SE=0.16), p<0.001, was predictive of a faster growth rate (ie, steeper increase) of depression symptom endorsement over time.

CONCLUSIONS

Concussion history, not years of participation, was associated with greater depressive symptom severity. Neither factor was predictive of changes over a 19-year period. Decline in physical function was a significant predictor of a steeper trajectory of increased depressive symptoms, independent of concussion effects. This represents one viable target for preventative intervention to mitigate long-term neuropsychiatric difficulties associated with concussion across subsequent decades of life.

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.

Clinical efficacy of a Decision Support Tool (Link-me) to guide intensity of mental health care in primary practice: a pragmatic stratified randomised controlled trial

BACKGROUND

The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression.

METHODS

In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303.

OUTCOMES

Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psychological distress than usual care plus attention control at 6 months (p=0·03), with a standardised mean difference (SMD) of -0·09 (95% CI -0·17 to -0·01). This reduction was also seen in the severe prognostic group (p=0·003), with a SMD of -0·26 (-0·43 to -0·09), but not in the minimal/mild group (p=0·73), with a SMD of 0·04 (-0·17 to 0·24). In the complier average causal effect analysis in the severe prognostic group, differences were larger among those who received some or all aspects of the intervention (SMD range -0·58 to -1·15). No serious adverse effects were recorded.

INTERPRETATION

Prognosis-based matching of interventions reduces psychological distress in patients with anxiety or depressive symptoms, particularly in those with severe symptoms, and is associated with better outcomes when patients access the recommended treatment. Optimisation of the Link-me approach and implementation into routine practice could help reduce the burden of disease associated with common mental health conditions such as anxiety and depression.

FUNDING

Australian Government Department of Health.

Matching depression management to severity prognosis in primary care: results of the Target-D randomised controlled trial

BACKGROUND

Mental health treatment rates are increasing, but the burden of disease has not reduced. Tools to support efficient resource distribution are required.

AIM

To investigate whether a person-centred e-health (Target-D) platform matching depression care to symptom severity prognosis can improve depressive symptoms relative to usual care.

DESIGN AND SETTING

Stratified individually randomised controlled trial in 14 general practices in Melbourne, Australia, from April 2016 to February 2019. In total, 1868 participants aged 18-65 years who had current depressive symptoms; internet access; no recent change to antidepressant; no current antipsychotic medication; and no current psychological therapy were randomised (1:1) via computer-generated allocation to intervention or usual care.

METHOD

The intervention was an e-health platform accessed in the GP waiting room, comprising symptom feedback, priority-setting, and prognosis-matched management options (online self-help, online guided psychological therapy, or nurse-led collaborative care). Management options were flexible, neither participants nor staff were blinded, and there were no substantive protocol deviations. The primary outcome was depressive symptom severity (9-item Patient Health Questionnaire [PHQ-9]) at 3 months.

RESULTS

In intention to treat analysis, estimated between- arm difference in mean PHQ-9 scores at 3 months was -0.88 (95% confidence interval [CI] = -1.45 to -0.31) favouring the intervention, and -0.59 at 12 months (95% CI = -1.18 to 0.01); standardised effect sizes of -0.16 (95% CI = -0.26 to -0.05) and -0.10 (95% CI = -0.21 to 0.002), respectively. No serious adverse events were reported.

CONCLUSION

Matching management to prognosis using a person-centred e-health platform improves depressive symptoms at 3 months compared to usual care and could feasibly be implemented at scale. Scope exists to enhance the uptake of management options.

Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan

Background

Approximately 12-20% of those with osteoarthritis (OA) in Australia who undergo total knee replacement (TKR) surgery do not report any clinical improvement. There is a need to develop prediction tools for use in general practice that allow early identification of patients likely to undergo TKR and those unlikely to benefit from the surgery. First-line treatment strategies can then be implemented and optimised to delay or prevent the need for TKR. The identification of potential non-responders to TKR may provide the opportunity for new treatment strategies to be developed and help ensure surgery is reserved for those most likely to benefit. This statistical analysis plan (SAP) details the statistical methodology used to develop such prediction tools.

Objective

To describe in detail the statistical methods used to develop and validate prediction models for TKR surgery in Australian patients with OA for use in general practice.

Methods

This SAP contains a brief justification for the need for prediction models for TKR surgery in general practice. A description of the data sources that will be linked and used to develop the models, and estimated sample sizes is provided. The planned methodologies for candidate predictor selection, model development, measuring model performance and internal model validation are described in detail. Intended table layouts for presentation of model results are provided.

Conclusion

Consistent with best practice guidelines, the statistical methodologies outlined in this SAP have been pre-specified prior to data pre-processing and model development.

