J Neural Transm DOI 10.1007/s00702-014-1325-9

PSYCHIATRY AND PRECLINICAL PSYCHIATRIC STUDIES - ORIGINAL ARTICLE

Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome Scott Richard Clark • Klaus Oliver Schubert Bernhard Theodor Baune

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Received: 29 May 2014 / Accepted: 8 October 2014 Ó Springer-Verlag Wien 2014

Abstract The concept of indicated prevention has proliferated in psychiatry, and accumulating evidence suggests that it may indeed be possible to prevent or delay the onset of a first episode of psychosis though adequate interventions in individuals deemed at clinical high risk (CHR) for such an event. One challenge undermining these efforts is the relatively poor predictive accuracy of clinical assessments used in practice for CHR individuals, often leading to diagnostic and therapeutic uncertainty reflected in clinical guidelines promoting a ‘watch and wait’ approach to CHR patients. Using data from published studies, and employing predictive models based on the odds-ratio form of Bayes’ rule, we simulated scenarios where clinical interview, neurocognitive testing, structural magnetic resonance imaging and electrophysiology are part of the initial assessment process of a CHR individual (extended diagnostic approach). Our findings indicate that for most at-risk patients, at least three of these assessments are necessary to arrive at a clinically meaningful differentiation into highintermediate-, and low-risk groups. In particular, patients with equivocal results in the initial assessments require additional diagnostic testing to produce an accurate risk profile forming part of the comprehensive initial assessment. The findings may inform future research into reliable identification and personalized therapeutic targeting of CHR patients, to prevent transition to full-blown psychosis.

Scott Richard Clark and Klaus Oliver Schubert contributed equally to this work and should therefore both be considered first authors. S. R. Clark  K. O. Schubert  B. T. Baune (&) School of Medicine, Discipline of Psychiatry, Royal Adelaide Hospital, University of Adelaide, 4th Floor, Eleanor Harrald Building, 5005 Adelaide, SA, Australia e-mail: [email protected]

Keywords Clinical high risk  Psychosis  Early detection  Early intervention  Risk prediction  Transition  Odds ratio  Bayes’ rule

Introduction The onset of the first episode of psychosis (FEP) is preceded by a variable period of prodromal symptoms in perception, thinking and function. These symptoms, conceptualized under the term clinical high risk for psychosis (CHR), can be detected up to 10 years before (Fusar-Poli et al. 2013b; Nelson et al. 2013), and with an average duration of 5–6 years prior to the onset of FEP (Klosterkotter et al. 2001). CHR symptoms in children and adolescents are thought to reflect disturbances of neurodevelopmental processes, which culminate in a frank psychotic break in some individuals (Pantelis et al. 2005; Rapoport et al. 2012; Fryers and Brugha 2013). The ability to detect neurodevelopmental abnormalities during childhood and adolescence using assessment modalities such as structured clinical interviews, cognitive, functional, and genetic testing, structural and functional brain imaging, and electrophysiology has nurtured the hope that it may be possible to screen children and young people for risk of psychotic disorders at the earliest illness stage (Klosterkotter et al. 2001; Cannon 2005; Strobl et al. 2012; Bodatsch et al. 2013; Piras et al. 2014; Yang et al. 2010; Singh et al. 2014; Smieskova et al. 2010; Fusar-Poli et al. 2012b). Such early detection, and the provision of effective early intervention strategies (Fusar-Poli et al. 2013b; van der Gaag et al. 2013), is thought to either prevent the full psychotic incident, or to substantially improve long-term clinical and functional outcomes of psychotic illnesses (Ruhrmann et al. 2010b; Bodatsch et al. 2011). Research

