Critique: diagnosis in psychiatry
Diagnosis in medicine is viewed as an essential process in choosing appropriate treatment, predicting illness course, and providing clarity and relief to patients that their illness is legitimate and understood. The relationship between disease, causation, and diagnosis is complex, and the diagnostic process operates on several levels (Scadding, Reference Scadding1996). In many fields of medicine, the diagnostic process has evolved in complexity over the past few decades from a predominantly clinical and phenotypic process to precision medicine (Collins & Varmus, Reference Collins and Varmus2015), which places a great deal of weight on investigations aimed at staging and stratifying or personalizing treatment in a more precise manner. This evolution is focused on improving the utility of diagnosis. Mainstream psychiatric research has embraced this paradigm; however, it remains almost completely aspirational.
A review of the historiography of psychiatric diagnosis and classification reveals several alternative theoretical approaches to defining the underlying nature of psychiatric disorders. None have led to a model that is fit for purpose. The current approach based on DSM and ICD superficially resembles the approach of standard medical diagnosis, yet we are no closer to a precision medicine, in which specific mechanisms and therapeutic targets play a meaningful role. This aspiration constantly seems tantalizingly within reach but has so far proven to be a mirage. One obstacle, often minimized, derives from the reality of heterogeneity and pleiotropism (McGorry, Reference McGorry1991a, Reference McGorry, Copolov and Singhb). Syndromes in medicine, as final common pathways, are underpinned by a range of underlying pathophysiological mechanisms. Pleiotropism reflects the converse, namely that any single pathophysiological process gives rise to a range of syndromes. These often evolve through a series of stages. The substantial disconnect that remains between current diagnostic frameworks and validity and clinical utility continues to dilute their value.
At the same time, the effects of medical diagnosis are powerful, deceptively complex, and there are significant risks as well as potential benefits (Lea & Hofmann, Reference Lea and Hofmann2022). In psychiatry, the benefits have not only been more elusive, but the risks more pronounced. Furthermore, the balance between risks and benefits varies across the diagnostic spectrum. Some diagnoses tend to be rejected because of their harmful effects, while others, notably Autism and ADHD, are being embraced with a degree of contagion, in pursuit of perceived benefits within a changing socioeconomic context. In general, attitudes to diagnosis in psychiatry remain ambivalent and polarized, reflecting Cartesian tensions between the extremes of ‘mindless’ and ‘brainless’ psychiatry (Angell, Reference Angell2011). These tensions are reflected in the range of historical perspectives on the nature of psychiatric disorders that have been thoroughly rehearsed over the past century. Stein and colleagues and Kendler have recently provided erudite expositions of these perspectives (Kendler, Reference Kendler2016; Stein et al., Reference Stein, Hartford, Gagné-Julien, Glackin, Maj, Zachar and Aftab2024).
While this philosophical discourse continues, disillusionment has grown from a lack of utility of the existing diagnostic framework for treatment selection, and the overpromise and under-delivery of an excessively reductionist biological psychiatry. Current diagnostic models also represent an insufficient and relatively weak basis for allocating health care funding, and other indicators of complexity and treatment needs have become more salient (IHACPA, 2023). What is needed to transcend this impasse? Should we aim to build slowly and incrementally on the status quo (Stein et al., Reference Stein, Shoptaw, Vigo, Lund, Cuijpers, Bantjes and Maj2022), or aim for a paradigm shift? If any paradigm shift is to succeed, it must be built on sustainable scientific foundations. A global plan and change management process would need to be conducted in relation to the real-world impacts and challenges of replacing the highly embedded DSM and ICD frameworks with a different paradigm should one emerge. Such a paradigm would need to be comprehensively road-tested prior to stepwise and widespread adoption.
Diagnosis as passport
Medicine in general and psychiatry in particular remain boundary managers: border police examining and certifying transit documents in an unceasing battle over depression and anxiety, sexuality and addiction. Psychiatry remains the peculiar legatee of such problems, an obligate participant in every generation’s particular cultural negotiations—a kind of canary at the pitface of cultural strife. (Rosenberg, Reference Rosenberg2006)
The border between mental health and mental illness is a soft border. It is readily crossed often without being aware of a transition and is difficult to map and define. The border has been shrouded in stigma, is under continual pressure from cultural, financial, and legal influences (Rosenberg, Reference Rosenberg2006), and is guarded by arbitrary diagnostic criteria and unyielding triage systems. The latter combine to restrict and exclude access to the neglected, underfunded, and overwhelmed systems of mental health care. The current reality is a hard border, with harmful effects, such as the exclusion of many who would benefit from treatment, and delays in treatment at earlier stages, which increases the risks of coercive forms of care and reduces the chances of recovery. A soft border may also pose dangers, notably stigma and the risk of premature and overdiagnosis. However, most of these potential harms can be overcome through healthy cultures of care and staged and proportional treatment. Overdiagnosis due to softening boundaries has surged in some domains, notably ADHD, ASD, and common mental disorders (Kazda et al., Reference Kazda, Bell, Thomas, McGeechan, Sims and Barratt2021; Mojtabai, Reference Mojtabai2013; Rødgaard et al., Reference Rødgaard, Jensen, Vergnes, Soulières and Mottron2019). The harmful impact here is that such trends divert resources from those with genuine need and there may be a need for ‘dediagnosis’ in some areas (Lea & Hofmann, Reference Lea and Hofmann2022; The Economist, 2023).
It is possible that well-intentioned awareness programs have softened this boundary and fuelled an extension of diagnosis beyond the point where it benefits people’s health (Foulkes, Reference Foulkes2022; Lea & Hofmann, Reference Lea and Hofmann2022). However, a soft and flexible border has many advantages, notably enabling early intervention and the patient to have a say in when help is sought. At least half, or perhaps the great majority, of us will experience at least one period of mental ill-health (Caspi et al., Reference Caspi, Houts, Ambler, Danese, Elliott, Hariri and Moffitt2020; McGrath et al., Reference McGrath, Al-Hamzawi, Alonso, Altwaijri, Andrade and Bromet2023). It might be optimal to negotiate milder episodes of ill-health with a low-intensity or even a wait-and-see approach drawing upon self-help, social and peer support, or online help where available, at least for a short period. But there is no more reason than with physical ill-health, such as chest pain or a respiratory infection, to discourage or delay help-seeking at a primary care level. Early diagnosis, safe and proportional or staged intervention, depends on tolerance for such a soft border. Diagnosis can still be withheld or deferred, and people can be ‘dediagnosed’ around such a border too. In a positive sense, a reimagined diagnostic system should function as the patient’s passport for this border crossing.
Diagnosis as useful
Diagnosis is essentially classification with utility (Kendell & Jablensky, Reference Kendell and Jablensky2003). The aim is to characterize the clinical phenotype in a shorthand way that helps to distinguish those who are ill and in need of care from those who are not, and enhance treatment selection and prognosis. A soft border creates some space for this as well as guarding against potential overdiagnosis, notably diagnoses that fail to provide any benefit and may cause harm (Lea & Hofmann, Reference Lea and Hofmann2022).