Clinical judgement, case complexity and symptom scores as predictors of outcome in depression: an exploratory analysis

BACKGROUND

Clinical guidelines for depression in adults recommend the use of outcome measures and stepped care models in routine care. Such measures are based on symptom severity, but response to treatment is likely to also be influenced by personal and contextual factors. This observational study of a routine clinical sample sought to examine the extent to which "symptom severity measures" and "complexity measures" assess different aspects of patient experience, and how they might relate to clinical outcomes, including disengagement from treatment.

METHODS

Subjects with symptoms of depression (with or without comorbid anxiety) were recruited from people referred to an established Primary Care Mental Health Team using a stepped care model. Each participant completed three baseline symptom measures (the Personal Health Questionnaire (PHQ), Generalised Anxiety Disorder questionnaire (GAD) and Clinical Outcomes in Routine Evaluation (CORE-10)), and two assessments of "case complexity" (the Minnesota-Edinburgh Complexity Assessment Measure (MECAM) and a local complexity assessment). Clinician perception of likely completion of treatment and patient recovery was also assessed. Outcome measures were drop out and clinical improvement on the PHQ.

RESULTS

298 subjects were recruited to the study, of whom 258 had a sufficient dataset available for analysis. Data showed that the three measures of symptom severity used in this study (PHQ, GAD and CORE-10) seemed to be measuring distinct characteristics from those associated with the measures of case complexity (MECAM, previous and current problem count). Higher symptom severity scores were correlated with improved outcomes at the end of treatment, but there was no association between outcome and complexity measures. Clinicians could predict participant drop-out from care with some accuracy, but had no ability to predict outcome from treatment.

CONCLUSIONS

These results highlight the extent to which drop-out complicates recovery from depression with or without anxiety in real-world settings, and the need to consider other factors beyond symptom severity in planning care. The findings are discussed in relation to a growing body of literature investigating prognostic indicators in the context of models of collaborative care for depression.

Link-me: Protocol for a randomised controlled trial of a systematic approach to stepped mental health care in primary care

Primary care in Australia is undergoing significant reform, with a particular focus on cost-effective tailoring of mental health care to individual needs. Link-me is testing whether a patient-completed Decision Support Tool (DST), which predicts future severity of depression and anxiety symptoms and triages individuals into care accordingly, is clinically effective and cost-effective relative to usual care. The trial is set in general practices, with English-speaking patients invited to complete eligibility screening in their general practitioner's waiting room. Eligible and consenting patients will then complete the DST assessment and are randomised and stratified according to predicted symptom severity. Participants allocated to the intervention arm will receive feedback on DST responses, select treatment priorities, assess motivation to change, and receive a severity-matched treatment recommendation (information about and links to low intensity services for those with mild symptoms, or assistance from a specially trained health professional (care navigator) for those with severe symptoms). All patients allocated to the comparison arm will receive usual GP care plus attention control. Primary (psychological distress) and secondary (depression, anxiety, quality of life, days out of role) outcomes will be assessed at 6 and 12 months. Differences in outcome means between trial arms both across and within symptom severity group will be examined using intention-to-treat analyses. Within trial and modelled economic evaluations will be conducted to determine the value for money of credentials of Link-me. Findings will be reported to the Federal Government to inform how mental health services across Australia are funded and delivered in the future.

Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design

Background

Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way that will enhance their uptake in routine clinical practice remains challenging; however, mobile apps show promise in this respect. To increase the likelihood that an app-delivered clinical prediction tool can be successfully incorporated into clinical practice, it is important to involve end users in the app design process.

Objective

The aim of the study was to maximize patient engagement in an app designed to improve treatment allocation for depression.

Methods

An iterative, user-centered design process was employed. Qualitative data were collected via 2 focus groups with a community sample (n=17) and 7 semistructured interviews with people with depressive symptoms. The results of the focus groups and interviews were used by the computer engineering team to modify subsequent protoypes of the app.

Results

Iterative development resulted in 3 prototypes and a final app. The areas requiring the most substantial changes following end-user input were related to the iconography used and the way that feedback was provided. In particular, communicating risk of future depressive symptoms proved difficult; these messages were consistently misinterpreted and negatively viewed and were ultimately removed. All participants felt positively about seeing their results summarized after completion of the clinical prediction tool, but there was a need for a personalized treatment recommendation made in conjunction with a consultation with a health professional.

Conclusions

User-centered design led to valuable improvements in the content and design of an app designed to improve allocation of and engagement in depression treatment. Iterative design allowed us to develop a tool that allows users to feel hope, engage in self-reflection, and motivate them to treatment. The tool is currently being evaluated in a randomized controlled trial.