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into identifying those at risk of a psychotic episode has grown rapidly over recent years, and has led to the identification of many candidate predictor variables across various modalities of investigation. However, these variables come with small-to-moderate effect, and are individually insufficient to accurately predict first episode psychosis (Thompson et al. 2013). Consequently, the field has struggled with unsatisfactory specificity of the screening tests employed, which has undermined the clinical justification for preventive therapeutic measures. Over the recent years, there has been a move towards the development of multivariate and multimodal psychosis prediction tools, to aid and direct the secondary prevention of psychotic disorders (Banati and Hickie 2009; Koike et al. 2013). In this article, we first briefly review the current evidence for clinical interventions to delay or prevent the onset of psychosis in CHR individuals. We then discuss the development of multivariate clinical prediction tools in CHR and their individual predictive properties. We go on to describe the challenges in determining ‘true’ base rates of transition to psychosis within the general population and in clinical samples, given variability in illness trajectories and spontaneous remission rates. We then ask the question whether it may be possible and worthwhile to move from multivariate to multimodal prediction of psychosis risk in those identified with CHR, and review recent examples of such an approach. Using data from published studies, we show that a relatively simple longitudinal prediction model employing the odds-ratio form of Bayes Rule (McGee 2002; Gale et al. 2013) could yield substantial benefits for the successful assessment of risk for transition from CHR to full-blown psychosis, and could help direct personalized interventions for this vulnerable group. Interventions for the indicated prevention of psychosis Accumulating evidence indicates that therapeutic interventions to prevent transition from CHR status to fullblown psychosis can be successful (Stafford et al. 2013; Ruhrmann et al. 2010b; Yung et al. 2007). Recent metaanalysis of CHR intervention trials (van der Gaag et al. 2013) found that antipsychotic treatment [number needed to treat (NNT) = 7], cognitive behaviour therapy (CBT) (NNT = 14) and supplementation with omega-3 oils (Amminger et al. 2010) (NNT = 5) are all effective in preventing transition to psychosis at 1-year post-presentation with CHR symptoms (van der Gaag et al. 2013; FusarPoli et al. 2013b). These values compare favourably with other interventions in medicine to prevent chronic illnesses, for example in pre-diabetes (metformin NNT = 14 and lifestyle changes NNT = 7 at 3 years), or prodromal

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cardiovascular disease (anticoagulant therapy NNT = 25 at 2 years) (Fusar-Poli et al. 2014a). However, there is a high rate of at-risk individuals who maintain CHR status for long periods of time before either transitioning to psychosis or remitting spontaneously. Based on a meta-analysis, over 60 % of individuals identified as CHR will not have transitioned 3 years after identification (Fusar-Poli et al. 2012a) and at least 30 % identified by basic symptoms criteria will not have transitioned by nearly 10 years (Klosterkotter et al. 2001). This has led to concerns regarding unwarranted diagnostic labelling and resultant distress, stigma and discrimination (Yang et al. 2010; Ruhrmann et al. 2012). For example, intervention with antipsychotic treatment carries the risks of side effect burden and of unknown impact on normal brain development during adolescence and early adulthood (Corcoran et al. 2010), and the use of antipsychotics is associated with the extent of progressive grey matter volume decreases and lateral ventricular volume increases observed in CHR individuals who transition to schizophrenia (Fusar-Poli et al. 2013c). Together, the concerns regarding the relatively low transition rate of the CHR state and the risk of adverse effects of early antipsychotic treatment have lead to conservative approaches of CHR management. Clinical guidelines focus on careful monitoring, and on the provision of relatively benign interventions such as family therapy and omega-3 supplementation in the first instance, and recommend antipsychotic treatment only for those at the immediate threshold of psychosis (Fusar-Poli et al. 2014a). However, antipsychotic medication still has the strongest cumulative evidence for prevention of transition (van der Gaag et al. 2013), and some CHR individuals may ‘miss out’ on effective treatments because of overly cautious guidelines. Therefore, the field has recognized the need to develop better predictive models, combining clinical findings with evidence from emerging prognostic technologies, to tailor therapies to individual needs, according to a clinically acceptable risk–benefit ratio. Strategies for multivariate clinical prediction of transition to psychosis Multivariate criteria for determination of CHR status, using a multitude of variables across several clinical assessment domains, have been derived from longitudinal studies of patients presenting to mental health services. There are two main approaches: basic symptoms (BS), which focuses on subjective impairment, and ultra-high risk (UHR), which tests for attenuated psychotic symptoms, brief limited psychotic episodes (BLIPS) and genetic and familial risk coupled with significant functional decline (Fusar-Poli et al. 2013b; Piras et al. 2014).