Broad diagnostic categories are usually of limited utility. Therefore, some form of subclassification, to the extent that this sharpens treatment selection and prediction of outcome, has become essential to greater utility. Staging is one example of subclassification, where illness progression is defined according to subsequent stages of illness (Figure 1) (McGorry & Hickie, Reference McGorry and Hickie2019; McGorry et al., Reference McGorry, Hickie, Yung, Pantelis and Jackson2006). Another example, compatible with and an enhancement of staging, is stratification through the definition of neurobiological and psychological subtypes that offer differing drug and treatment targets (Trusheim et al., Reference Trusheim, Berndt and Douglas2007). This increasing precision means that the treatment options may be personalized in a relatively fine-grained manner (Collins & Varmus, Reference Collins and Varmus2015). However, there has always been a tension in psychiatric diagnosis between ‘lumping’ and ‘splitting’ and the basis for this has been somewhat arbitrary. The hierarchies of HiTOP (Hierarchical Taxonomy of Psychopathology; Figure 1), with the unitary ‘p’ factor at the apex, lump and split according to patterns of coherence within and between a finite number of dimensions of psychopathology. However, this form of lumping and splitting is mathematically based, according to the degree of statistical coherence and stability of symptom clusters, which may or may not map on to treatment response or prognosis. Ultimately, defining subcategories through precision or personalized medicine and therefore therapeutic utility would be the most useful form of splitting. This type of profiling based in part on biomarkers is highly compatible with, and can potentially redefine, a staging framework. It should also evolve with advances in research and treatment.
Figure 1. Alternative models and their integration. Panel A depicts the RDoC matrix of constructs (concepts representing a specified functional dimension of behavior) and seven units of analysis. Panel B depicts the HiTOP hierarchical organization of symptoms and maladaptive behaviors into progressively more general dimensions. Panel C illustrates the clinical staging model with the potential trajectory from asymptomatic state to late-stage severe and persistent mental illness with possible links to biomarkers. Panel D illustrates a conceptual integration of these models. The figure integrates time with evolution of the clinical phenotype by stage and different elements of neurobiology. A subset of individuals will progress from one stage to the next and some may remit.
Spurious precision
The advent of operational definition of putative disorders from the 1970s led to a marked improvement in reliability, at least in research settings. However, the definition of disorders and their boundaries was constrained by history and conservative opinion. DSM-IV produced a substantial expansion in the number of disorders constructed by committees, which blended opinions into consensus, albeit based on relatively sparse new evidence (Frances, Reference Frances2013; Wakefield, Reference Wakefield2016). Validity and utility continued to be conspicuously lacking. The spurious precision created has led to disillusionment and diminishing value of such nosologies in clinical practice (McGorry, Reference McGorry2010).
If we restrict ourselves to a purely psychopathological and syndromal level of analysis, the boundaries can be drawn widely or narrowly, and the ebbs and flows, the lumping and splitting, of the past 130 years will continue. Other challenges with this approach relate to the fact that the boundaries are obscure. There is a lack of ‘points of rarity’ between syndromes, nearly all operational definitions of syndromes are polythetic, and comorbidity is ubiquitous and managed inconsistently (McGrath et al., Reference McGrath, Lim, Plana-Ripoll, Holtz, Agerbo, Momen and de Jonge2020). Furthermore, currently known biosignatures map poorly on to current diagnostic categories (Abi-Dargham et al., Reference Abi-Dargham, Moeller, Ali, DeLorenzo, Domschke, Horga and Krystal2023), and almost certainly will require a transdiagnostic approach to map and align (Caspi & Moffitt, Reference Caspi and Moffitt2018; McGorry & van Os, Reference McGorry and van Os2013). To move beyond this impasse, different conceptual frameworks and new knowledge are required.
Incrementalism vs paradigm shift
The crisis consists precisely in the fact that the old is dying and the new cannot be born. (Antonio Gramsci)
Stein et al. (Reference Stein, Shoptaw, Vigo, Lund, Cuijpers, Bantjes and Maj2022) recently addressed the issue of diagnosis in an erudite exposition. However, their stance risks complacency in that the notion of a crisis in psychiatric diagnosis was denied, and the authors sought to justify a conservative, incrementalist approach within the current paradigm. The corollary of their claim that no crisis exists is that no paradigm shift is required. In arguing their case, they highlighted the limitations of some of the candidates for a paradigm shift, notably HiTOP, Research Domain Criteria (RDoC; Figure 1), and network theory. However, they failed to consider the clinical staging model and other related elements of the psychiatric ecosystem. Incrementalism is always needed within paradigms but will fail to deliver progress if the paradigm is flawed, unproductive, and impedes progress. The fact that we have not succeeded yet in formulating a new improved paradigm is not a valid defense of the current one.
While it is essential that the field evolves from the current flawed diagnostic system, it remains unknown what a new system will look like. So far no single emergent approach to psychiatric diagnosis yet satisfies all the different demands placed upon it (e.g. neurobiological, treatment, sociological, consumer-friendly). Kendler (Kendler, Reference Kendler2024) and Stein and colleagues (Stein et al., Reference Stein, Hartford, Gagné-Julien, Glackin, Maj, Zachar and Aftab2024) have considered how we might attempt to integrate multiple perspectives. The optimal way to proceed is unclear. One alternative is to identify and evolve multiple heuristic systems or strategies that address different purposes and work to integrate these. Such an ecosystem of compatible and complementary approaches, which collectively enhance utility across different domains, could create the conditions for a true paradigm shift, or at least a stepping stone beyond complacency. This is an example of ‘integrative pluralism’ (Kendler, Reference Kendler2024). Another option would be to pursue ‘adversarial collaboration’ (Bateman et al., Reference Bateman, Kahneman, Munro, Starmer and Sugden2005; Rakow, Reference Rakow, O’Donohue, Masuda and Lilienfeld2022), which is an approach to resolving scientific disputes and paradigm clashes, wherein researchers who have different positions on the issue at hand collaborate with the aim of making progress on their disputed research question. This might result in the desired goal of integration or at least integrative pluralism, or it might lead to one of the heuristic models achieving preeminence based on new scientific data and superior utility and validity.
Candidate pathways to a new paradigm for diagnosis
Research domain criteria
RDoC is a translational research framework and is not a classification or diagnostic system. RDoC explores psychopathology as dysregulation in constructs jointly defined by data for a psychological/behavioral function (e.g. cognitive control or reward reactivity) and for an implementing neural circuit/system, rather than as symptom constellations defined a priori by clinical consensus (Figure 1) (Cuthbert, Reference Cuthbert2020, Reference Cuthbert2022). Constructs are viewed as dimensions that span the full range of population functioning from normal to so-called abnormal and cut across traditional disorder categories. Constructs are nested within broader domains of function, such as cognitive systems or social processes.
