Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care

New diagnosis in psychiatry: beyond heuristics

BACKGROUND

Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

METHODS

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

RESULTS

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

CONCLUSIONS

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Novel youth mental health services in Australia: What differences are being reported about the clinical needs of those who attend and the outcomes achieved?

Globally, youth mental health services are evolving, with Australia's headspace services presented as a leading exemplar. headspace services were designed as enhanced primary care-based entities and were expected to collaborate with local acute, and specialist clinical and psychosocial services. The lack of large-scale health services trials necessitates understanding their impact through systematic monitoring and evaluation. This paper compares data from differing sources that describe the demographic and clinical features, and functional outcomes, of young people presenting to various headspace services. headspace National reports that care is provided largely to youth with transient distress, minimal clinical disorders, suicidality or comorbidities and limited functional impairment. Almost 50% of clients are reported to have no significant psychological symptoms or risk factors, and less than 30% to have a clinical disorder. Consequently, 36% receive only a single session of care and the median number of clinical sessions provided is three. By contrast, empirically derived estimates, utilising data from an academic centre and its affiliated centres, other independent agencies and more refined analyses of headspace national data variously portray 50-60% of youth as presenting with at least moderate clinical complexity (including at least 20% with high complexity), and with another 27% requiring active clinical intervention. Together, these data suggest approximately 75% of attendees require substantive clinical care. Clinical outcomes data from all sources indicate limited impacts on functional outcomes, with less than a third achieving significant improvement. These data support the original intent of headspace services to focus on equitable access to multidimensional and clinical assessment, evidence-based early interventions for early stages of major anxiety, mood or psychotic disorders. As demand for youth services continues to rise, there is an urgent need to reconfigure our national youth service networks to address the unmet clinical and psychosocial needs of youth presenting in the early stages of major mental disorders.

Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning

Care as usual has failed to stem the tide of mental health challenges in children and young people. Transformed models of care and prevention are required, including targeting the social determinants of mental health. Robust economic evidence is crucial to guide investment towards prioritised interventions that are effective and cost-effective to optimise health outcomes and ensure value for money. Mental healthcare and prevention exhibit the characteristics of complex dynamic systems, yet dynamic simulation modelling has to date only rarely been used to conduct economic evaluation in this area. This article proposes an integrated decision-making and planning framework for mental health that includes system dynamics modelling, cost-effectiveness analysis, and participatory model-building methods, in a circular process that is constantly reviewed and updated in a 'living model' ecosystem. We describe a case study of this approach for mental health system policy and planning that synergises the unique attributes of a system dynamics approach within the context of economic evaluation. This kind of approach can help decision makers make the most of precious, limited resources in healthcare. The application of modelling to organise and enable better responses to the youth mental health crisis offers positive benefits for individuals and their families, as well as for taxpayers.

Applying a clinical staging model in patients affected by schizophrenia spectrum disorder

Background

Clinical staging, already widespread in medicine, represents a new frontier in psychiatry. Our goal was to convert the existing theoretical staging model for schizophrenia into a feasible tool to have a timely assessment of patients' health status applicable in any psychiatric facility.

Methods

We assessed the empirical soundness of a staging model for schizophrenia spectrum disorders (SSDs), primarily centered on their current status. This model delineated six sequential stages (1, 2A, 2B, 3A, 3B, and 4) based on factors like symptom recurrence, persistence, and progression, including functional decline. Our analysis involved data from 137 individuals affected by SSDs. We examined 22 baseline variables, 23 construct-related variables, and 31 potentially modifiable clinical variables.

Results

The latter stages demonstrated significantly poorer outcomes compared to the early stages across various measures, indicating medium to large effect sizes and a dose-response pattern. This pattern confirmed the validity of the model. Notably, stages 2 and 3A exhibited pronounced differences in comparison to other stages, although variables from each validation category also distinguished between consecutive stages, particularly 3A and beyond.

Conclusion

Baseline predictors, such as familial predisposition to schizophrenia, neurodevelopmental impairment, childhood adversities, treatment delay, negative symptoms, neurological impairment, and inadequate early response to treatment, independently largely explained the staging variance. The clinical staging model, grounded in the extended course of psychosis, exhibited sound validity and feasibility, even without the use of biological or neuroimaging markers, which could greatly improve the sensitivity of the model. These findings provide insights into stage indicators and predictors of clinical stages from the onset of psychosis.

Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.

Staging 2.0: refining transdiagnostic clinical staging frameworks to enhance reliability and utility for youth mental health

Globally, 75% of depressive, bipolar, and psychotic disorders emerge by age 25 years. However, these disorders are often preceded by non-specific symptoms or attenuated clinical syndromes. Difficulties in determining optimal treatment interventions for these emerging mental disorders, and uncertainties about accounting for co-occurring psychopathology and illness trajectories, have led many youth mental health services to adopt transdiagnostic clinical staging frameworks. In this Health Policy paper, an international working group highlights ongoing challenges in applying transdiagnostic staging frameworks in clinical research and practice, and proposes refinements to the transdiagnostic model to enhance its reliability, consistent recording, and clinical utility. We introduce the concept of within-stage heterogeneity and describe the advantages of defining stage in terms of clinical psychopathology and stage modifiers. Using examples from medicine, we discuss the utility of categorising stage modifiers into factors associated with progression (ie, potential predictors of stage transition) and extension (ie, factors associated with the current presentation that add complexity to treatment selection). Lastly, we suggest how it is possible to revise the currently used transdiagnostic staging approach to incorporate these key concepts, and how the revised framework could be applied in clinical and research practice.

Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study

BACKGROUND

As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.

OBJECTIVE

The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.

METHODS

We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.

RESULTS

Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings.

CONCLUSIONS

This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.