Personalized treatment in clinical high risk of psychosis

The Bonn Scale for Assessing Basic Symptoms (BSABS) was designed to identify those at risk of transition to schizophrenia based on subjective phenomenological descriptions of early abnormal experiences in those that eventually develop schizophrenia. The scale includes selfreported impairment in affect, attention, volition, memory, perception, thinking and speech impairment. (Klosterkotter et al. 1996; Piras et al. 2014). Klosterkotter et al. (2001) found that a model consisting of a subset of 66 of these variables predicted transition with a sensitivity of 0.98, and specificity 0.59, over 9.6 years. This scale has been abbreviated into the Schizophrenia Proneness InstrumentAdult (SPI-A). The SPI-A has been further subdivided into the overlapping cognitive perceptive (COGPER, 9 variables, sensitivity = 0.87, specificity = 0.54) and Cognitive Disturbance (COGDIS, 10 variables, sensitivity = 0.67, specificity = 0.83) scales, which include an assessment of symptom severity (Schultze-Lutter et al. 2007; Klosterkotter et al. 2011). The UHR approach was originally developed from DSM diagnostic criteria for psychotic disorders, focussing on attenuated positive symptoms and BLIPS (Yung et al. 2005). A third risk variable combining the contribution of family history of psychosis and early decline in function was subsequently added. A number of similar scales have been developed to assess these groups of variables including the basel screening instrument for psychosis (BSIP) (Riecher-Rossler et al. 2007), the Comprehensive Assessment of the At-Risk Mental State (CAARMS) (Yung et al. 2005), and the Scale of Prodromal Symptoms (SOPS) using the Structured Interview for Prodromal Syndromes (SIPS) (Miller et al. 2003; Fusar-Poli et al. 2013b). There is significant variation between scales in criteria, thresholds, transition operationalization and ultimately sensitivity and specificity (Fusar-Poli et al. 2013b; Piras et al. 2014; Schultze-Lutter et al. 2013). Meta-analysis suggests a pooled sensitivity of 0.81 (95 % CI 0.76–0.85) and specificity of 0.67 (95 % CI 0.64–0.70) across scales (Chuma and Mahadun 2011). Challenges to successful clinical risk screening and assessment of CHR individuals: variable transition rates, spontaneous or transient remission, and heterogeneous longitudinal illness trajectories Sub-threshold psychotic experiences are common. In children, they are estimated to occur in 17 % of 9–12 year olds and 7.5 % of 13–18 year olds (Kelleher et al. 2012). In adulthood, an estimated 10–25 % of the population report occasional psychotic experiences (Johns and van Os 2001). However, transition rates from such symptoms to fullblown psychosis are very low at an estimated 0.56 % yearly in the general population (Kaymaz et al. 2012). The

situation changes substantially in help-seeking individuals who present to services: in these enriched clinical samples, a wide variation of transition rates between 9 and 70 % have been reported (Fusar-Poli et al. 2013a). To complicate matters further, the help-seeking CHR group is heterogeneous in terms of comorbidity with mood, substance and personality disorders, all of which have an impact on risk of transition and prognosis (Fusar-Poli et al. 2014b; Kaymaz et al. 2012; Salokangas et al. 2012). Salokangas et al. (2012) found that 71 % of a multinational CHR cohort had another lifetime axis 1 diagnosis and 62 % a current diagnosis, predominantly of a mood disorder. Transition from CHR to a primarily affective disorder with psychotic symptoms occurs in 11 % of cases (Fusar-Poli et al. 2013a). A recent meta-analysis suggests that risk of transition increases with time from identification of prodromal symptoms, independent of the scale used, ranging between 18 % at 6 months, 22 % at 1 year, 29 % at 2 years and 36 % after 3 years (Fusar-Poli et al. 2012a). The authors found that significantly higher transition rates occurred in studies with older participants, later publication year, with non-specific interventions (treatment as usual greater transition than specific treatments such as CBT and antipsychotics), and with DSM transition criteria in contrast to specific CHR scale criteria. Authors have explained the differences in transition rates between studies by a number of factors. Firstly, there is systematic variability in measurement between studies due to the differences in CHR criteria, transition criteria and between qualitative features of specialist CHR services and the treatments used (Fusar-Poli et al. 2013b; Simon et al. 2011; Schultze-Lutter et al. 2013). Secondly, reduction in the rate of transition in more recent studies has been explained by the emerging awareness of prodromal symptoms amongst medical practitioners and mental health professionals, leading to early referral into specialized clinics and resulting in a population of patients less likely to transition within 1–2 year studies (Wiltink et al. 2013; Simon et al. 2011, 2013). These samples may contain an increased number of false negatives at the conclusion of short 1–2 year studies (see trajectories c and d in Fig. 1). Thirdly, the natural course of CHR symptoms is known to fluctuate across time, and therefore cross-sectional assessments are likely to produce false positive and negative results that depend on both day-to-day variation and overall trajectory of illness (Ruhrmann et al. 2010a). Another considerable challenge for the accurate prediction of individuals who will develop psychosis is that a number of potential longitudinal illness trajectories in CHR individuals have been identified (Ruhrmann et al. 2010a). Trajectories may be summarized as follows (Fig. 1):