RDoC constructs are regarded as exemplars of the strategic approach, with novel or revised domains/constructs appearing continually as new data dictate. Research designs may involve one or multiple constructs (e.g. threat responses and attention). Emphasis is placed upon integrative analyses of multiple measurement classes (e.g. neurobiology, behavior, self-reports), and also upon studies examining developmental trajectories and environmental influences. Computational approaches are of high priority for using model-based paradigms to examine constructs defined by brain–behavior relationships (Viviani et al., Reference Viviani, Dommes, Bosch, Steffens, Paul, Schneider and Beschoner2020); for addressing heterogeneity and comorbidity with data-driven approaches to identify new transdiagnostic clinical phenotypes that share common mechanisms (Bzdok & Meyer-Lindenberg, Reference Bzdok and Meyer-Lindenberg2018); and for identifying new treatment targets (Sanislow et al., Reference Sanislow, Ferrante, Pacheco, Rudorfer and Morris2019).
While RDoC has fostered studies that move toward precision psychiatry (Williams, Reference Williams2020), its domains and constructs do not directly guide current clinical practice given its role as a heuristic research framework rather than a clinical diagnostic manual (although key scientific bodies have begun to discuss precision-medicine indications; National Academies of Sciences, 2016). Also, the goal of understanding psychopathology in terms of brain–behavior constructs, while promising for the long run, presents conceptual, experimental, and practical challenges at present. Critics, while acknowledging its cross-diagnostic strengths, regard RDoC not as an entirely new paradigm but more a rearticulation of preexisting ideas in biological psychiatry with limited clinical utility at this stage (Stein et al., Reference Stein, Shoptaw, Vigo, Lund, Cuijpers, Bantjes and Maj2022).
Hierarchical taxonomy of psychopathology
The HiTOP consortium was formed by quantitative nosologists to integrate evidence from studies on the organization of psychopathology and develop a system based on these data (https://renaissance.stonybrookmedicine.edu/HITOP). The initial model has been published and is updated as data become available (Kotov et al., Reference Kotov, Cicero, Conway, DeYoung, Dombrovski, Eaton and Wright2022; Kotov et al., Reference Kotov, Krueger, Watson, Cicero, Conway, DeYoung and Wright2021; Kotov et al., Reference Kotov, Waszczuk, Krueger, Forbes, Watson, Clark and Zimmerman2017; Krueger et al., Reference Krueger, Kotov, Watson, Forbes, Eaton, Ruggero and Zimmermann2018). The HiTOP system aims to address three limitations of traditional nosologies. First, it defines psychopathology in terms of dimensions of psychological function that range from normal to abnormal, resolving the problem of arbitrary boundaries. Second, HiTOP identifies dimensions based on observed covariation among signs, symptoms, and maladaptive behaviors, thus reducing heterogeneity within constructs. Third, it combines primary dimensions into larger spectra, thus recognizing comorbidity and capturing this in a hierarchical organization.
The HiTOP system includes over 100 fine-grained dimensions (e.g. insomnia, suspiciousness), larger subfactors, six broad spectra, and the general factor that contains features common to all psychopathology (Figure 1) (Caspi & Moffitt, Reference Caspi and Moffitt2018; Conway et al., Reference Conway, Forbes, Forbush, Fried, Hallquist, Kotov and Eaton2019; Lahey et al., Reference Lahey, Krueger, Rathouz, Waldman and Zald2017). This system was derived from a large body of structural research, and many elements of it have been validated against genetic and neurobiological mechanisms, illness course, and treatment response (Conway et al., Reference Conway, Forbes, Forbush, Fried, Hallquist, Kotov and Eaton2019; Kotov et al., Reference Kotov, Jonas, Carpenter, Dretsch, Eaton, Forbes and Watson2020; Krueger et al., Reference Krueger, Hobbs, Conway, Dick, Dretsch and Eaton2021; Waszczuk et al., Reference Waszczuk, Eaton, Krueger, Shackman, Waldman, Zald and Kotov2020; Watson et al., Reference Watson, Levin-Aspenson, Waszczuk, Conway, Dalgleish and Dretsch2022). Compared to traditional classification approaches, HiTOP has demonstrated better reliability and predictive power, and is gaining in acceptability to clinicians (Balling et al., Reference Balling, South, Lynam and Samuel2023; Kotov et al., Reference Kotov, Cicero, Conway, DeYoung, Dombrovski, Eaton and Wright2022; Kotov et al., Reference Kotov, Jonas, Carpenter, Dretsch, Eaton, Forbes and Watson2020; Markon et al., Reference Markon, Chmielewski and Miller2011).
Recent papers have comprehensively summarized its progress and set out the agenda for evolving and enhancing HiTOP (Conway et al., Reference Conway, Kotov, Krueger and Caspi2023; Kotov et al., Reference Kotov, Cicero, Conway, DeYoung, Dombrovski, Eaton and Wright2022; Kotov et al., Reference Kotov, Krueger, Watson, Cicero, Conway, DeYoung and Wright2021). This includes interplay with neurobiological research, improving research, clinical utility, and implementation in clinical settings (Kotov et al., Reference Kotov, Cicero, Conway, DeYoung, Dombrovski, Eaton and Wright2022; Ruggero et al., Reference Ruggero, Kotov, Hopwood, First, Clark, Skodol and Zimmermann2019), and introducing a stronger longitudinal and developmental research perspective to transcend the largely cross-sectional nature of HiTOP, which has relied primarily on cross-sectional data and often in adults. HiTOP can provide useful phenotypes for longitudinal research, but the system does not yet include phenotypic features that sensitively reflect illness stage or course, and this aspect may be addressed in future research.
Developmental approaches and clinical staging
Developmental research has revealed substantial heterotypic continuity, namely that psychopathology often evolves from one form to another with a variable level of accumulating comorbidity over a person’s lifetime (Caspi et al., Reference Caspi, Houts, Ambler, Danese, Elliott, Hariri and Moffitt2020; Plana-Ripoll et al., Reference Plana-Ripoll, Pedersen, Holtz, Benros, Dalsgaard, De Jonge and McGrath2019). This shows that static, cross-sectional approaches and hierarchical exclusion rules alone will not do justice to the diversity and complexity of the clinical phenotype.
Clinical staging aims to capture this dynamic, complex natural history and link it to pragmatic models successfully developed in other medical fields. Staging acknowledges the dimensional basis of psychopathology, recognizing complexity but proposing subcategories, the boundaries of which are defined by treatment needs and/or underlying neurobiological changes. Stein et al. (Reference Stein, Shoptaw, Vigo, Lund, Cuijpers, Bantjes and Maj2022) point out that categorical and dimensional approaches are interchangeable: since not only can a dimension be converted into a category, but the reverse is equally true (Kessler, Reference Kessler2002).