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CHR symptoms

A B C D

x

y z Time (years)

Fig. 1 Trajectories of illness progression in CHR. A CHR positive at baseline then remission at y and z end of study points. B CHR positive without transition at all points. C CHR positive at baseline then remission at end point y but transition at end point z. D CHR positive at baseline and end point y, transition at end point z

recovery from CHR (group A); chronic CHR with and without resolution of symptoms (group B); initial recovery and later transition to psychosis (group C); and transition from CHR to psychosis (group D). Some recent work has been undertaken to quantify and characterize these groups, which is described below. About 24–30 % CHR patients appear to fully recover from the condition, (group A in Fig. 1), at least in followup studies of 2–2.5 years (Schlosser et al. 2012; Addington et al. 2011). Low levels of negative, mood and anxiety symptoms at baseline were found to be associated with recovery (Schlosser et al. 2012). In a 2.5-year follow-up study, about 20 % of CHR patients retained attenuated positive symptoms at lower levels of severity than those present at baseline (group B in Fig. 1) (Addington et al. 2011). Whilst full-blown psychosis does not develop in this group, they still have high rates of poor functional outcomes (Addington et al. 2011; Simon et al. 2011, 2013; Schlosser et al. 2012; de Wit et al. 2014). Carrion et al. (2013) found that 40.3 and 45.5 % of CHR non-converters had poor social and role outcomes at 3–5 years, which possibly is confounded by an elevated rate of schizotypal personality disorder in this group. Amongst chronic CHR patients, high rates of comorbid mood disorders have been identified (de Wit et al. 2014). Interestingly, a significant number of patients, for example, 5 % in Schlosser et al. (2012)’s study, remit over time but transition to psychosis at a later time (group C in Fig. 1). Transient psychotic symptoms, at baseline, are potential markers of later transition (Simon et al. 2011, 2013; Addington et al. 2011; Schlosser et al. 2012). Clinically, those who transition to psychosis (group D in

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Fig. 1) appear to progress from BS to attenuated psychotic symptoms and on to BLIPS (Yung et al. 2005). BS themselves may progress from early disturbance in affect, attention, volition, memory, progressing to more psychosis-like impairment in perception thinking and speech closer to transition. (Klosterkotter et al. 2011; Fusar-Poli et al. 2013b; Piras et al. 2014). Clinical progression has been linked to evolving cognitive deficits and brain changes on structural imaging (Koutsouleris et al. 2009; Rapoport et al. 2012). Considering these challenges to successful risk screening and assessment for the targeted prevention of psychotic disorders, it becomes clear that novel avenues of investigations are required, which go beyond the purely clinical tools currently used in practice. Multimodal modelling for prediction of transition The inclusion of data from multiple modalities of investigation in predictive modelling is consistent with emerging evidence of underlying changes in brain physiology, psychopathology and cognitive, social and general function with transition to psychosis (Smieskova et al. 2010; Bodatsch et al. 2013; Fusar-Poli et al. 2012b; Fett et al. 2011). Studies combining this data have shown that sensitivity and specificity for the prediction of transition from CHR to psychosis can be considerably improved. For instance, a model combining cognitive function (vocabulary, executive function) and negative symptoms and CHR criteria increased sensitivity and specificity to 83.3 and 79.3 %, respectively, in those identified as UHR (Riecher-Rossler et al. 2009). Machine-learning approaches to the assessment of MRI data have been used to improve the accuracy of CHR criteria. Koutsouleris et al. (2012) used a support vector machine (SVM) model of differences in white matter, grey matter and cerebrospinal fluid (CSF) to identify CHR individuals who transition to psychosis. The combined model displayed a sensitivity of 81 % and a specificity of 87.5 %. Multimodal CHR transition models have also included electrophysiological measures of brain function such as event-related potentials (ERPs). One of the most investigated ERPs in mental health research is the P300 wave form, thought to reflect the initial automatic processing of novel stimuli followed by neural activity involved in directing attention and updating memory (Bramon et al. 2004; Nieman et al. 2002; Jeon and Polich 2003). Small P300 amplitude is associated with increased risk of transition to psychosis (van der Stelt et al. 2005; Frommann et al. 2008; Bramon et al. 2008). A model combining the CAARMs CHR criteria with P300 amplitude was able to identify those who transitioned to psychosis with a

Personalized treatment in clinical high risk of psychosis

sensitivity of 83.3 % a specificity of 79.1 % (van Tricht et al. 2010). Surprisingly, only a limited number of studies have been published that seek to associate specific genetic risk markers with transition from CHR to psychosis. Two candidate genes have been investigated, Neuregulin 1 (NRG1), coding for a protein important for normal neurodevelopment, and D-amino acid oxidase activator (DAOA), which codes for an enzyme thought to activate Dserine, a NMDA receptor agonist (Bousman et al. 2013; Mossner et al. 2010). One longitudinal cohort study of 225 CHR individuals followed for up to 14 years found that two NRG1 single nucleotide polymorphisms (SNP) (rs12155594 and rs4281084) predicted transition to psychosis (Bousman et al. 2013). Those carrying a combination of any 3–4 risk alleles at these sites had a 71.4 % risk of transitioning to psychosis; however, only 7 cases met these criteria. Bousman et al. found no association of the DAOA genotype with transition. In contrast, in a shorter, 2-year design, Mossner et al. (2010) found at the SNP rs1341402, the DAOA CC genotype was associated with increased risk of transition (relative risk = 4.6) The further development of multimodal risk modelling requires a structured approach in longitudinal studies to determine detectable changes across multiple domains of investigation, such as clinical symptoms, imaging, cognition, and electrophysiology and combine them in predictive models (Koike et al. 2013). While such an approach is likely to improve the early predictive accuracy for transition, future diagnosis (affective versus nonaffective) and functional outcomes, allowing for the targeting of preventive therapeutic measures towards those patients in whom they are clinically justified, systematic assessments of neurocognition, neuroimaging, or electrophysiology are not yet part of standard clinical practice. As per the international clinical guidelines described above, contemporary care of CHR individuals rests on an initial clinical assessment with a structured tool such as BS or UHR, followed by observation over time. The trajectory of pre-psychotic phenomena during this time of observation then determines prediction of psychosis risk for the individual. For this article, we have asked the question how individual risk prediction of psychosis in CHR can be modelled using a battery of multimodal investigations offered to each CHR patient at first presentation. This approach is exemplified in two different clinical scenarios: the first aims to identify individuals at high risk at a population level and the second scenario addresses a clinical situation of help-seeking individuals presenting to psychiatric services.

An extended diagnostic approach to the assessment of risk transition to psychosis in CHR In order to explore the impact of multimodal assessments on individual transition risk prediction in patients with CHR, we conceptualized an extended diagnostic approach to this group (Fig. 2). Here, risk of transition is first assessed clinically and then further defined by assessment of predictive markers of neurocognition, imaging, electrophysiology, and blood analyses for genetic or proteomic variance. In theory, predictive accuracy for transition to psychosis should improve with every new investigation. We set out to test this assertion in a mathematical model, using data from published studies. Operationalizing the extended diagnostic approach using Bayes’ rule A mathematical technique that can simulate an extended diagnostic approach in CHR (Fig. 2) is the odds-ratio form of Bayes Rule (McGee 2002; Gale et al. 2013). Using this technique, it is possible to model the impact of each additional investigation on the estimation of risk for transition to psychosis (Clark et al. 2003, 2005; Clark 2009; Sox et al. 2013). Calculations incorporate the underlying base rate of transition, thereby correcting for differences between samples. For example the base rate of transition to psychosis in a community sample screened for occasional psychotic symptoms is much lower than the base rate in a clinical sample of help-seeking individuals. We set out to explore whether simulations of an extended diagnostic approach, operationalized with the odds ratio Bayes’ rule and using data from published studies, would differentiate high- and low-risk trajectories of transition to psychosis at the initial point of assessment. Clinically, the successful application of this approach would allow a decision to be made on prognosis and treatment at the earliest stage of assessment, once further testing is unlikely to improve the accuracy of prognosis. Such accurate early identification of those at highest, lowest, and intermediate risk of transition to psychosis would help to target personalized therapies to each risk group.