Clinical staging thus attempts to define, especially at a first diagnostic encounter, nodes for where an individual is located at a given point in time along a continuum of illness (Figure 1) (McGorry & Hickie, Reference McGorry and Hickie2019; McGorry et al., Reference McGorry, Hickie, Yung, Pantelis and Jackson2006). Clinical staging adopts a quasi-dimensional approach to multiple dimensions of symptomatology, delineating stepwise stage changes imposed upon continuous symptomatology to guide treatment decision-making, prediction, and aetiological research. Clinical staging takes a transdiagnostic approach by delineating illness stages across symptom domains and true (non-polythetic) syndromes (psychosis, mood, anxiety, etc.), rather than traditional disorders, which typically capture late-stage phenotypes, such as schizophrenia, which are further weakened by the confounds of variably coherent polythetic definitions. The latter often lack construct validity. This allows for a pluripotential mindset early in the course of a disorder where fluid heterotypy and frequent comorbidity are frequently present, as well as an inherent expectation of the evolution of symptoms over time.
The utility of clinical staging is that it mandates early treatment of distress and functional impairment, guiding this according to risk–benefit principles (Shah et al., Reference Shah, Jones, van Os, McGorry and Gülöksüz2022). That means treatments used earlier should prove safer, and be more effective than later. Later stages justify more risk and adverse effects since the stakes are higher. Longitudinal studies support this notion as later stages are associated with illness progression and poor clinical and functional outcomes (Capon et al., Reference Capon, Hickie, Varidel, Prodan, Crouse, Carpenter and Iorfino2022; Iorfino et al., Reference Iorfino, Scott, Carpenter, Cross, Hermens, Killedar and Hickie2019). Staging is designed to complement syndromal diagnosis. Whether syndromal diagnosis complements staging depends on the capacity to show specificity of biological (or psychosocial) interventions, e.g. lithium for bipolar disorder. The evolution of clinical staging to clinicopathological staging, via the inclusion of pathophysiological biomarkers as in oncology, is part of the blueprint for precision psychiatry. Staging is in fact a heuristic framework that allows changes or stability in biomarkers to be studied and linked to (and potentially redefine) stage as well as syndrome (McGorry et al., Reference McGorry, Keshavan, Goldstone, Amminger, Allott, Berk and Hickie2014). However, one of the weaknesses of this more fluid approach, early in the course of illness especially, is that no definitive label can be offered, which some patients and families seek. This can reduce stigma and prevent premature closure; however, it can also be confusing and create anxiety.
Comparison and integration
The three models have some common characteristics (Table 1). All include dimensions and emphasize the links between syndromes and behavior with biological variation. Clinical staging and HiTOP include categorization alongside dimensions. The distinctions and differing emphases complement each other. For instance, the powerful and sophisticated quantitative psychopathology techniques of HiTOP capture and organize dimensions of signs and symptoms in cross-section hierarchically. Clinical staging is compatible with this but adds both clinical and the potential for, biological, utility by adding a transdiagnostic fluidity and longitudinal dimension. In addition, it offers a heuristic framework for the interpretation and study of neurobiological markers and the conduct of clinical trials. RDoC characterizes these constructs in other units of analysis, offering a full biobehavioral description, including developmental trajectories (Ip et al., Reference Ip, Jester, Sameroff and Olson2019), and prioritizes clinical research that could advance clinical utility in the future. Altogether, psychopathology can be understood as an ultimately (uni)dimensional construct (with subdimensions) that ranges from normal to dysfunctional, manifesting in biology as well as dimensions of behavior, which are all subject to change over time (Figure 1). However, full specification of constructs in three dimensions requires novel research designs and statistical methods. Furthermore, dimensionality must be channeled into new categories if clinical utility is to be achieved. Whether integration within a pluralist ecosystem is the final destination or a stepping stone to a new paradigm that may be more strongly derived from one of these candidate approaches is yet to be determined.
Table 1. Comparison of alternative models
Note: Type is either a system (specific set of features) or a framework (set of principles with more limited specification of features). Dimensions and classes are types of entities included (i.e. continuous or categorical). Units of analysis may include multiple modalities for measuring biology (e.g. genes, molecules, cells, circuits, and physiology) and behavior (e.g. self-reports, behavioral observations, and paradigms). Hierarchical organization describes relationships between constructs, such as when general constructs encompass specific constructs. Focus section lists features that are explicitly included in the model (direct) or are not yet explicated (indirect). Computational description is the statistical modeling of the construct. Normal development captures changes that occur prior to illness onset. Illness course captures changes that occur after onset. Mechanisms indicate specific causal processes. Clinical application indicates the extent to which the model can be used clinically at present.
Research methodology to deliver a new paradigm
Transdiagnostic and dimensional study designs
Future research should move away from traditional single disorder study designs, and adopt a transdiagnostic and a hybrid dimensional/categorical approach to psychopathology, which captures the full range of dysfunction at any of the axes displayed in Figure 1. New categorical divisions superimposed upon a background dimensional state selected through utility and validity would be expected to emerge. These would reflect a change in underlying biology or treatment need and efficacy. Future research will need to maximize sample sizes, e.g. through collaborative efforts, in order to capture sufficient variation across the different dimensions in Figure 1. Boundaries for sample selection will still need to be set for feasibility reasons but there may be a trade-off or ‘sweet spot’ that could be guided by staging. This approach was foreshadowed for the National Institute of Mental Health (NIMH) with the advent of RDoC but does not appear to have materialized, with research funding continuing to be allocated within the traditional diagnostic silos.
The essential value of a developmental and staging perspective
There is an emerging appreciation of a lifespan view of psychopathology. About 75% of mental illnesses have an onset before the age of 25 years and the clinical phenotype is an evolving one; people tend to experience diverse mental disorders over the life course and every disorder is associated with elevated risk for every other disorder (Caspi et al., Reference Caspi, Houts, Ambler, Danese, Elliott, Hariri and Moffitt2020; Kessler et al., Reference Kessler, Berglund, Demler, Jin, Merikangas and Walters2005; McGrath et al., Reference McGrath, Al-Hamzawi, Alonso, Altwaijri, Andrade and Bromet2023; Solmi et al., Reference Solmi, Radua, Olivola, Croce, Soardo, Salazar de Pablo and Fusar-Poli2022). Nonetheless, evidence to date for both conventional and proposed alternative approaches to classification of psychopathology is not currently developmentally informed or sensitive to illness stage (Kotov et al., Reference Kotov, Waszczuk, Krueger, Forbes, Watson, Clark and Zimmerman2017; Krueger et al., Reference Krueger, Kotov, Watson, Forbes, Eaton, Ruggero and Zimmermann2018).
The syndromal structure and biological substrates of psychopathology, as well as the interrelationships between the different axes in Figure 1, may differ at different developmental stages of onset and at different stages of disorder evolution. Future research designs should respect and capture the evolution of psychopathology over time against the context of normative human development (Cicchetti & Toth, Reference Cicchetti and Toth2009).
Novel methodologies with dense sampling over short periods of time (e.g. Ecological Momentary Assessments [EMA], actigraphy, or digital phenotyping) to complement less frequent, traditional assessments over longer time intervals should be employed. These methods, particularly EMA, could be aligned and applied within the coherent constructs defined via HiTOP (Simms et al., Reference Simms, Wright, Cicero, Kotov, Mullins-Sweatt, Sellbom and Zimmermann2021). This would enable the validation, or alternatively the revision and reengineering of the existing HiTOP constructs within a longitudinal perspective. The results obtained within the cross-sectional studies may well be challenged by such longitudinal and developmental research designs and require amendment, which might become more congruent and better integrated with clinical staging. While the stability and replicability of network structures have been debated (Borsboom et al., Reference Borsboom, Fried, Epskamp, Waldorp, van Borkulo, van der Maas and Cramer2017; Forbes et al., Reference Forbes, Wright, Markon and Krueger2017), network theory is well-placed to guide this reengineering. Dynamic systems perspectives suggest that the extent and duration of the disordered state may undermine the resilience of the healthy state (Scheffer et al., Reference Scheffer, Bockting, Borsboom, Cools, Delecroix, Hartmann and Nelson2024a, Reference Scheffer, Bockting, Borsboom, Cools, Delecroix, Hartmann and Nelsonb). A corollary is that ‘Dynamic Indicators Of Resilience’ based on the pattern of fluctuation in any of the ‘units of analysis’ would be useful to monitor the risk of future disorder and quantify real-time treatment effects (Schumacher et al., Reference Schumacher, Klein, Elsaesser, Härter, Hautzinger, Schramm and Kriston2023).
Statistical methods to model the complexity of mental illness
The complexity of mental illness is increasingly acknowledged, as is the need to model this complexity (Maj, Reference Maj2016; Nelson et al., Reference Nelson, McGorry, Wichers, Wigman and Hartmann2017). While extensive work has been carried out using traditional multivariate techniques, several novel theoretical approaches and accompanying statistical techniques have emerged or been adapted from other fields that can assist in this development (Panel 1; Table 2). These involve capturing the dynamic nature of psychopathology over time and discovering dimensions of psychopathology that cohere with other patient, phenotypic, and environmental characteristics (e.g. biology, treatment response, social adversity, and social support) that cross traditional diagnostic categories.
Panel 1. Finding thresholds along the psychopathology dimension
Dimensional models of psychopathology have to be somehow reconciled with the often binary process of clinical decision-making. Similar to cutoffs for dimensional measures of, for example, hypertension (systolic blood pressure of 140 mm Hg or higher) or obesity (body mass index of 30 kg/m2 or higher), thresholds can be imposed on dimensional classifications of mental illness for categorical decisions. A major advantage of applying thresholds to dimensional psychiatric classifications for categorical clinical decision-making, as opposed to defining psychopathology as categorical entities, is that thresholds can be flexibly adjusted along the dimension for different types of decisions and adapted when more data (evidence) become available or with new developments in available interventions or theoretical developments. Hence, the threshold to achieve benefit at the population level differs from that at the individual level. A high Number Needed to Treat (NNT) will reveal and expose that disconnect (Haslam, Reference Haslam2022). Moreover, in line with a clinical staging model, one can adopt a multi-threshold framework, with stepped models of care provided for different thresholds (e.g. based on severity, stage, biological alteration, or a combination of factors).
Thus, even though psychopathology is expressed dimensionally, this does not preclude the existence of meaningful thresholds. Our challenge lies in identifying these thresholds and predicting transitions thereof. Importantly, thresholds should be empirically defined based on external criteria such as side-effect profiles of available interventions or the likelihood of progression to more serious stages of the illness. We recommend that future research systematically vary thresholds within the same sample—and compare the different thresholds with respect to their predictive value and NNT for interventions for clinically relevant outcomes—to determine the level of symptoms (or a combination of symptoms and other units of analysis) that define a sensible threshold. Such research can provide a set of standardized definitions for thresholds and clinical decisions, which is crucial for the coordination of care among treatment providers and the development of treatment guidelines.
Statistical techniques such as normative modeling and dynamic systems modeling are promising to identify meaningful thresholds or cut points for clinical decision-making (Table 2). Normative modeling allows identifying deviations from a normative variation in association between, for example, specific symptoms and level of dysfunction. As many associations may be characterized by inverted U-shape relationships (i.e. either too little or too much is associated with a dysfunctional state) (Northoff & Tumati, Reference Northoff and Tumati2019), it is important to include nonlinear associations in normative modeling studies. Natural points of discontinuity (i.e. transitions) between normal or subthreshold states and a clinically significant disturbed state can be identified with dynamic systems modeling (Table 2). In this approach, transitions are preceded by accumulating instability within the system (Scheffer, Reference Scheffer2009; Van De Leemput et al., Reference Van De Leemput, Wichers, Cramer, Borsboom, Tuerlinckx, Kuppens and Scheffer2014). This instability can be monitored or inferred from early warning signals (e.g. increasing autocorrelation, variance, and cross-correlations) (Scheffer, Reference Scheffer2009), suggesting that sudden transitions in mental health (‘tipping points’) are preceded by early warning signals. This process has been demonstrated in depression and bipolar disorder. For example, in a case study of depression, increased autocorrelation and variance of momentary measures of feeling down as well as associations between different mental states were observed as early warning signs before a clinically and statistically significant transition in depression (Wichers & Groot, Reference Wichers and Groot2016).
Table 2. Examples of statistical techniques
A promising approach to modeling the structure of psychopathology is network analysis or modeling (Table 2). This is linked to dynamic systems theory and network theory (Table 2), building on the idea that mental disorders can be viewed as systems of causally interacting symptoms and other variables over time (Borsboom, Reference Borsboom2017). The notion of mental disorders as a complex dynamic system aligns with many other scientific disciplines, such as ecology or meteorology, which have developed mathematical models for forecasting system transitions to different states in nature and even within financial markets. These tools have been applied to forecast transitions in mental disorders, such as depression and bipolar disorder (Bayani et al., Reference Bayani, Hadaeghi, Jafari and Murray2017; Van De Leemput et al., Reference Van De Leemput, Wichers, Cramer, Borsboom, Tuerlinckx, Kuppens and Scheffer2014; Wichers & Groot, Reference Wichers and Groot2016). Preliminary evidence emerging from this work indicates that system-level early warning signals, such as critical slowing down, increasing autocorrelation, variance, and cross-correlations between symptoms (indicating increased system instability), might forecast transitions from healthy to disordered states (Helmich et al., Reference Helmich, Olthof, Oldehinkel, Wichers, Bringmann and Smit2021; Van De Leemput et al., Reference Van De Leemput, Wichers, Cramer, Borsboom, Tuerlinckx, Kuppens and Scheffer2014; Wichers & Groot, Reference Wichers and Groot2016). However, larger studies have also raised doubts about these findings (Bos et al., Reference Bos, Schreuder, George, Doornbos, Bruggeman, van der Krieke and Snippe2022; Curtiss et al., Reference Curtiss, Mischoulon, Fisher, Cusin, Fedor, Picard and Pedrelli2023; Helmich et al., Reference Helmich, Smit, Bringmann, Schreuder, Oldehinkel, Wichers and Snippe2023; Schreuder et al., Reference Schreuder, Hartman, Groen, Smit, Wichers and Wigman2023). This complex dynamic systems approach is consistent with network theory’s proposal that mental disorders can be understood as complex networks of mental states that trigger each other. In the scenario of high network connectivity, activation of a single node (mental state) may initiate a cascade of effects that resonate throughout the network. However, most research in network theory to date has been cross-sectional, and cross-sectional networks do not lend themselves to causal inference or prediction (Ryan et al., Reference Ryan, Bringmann and Schuurman2022). Therefore, network models have increasingly been applied to intensive longitudinal data, where temporal associations can be investigated (Borsboom et al., Reference Borsboom, Deserno, Rhemtulla, Epskamp, Fried, McNally and Waldorp2021; Bringmann, Reference Bringmann2021; Bringmann et al., Reference Bringmann, Albers, Bockting, Borsboom, Ceulemans, Cramer and Wichers2022).