Methods A selective literature review was performed on articles drawn from Medline searches for relevant key words. Articles were selected that reported either sensitivity and specificity, or likelihood ratios (LR) for predictive investigations for CHR to psychosis. Gale et al. (2013) have

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S. R. Clark et al. Fig. 2 Extended diagnostic approach in CHR Extended diagnostic approach in CHR. Stepwise incorporation of clinical, cognitive, brain imaging, electrophysiology and blood biomarkers should increase the accuracy of prediction for risk of transition to psychosis

Table 1 Model data extracted from prediction studies Specificity

?LR

LR

2009; Koutsouleris et al. 2012; Chuma and Mahadun 2011; van Tricht et al. 2010). Where required LRs were calculated using the formulas:

Finding

Sensitivity

Ultra high riska

0.81

0.67

2.45

0.28

Cognition: MWT (verbal IQ) ? TAP Go/NoGo (false alarm)b

0.8

0.586

1.93

0.34

Positive Likelihood ratio ðLRþÞ ¼ Sensitivity=ð1  SpecificityÞ

MRIc

0.81

0.875

6.50

0.20

Negative Likelihood ratio ðLRÞ ¼ ð1  SensitivityÞ=Specificity

Electrophysiology: P300 waveformd

0.833

0.791

3.99

0.21

a

Clinical investigation: Pooled likelihood ratios from meta-analysis of Ultra High Risk criteria for transition to psychosis (Chuma and Mahadun 2011)

b

Neurocognitive investigation: Cognitive model combining verbal IQ [Mehrfachwahl–Wortschatz test (MWT)] and the Go/No Go false alarm test for attentional performance. Study transition rate = 34 % at 7 years (Riecher-Rossler et al. 2009) c Neuroimaging investigation: Koutsouleris et al. (2012)—Support Vector Machine (SMV) MRI model—main differences between transition and non-transition in (1) the dorsomedial, rostromedial, and cingulate cortex, bilaterally, with extensions to the medial orbitofrontal, precuneal, and premotor areas; (2) the dorsolateral prefrontal GM and WM; (3) the right parahippocampal and inferior temporal cortex; as well as (4) the thalamus, bilaterally; transition rate 43.2 % at 3–7 years d

Electrophysiology investigation: small P300 waveform associated with higher risk of transition (van Tricht et al. 2010); Study transition rate = 30 % at 3 years

previously critically noted that the majority of publications in this area do not report this type of data. A simulation model was constructed from data derived from three multimodal studies and a meta-analysis of clinical CHR criteria (see Table 1) (Riecher-Rossler et al.

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To simulate the assessment of CHR individuals with sequential investigations including (a) UHR criteria (Chuma and Mahadun 2011), (b) neurocognitive testing (Riecher-Rossler et al. 2009), (c) structural MRI (Koutsouleris et al. 2012) and (d) electrophysiology (van Tricht et al. 2010), we combined likelihood ratios (LR) using the oddsratio model and calculated the stepwise probability of transition to psychosis with each new assessment. Odds-ratio model: Pretest odds ¼ probability of transition= ð1  probability of transitionÞ Odds of transition ¼ Pretest odds  LR UHR  LR Cognitive function  LR MRI  LR electrophysiology ðP300 waveformÞ

Probability of transition ¼ odds of transition=ð1 þ odds of transitionÞ: We also considered the use of genetic information in this model using data from the Bousman et al. (2013) study. Supplementary tables from this publication, provided on the journal website contained adequate information to calculate a likelihood ratio for the combination of

Personalized treatment in clinical high risk of psychosis

Fig. 3 Schematic diagram of the stepwise probabilistic assessment of risk of transition to psychosis The x-axis describes investigations (clinical, cognitive, imaging, electrophysiology), which are carried out in sequence for CHR individuals at first presentation. The y-axis

describes the probability of transition from CHR to psychosis. Binary y-values at each investigation point describe the impact of a positive or negative test on the probability of transition from CHR to psychosis