The network approach can be easily reconciled with dimensional and transdiagnostic perspectives on mental illness. It provides more insight into the overall quasi-categorical structure of symptom interactions (e.g. which symptoms cohere and cluster stably together), as well as the role of individual symptoms (e.g. which symptom is most influential) (Borsboom, Reference Borsboom2017; Borsboom & Cramer, Reference Borsboom and Cramer2013; Cramer et al., Reference Cramer, Waldorp, Van Der Maas and Borsboom2010). To define and map new syndromal patterns, data-driven, bottom-up approaches are essential, notably the study of dynamic processes within individuals at the idiographic level, and investigating to what degree these processes can yield more stable clusters across individuals (Beltz et al., Reference Beltz, Wright, Sprague and Molenaar2016; Wright & Woods, Reference Wright and Woods2020). The bridge between idiographic and nomothetic approaches with broader utility and links to other aspects of the new diagnostic ecosystem has yet to be crossed; however, network theory and analysis offer a possible ‘common vocabulary’ across disciplines and levels of analysis and an avenue to prediction and personalized therapies (Borsboom et al., Reference Borsboom, Deserno, Rhemtulla, Epskamp, Fried, McNally and Waldorp2021; Bringmann et al., Reference Bringmann, Albers, Bockting, Borsboom, Ceulemans, Cramer and Wichers2022).
Another relatively novel statistical technique for modeling the dynamic nature of psychopathology is joint modeling. This technique jointly models the trajectories of longitudinal risk factors, including emerging symptoms, and the risk of an outcome event. Moreover, it enables dynamic prediction of the outcome (i.e. ongoing update of the risk estimate over time as further information about changes in symptoms and risk factors is obtained), which could guide personalized treatment (Illipse et al., Reference Illipse, Czene, Hall and Humphreys2023).
Supervised machine learning methods (Table 2) allow integration of multimodal data (Figure 1 and empirically identify the most relevant (combinations of) measures that predict outcomes over time. Future studies can help determine which units of analysis are most predictive of different course trajectories, clinical stages, or treatment outcomes. Large sample sizes and independent replication samples are critical for these types of studies to avoid overfitting (Vabalas et al., Reference Vabalas, Gowen, Poliakoff and Casson2019; Varoquaux, Reference Varoquaux2018) and to ensure validity and generalizability of findings.
Finally, unsupervised machine learning algorithms are demonstrating their utility to incorporate a sizable number of variables in analyses with large cohorts, which can produce results that identify precision transdiagnostic phenotypes. The integration of multiple types of data adds increasing precision for deriving new psychopathological factors. Subgroups of patients identified by these analyses have already shown the capability of better prediction of treatment outcomes as compared to traditional disorders. With sufficient sample sizes for prediction, the promise of individualized treatment selection is already being tested (Bzdok & Meyer-Lindenberg, Reference Bzdok and Meyer-Lindenberg2018). The US NIMH has begun to initiate such studies with a focus on clinical data, digital measures, and tasks to assess behavioral functions (Koutsouleris et al., Reference Koutsouleris, Dwyer, Degenhardt, Maj, Urquijo-Castro, Sanfelici and Consortium2021; NIMH, 2023).
Stakeholders
There are many stakeholders in the diagnostic process: people with mental ill-health, their families, and funders of health care, notably governments, insurance companies, and other third-party payers. It is therefore critical to consider carefully the contexts in which diagnoses are currently used, how they are interpreted and what ‘work’ they do, their benefits and risks, and to engage with those most likely to be affected by change (see Panel 2 for a case vignette with a ‘sliding doors’ structure). This needs to include the notion of ‘dediagnosing’ to limit the effects of diagnoses that do not benefit people’s health or cause harm or waste of precious health care resources (Lea & Hofmann, Reference Lea and Hofmann2022).
Panel 2. Vignette.
Background
Robin grew up with their younger sibling and single mother in supported housing in a large urban center in the United States. Robin is a member of a visible minority, speaks English and Spanish, and during childhood and adolescence the stability and security of their housing situation was fragile. Robin witnessed their mother being physically assaulted by their father from a young age and this domestic violence continued until Robin’s mother left the family home with the children when Robin was 10. Robin’s mother is a casual worker in the services industry. Her modest income covers the basics of the family’s expenses, but there is little financial security in terms of health insurance. Robin had suffered from anxiety during childhood but had functioned relatively well in primary school and had a number of close friends. However, problems began to surface in early high school. Robin became more anxious, and at times intensely distressed and overwhelmed. At age 14, Robin’s teacher had noticed that they had become ‘nervous’, quieter, and withdrawn. This began in the context of a prolonged period of being bullied. The teacher was aware of this and ensured that the school counselor provided support for Robin and that the bullying was eventually dealt with and ceased.
Scenario 1: current diagnostic/system approach
The school counselor provided a referral to a psychologist. At that first appointment with the psychologist, Robin was given a thorough evaluation and told that their symptoms were insufficiently severe and below the current threshold to meet criteria for any DSM disorder, which meant that they were not eligible to receive any age-appropriate services.
Robin was confused and somewhat frustrated by this, but returned to school for a few months and did their best to participate. However, when the feelings of anxiety, lowered mood, and irritability continued, Robin became slowly more and more discouraged, and their grades suffered. Robin eventually stopped attending school in their final year of high school, and withdrew socially from both friends and family, finding it increasingly difficult to interact with others. They spent more and more time in their bedroom playing video games at night and sleeping through the day.
A year later, Robin’s mother commented on this to a friend, an informal caregiver who was aware of community health clinics and provided a phone number to call. An appointment there was booked. Despite being reluctant to attend the initial visit, Robin went and was seen by a psychiatrist who noted that by then, Robin’s symptoms had changed and were characterized by prominent mixtures of anxiety and mood lability, yet still without reaching the threshold of a major DSM-5 diagnosis. Once again, because no specific diagnostic category seemed to fit and severity was below threshold, access to state-funded specialized care was not approved and the family lacked private medical insurance coverage.
Over the next couple of years, Robin began having distressing thoughts in the evenings that would cause difficulty in falling asleep, as well as nightmares of increasing frequency. Sometimes these were memories of the domestic violence they had witnessed as a child, although Robin did not feel able to discuss this with anyone. One of Robin’s friends suggested trying cannabis at night time, and Robin found this effective for sleep so began using this daily. Yet within weeks, first Robin and then their mother found them increasingly irritable and even irrational at times. They also found themselves becoming more suspicious and anxious in the company of others. They began to hear strange noises, and later whispering and mumbling, which sometimes became more distinct as actual verbal conversations. These ‘voices’ became frightening and critical, warning of danger and possible attacks. At first, these distressing experiences only occurred after smoking cannabis but later they became more intense and occurred at other times. Another new feature was instability of mood with fluctuations between deep depression and days of irritability, increased energy and confidence, and reduced sleep.
Robin’s distress and isolation increased and it became more difficult for their mother to communicate with them. They were less cooperative now with her attempts to get help for them. Now 19 years of age, they became more distressed and a sense of hopelessness and entrapment enveloped them and ultimately led to an overdose of their mother’s antidepressant medication. An ambulance was called and they were taken to the emergency department. They were admitted to an adult psychiatric unit where most of the other patients were in their mid-40s and had a diagnosis of schizophrenia. This was a very dispiriting and frightening experience for Robin. A diagnosis of drug-induced psychosis was assigned and antipsychotic medication was prescribed at doses which resulted in unpleasant side effects. After 5 days in hospital, the medication was abruptly ceased, based on the drug-induced diagnostic decision, and they were discharged with an appointment with the local general practitioner as the sole follow-up.
Over the next few months, Robin’s psychotic symptoms persisted and worsened, and further suicidal and at times aggressive behavior led to further hospital admissions and ultimately follow-up with the community mental health clinic. Robin was prescribed regular antipsychotic medication but received only minimal psychosocial support and their mother was largely excluded from appointments with the treating team, an approach explained on the basis of privacy and confidentiality. Robin by this stage was informed that their diagnosis had been changed to schizophrenia. They were confused and demoralized having had some exposure to what schizophrenia appeared to mean in the hospital and outpatient clinic for their future prospects.
Scenario 2: new diagnostic approach
The school counselor ensured that Robin was able to see a psychologist via the local integrated youth health service, which offered a warm, engaging welcome, a ‘listening ear’, and needs-based care for young people in the local community at an accessible stigma-free venue. No formal diagnosis was necessary to access care for their manifest distress and functional impairment, which involved peer support, psychological interventions to improve coping and reduce stress, and trauma-oriented care both for the bullying and childhood exposures. Robin was carefully followed up for several months as their mental health steadily improved and they continued at school with improved grades.
Later in high school, symptoms of anxiety and depression returned after a relationship breakup. Robin experienced a great deal of difficulty sleeping and began to be troubled by nightmares and memories of their violent childhood. They began using cannabis to manage these symptoms and help with sleep, but after a number of months, they began to develop panic attacks and feel frightened to go out because of increasing suspiciousness and fear of being harmed. They also began to hear strange sounds, which morphed into mumbling and whispering. Eventually, clear-cut voices and conversations distressed and disturbed them with hostile and critical themes, such as that Robin was in danger and was a terrible person who deserved to die. Mood instability was a new feature and days of deep depression were followed by days of increased energy and confidence, irritability, and reduced sleep.
Fortunately, Robin’s mother had noticed these changes, which had by now prevented them from attending school, and she and the school helped to arrange for the integrated youth health service to reach out to them once again. Because Robin was reluctant to venture out, the outreach worker (a clinical psychologist) came to the home to visit and carry out an assessment together with a youth peer worker. Because of their previous positive experience at the center, Robin was comfortable with this process and was able to re-engage with the team at the youth center. Due to the complex blend of short-lived and fluctuating symptoms in the context of cannabis use, the enhanced primary care clinicians at the youth center were reluctant to assign a definitive diagnosis but clearly recognized and communicated with Robin and his mother that a potentially serious condition had developed.
Robin was provided with a warm and personalized referral to the more specialized early intervention service where they were able to see a psychiatrist and gain access to a full multidisciplinary team oriented toward recovery. Investigations were carefully carried out to rule out other medical or central nervous system causes of the symptoms. The diagnostic term used to describe their presentation was first episode psychosis, as a ‘working diagnosis’, and low-dose antipsychotic medication was carefully prescribed along with anti-anxiety medication to ensure that Robin was able to sleep peacefully. Other interventions offered in sequence as they recovered were CBT and later exposure-based trauma therapy to refocus on earlier traumas. Vocational interventions helped them to return as soon as possible to education, and after recovery they were able to successfully complete high school and get a job. Robin’s drug use ceased as they felt less distressed and their other symptoms subsided, such that the drive to self-medication abated. Nevertheless, further acute relapses did occur later and the clinical picture became oriented more to one of mood disturbance with episodes of elevation of mood as well as periods of depression. Psychotic symptoms also returned but were less dominant. The treating team explained the descriptive and evolving nature of diagnosis to Robin and their mother early on, and they understood that treatments were tailored to syndromes and needs as they evolved, rather than a single traditional diagnostic label. Stigma appeared to be substantially minimized and a more hopeful stance to the future safeguarded through this approach .
Summary
The first scenario describes a pathway that is common under current diagnostic and service models. Timing of and access to care was dependent on clarity and severity of the diagnosis and quality was also affected. Robin was given a number of different conventional diagnoses to which they had various reactions, including feeling validated (anxiety and depression) but also stigmatized (psychosis). Without a formal DSM diagnosis, Robin could not really access evidence-based treatment due to the constraints of the funding of mental health care. The role of trauma and the value of psychosocial interventions were overlooked. The second scenario illustrates some of the advantages of a more flexible and agnostic approach, which recognizes the evolving nature of the clinical picture and the need for expert, holistic, and team-based mental health care during the earlier stages of illness before clarity and stability of the syndromal picture have been achieved or indeed so that such evolution can be halted in a proportion of cases.
Although the boundary between normality and mental ill-health is difficult to define, a decision to offer treatment or not must be made. Who decides? And on what basis? Is a diagnosis necessary or helpful? In addition to ‘objective’, yet arbitrary criteria, the person experiencing mental distress should have their say in when help can be expected and the diagnostic process. Clinicians and funders should acknowledge this. The assignment of a diagnosis is often equated with a ‘need for care’, though this is not necessarily the case (Lea & Hofmann, Reference Lea and Hofmann2022). Because diagnoses may influence identity and human rights, the process should be explicitly discussed and negotiated with the patient (Lea & Hofmann, Reference Lea and Hofmann2022). Need for care, as reflected in distress, risk, and/or functional impairment, typically precedes a traditional DSM or ICD diagnosis or ‘macrophenotype’. Clinicians should explain the syndromal basis and meaning of a diagnosis, and that early in the illness neither fixed or clear-cut diagnoses nor prognoses cannot necessarily be provided. Working or provisional diagnoses may be more useful and flexible, and suffice as guides to treatment options, including the decision that no diagnosis and no treatment are necessary.
Not being assigned a diagnosis may be confusing, frustrating, and stressful, but equally, a lack of diagnosis may be reassuring as long as it does not deny care. During the early weeks of treatment, a ‘working diagnosis’, complemented by a personalized formulation, which includes unique personal and contextual features, can become a focus around which to build a therapeutic alliance. If the clinical presentation is initially subtle or complex but then evolves, both clinician and patient should see that it would have been problematic to prematurely offer a hard or fixed diagnosis. This approach also reduces confusion caused by abrupt changes in diagnostic terms used, and may reduce the need for later ‘dediagnosis’ (Lea & Hofmann, Reference Lea and Hofmann2022). The evolution of clinical presentation is consistent with the known natural history of mental illness and is not ‘failure’ by the original clinical team. What these changes mean is presently unclear. Do they have a single illness with an evolving clinical picture or are they attracting additional layers of complexity or comorbid syndromes? Such changes indicate a need to rethink and recalibrate the pattern of care required.
Even with the many limitations of current diagnosis, a large subgroup of people still find being able to put a name to their condition validating. Being able to name the condition in shorthand can demystify the whole experience and help people to communicate it to others. It can also generate vital access to practical, online, and social support and to welfare benefits. The field has also built a framework of hard-won clinical knowledge and evidence-based treatments around established diagnostic concepts, meaning that careful consideration will need to be given to how to salvage and retain what we already know from earlier clinical trials within traditional diagnostic boundaries within any new transcendent model.
Challenges for a new paradigm
If after a wave of innovative research, a new paradigm were to emerge as a serious contender to supplant the current systems of psychiatric diagnosis, a number of daunting practical considerations would need to be addressed. A systematic worldwide educational process would have to be formulated and offered to existing clinical practitioners and introduced into the education of new graduates particularly in psychiatry and psychology but in fact across the whole of mental health care. There would be substantial impacts on health financing, on the legal system, which emphasizes diagnostic clarity above validity and flexibility, on welfare systems, and on systems for producing, regulating, and licensing new therapies. A comprehensive bridge and crosswalk would need to be developed between the former and the new system to ensure the vast body of existing research data was not rendered obsolete or irrelevant. This is a task that might require the power of Artificial Intelligence to address and master. The effort and expense of such a sea change could only be justified if the new paradigm conferred very substantial benefits in validity, utility, and acceptability to patients and the wider community. This raises the question of how such a judgment could be made, what kinds of research and data would be required, criteria to determine when data in support of such a new paradigm would be sufficiently persuasive that the effort and cost are justified.
Conclusion
Diagnosis that works for the patient, the family, the clinician, and the researcher needs to be as simple as possible, but no simpler. The current approach to revisions of the existing diagnostic manuals, rooted as they are in the psychiatry of traditional tertiary care and opinion-driven consensus, will not reinvent diagnosis. Nor will endless introspection on the theme of diagnosis and its discontents, or a simple recycling of quantitative nosology from a static and purely psychopathological perspective. The field needs testable new models that are parsimonious enough to work in the clinic and yet complex enough to understand the underlying complexity of mental illness as well as support more personalized and sequential treatment selection. A perspective that links the tertiary care perspective to the modern and more inclusive population-based and primary care context may be best suited to take up the challenge of modernizing the diagnosis of mental disorders from first principles. This longitudinal approach can certainly be enhanced by quantitative nosology, clinical staging, network analysis, and dynamic biomarkers as potent research and design tools. The most important benefit to be gained is greater utility and a clearer and wider pathway to the holy grail of validity. This should moderate, redefine, and condense the ever-increasing generation (and occasional extinction) of diagnostic categories, by allowing the timing, and mode and extent of progression of illness to anchor the diagnostic process, and forge a stronger link to treatment decisions sensitive to risk–benefit considerations and patient choice.
A crucial step in constructing such a novel diagnostic strategy is to operationally define the early clinical phenotypes, which require intervention in their own right, but also connote risk for later stages and more elaborated syndromes, which are likely to be multiply comorbid and more persistent, recurrent, and disabling. The early clinical phenotypes may be initially truly pluripotential, or there may be early hints or warning signs, emerging symptom relationships and biosignatures suggesting particular patterns, sequences, trajectories, and outcomes. Treatments may also be characterized by cross-diagnostic effectiveness and preventive or preemptive influence and, at the same time, have specificity for certain aspects as well. These considered conjectures require a decisively heuristic approach to the early course and treatment of mental disorders. The holy grail is a single integrated model that is sophisticated and yet clear enough to meet the needs of patients, clinicians, researchers, policymakers, and society as whole. We believe this is an achievable dream but one that will depend on a new wave of innovative data-driven research, the evolution of the concepts and strategies described, and the influence of the consumers and funders of mental health care.
Author contribution
This article consisted of six sections to which authors were assigned as lead authors (PDM, IBH, BC, RK, LS, SJW) or coauthors (all remaining authors). Apart from the first and last authors, authors are listed alphabetically with the section leads grouped first. All authors contributed to the writing of their respective sections, and reviewed, commented upon, and approved the final version of the manuscript. The views expressed in this article do not necessarily reflect those of the institutions with which individual authors are affiliated.
Funding statement
PM was supported by a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (1155508). BN was supported by a University of Melbourne Dame Kate Campbell Fellowship. SG was supported by the YOUTH-GEMs project, funded by the European Union’s Horizon Europe program under Grant Agreement Number 101057182 and by the Ophelia research project, funded by the ZonMw under Grant 636340001. JLS was supported by a clinician–scientist salary award from the Fonds de Recherche du Québec–Santé. LS was supported by an NHMRC Investigator Grant Leadership 1 (2017962). MS was supported by the European Research Council under the European Union’s Horizon 2020 research and innovative programme (681466). IH was supported by NHMRC Centre for Research Excellence grants (1171910 and 1061043). LB was supported by a Netherlands Organization for Scientific Research Veni Grant (191G.037). RK was partly supported by the following NIH grants: U19AG051426; R01AG05321; R01AG077742–02. The funding source had no role in the writing or publication of the manuscript.
Competing interests
IH has received honoraria for consultancy and educational activities from Janssen Cilag. He is a member of the Medical Research Future Fund’s (MRFF) Australian Medical Research Advisory Board; an unpaid member of the board of Psychosis Australia Trust; member of the Clinical Advisory Group for the evaluation of the Better Access to Psychiatrists, Psychologists, and General Practitioners through the Medicare Benefits Schedule (MBS) initiative; and member of Mental Health Reform Advisory Committee (Department of Health). He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd, which aims to transform mental health services through the use of innovative technologies. DO has received honoraria from Neumora Inc. and Guggenheim LLC for scientific presentations. JTWW has received payment as a member of the Mosaic 2.0 committee of Dutch Scientific Organization (NWO). BN receives research funding from the NIH, NHMRC, and MRFF and has received payment from the University of Southern California for consulting on early intervention for psychosis implementation. The other authors declare no competing interests.
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