3–4 risk alleles at NRG-1 SNPs. However, our calculation of the LRs associated with the use of a threshold of three risk alleles at NRG1 SNPs indicated this investigation was likely to have only a limited impact on probability calculations (LR? = 1.24; LR- = 0.75) and we therefore excluded genetic information from the model. We plotted data to produce graphs depicting each investigation on the x-axis, and the probability of transition from CHR to psychosis on the y-axis (Fig. 3). Starting with the baseline odds of transition in the population of interest, LRs (positive or negative depending on the result) were combined sequentially to determine the post-test probability. Each point in these tree-like structures represents an assessment with a binary outcome: a positive or negative test result with associated positive or negative LRs. Starting with the pre-test odds for the population of interest, each LR for a given trajectory is multiplied by the previous value to determine the next point in the series. These figures thus plot all possible prognostic outcomes. We have chosen to represent three bands of certainty of prognosis into low (\20 %), intermediate (20–80 %) and high ([80 %) risk groups. We selected these probability values heuristically, assuming that they might influence a clinician’s decision when considering treatments. We acknowledge that the values are generalizations, and that individual clinicians will vary in their decision-making thresholds depending on risk attitude, and clinical experience (Sox et al. 2013). For the first simulation (Fig. 4), we assumed a clinical youth sample representing the 7.5 % of the general population who screen positively on brief population screens for psychotic phenomena (Kelleher et al. 2012). Amongst these individuals, the pre-assessment yearly risk of transition to psychosis (base rate) is very small at 0.056 % (Kaymaz et al. 2012). Screened individuals would then

undergo an Extended Diagnostic Assessment including clinical interview (UHR), neurocognition, imaging, and electrophysiology to determine their post-test likelihood of transition (Fig. 4). The second simulation (Fig. 5) assumes a clinical sample of help-seeking individuals who present to specialist mental health services. Because these patients are selfselected, and are likely to have experienced prodromal psychotic symptoms of greater severity, we assumed a pretest probability (base rate) of transition to psychosis of 0.5, much higher than for the general population. In a third analysis (data not shown as graph), we investigated the effect of ordering assessments by the strength of their positive LRs (MRI = 6.5 [ electrophysiology = 3.99 [ clinical assessment = 2.45 [ cognitive assessment = 1.93) on the evolution of probability of transition. This was hypothesized to reflect the most efficient assessment ordering in comparison to normal workflow that usually begins with a structured clinical assessment.

Results Impact of clinical, neurocognitive, imaging, and electrophysiology investigations on clinicians’ ability to accurately predict psychosis Clinical scenario 1: general population screen In our first analysis (Fig. 4), we simulated a situation in which screening of the general population identifies a group of individuals reporting occasional, sub-clinical psychotic symptoms on brief screening questionnaires. These individuals would then undergo a set of assessments to identify those at low, intermediate, and high risk for a

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S. R. Clark et al. Fig. 4 Multimodal prediction of transition to psychosis in a population sample reporting occasional, sub-clinical, psychotic symptoms

Fig. 5 Multimodal prediction of transition to psychosis in help-seeking individuals presenting to mental health services with sub-clinical psychotic symptoms

full-blown psychotic episode. Assessments include, in sequence: (a) a clinical interview testing for UHR criteria; (b) a neurocognitive assessment; (c) an MRI scan with subsequent SVM analysis; (d) electrophysiological assessment of the P300 waveform. The initial clinical investigation (interview for UHR criteria) lowers the risk of transition for the group of individuals who are negative for UHR criteria below base rate (dotted line in Fig. 4). For those positive for UHR criteria (straight line in Fig. 4), the predicted risk for transition to psychosis remains strikingly low at \20 %. The second investigation (neurocognitive assessment) achieves relative predictive certainty for the group of patients who were negative for UHR criteria and had

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unremarkable cognitive tests. Their low-risk status is unlikely to change, even if MRI and electrophysiology yielded positive results. In contrast, for all other groups, additional investigations are necessary to bring clarity about risk status. For example, being positive for UHR criteria and positive for neurocognitive deficits still only predicts a 25 % likelihood of transitioning to psychosis. Similarly, being positive for UHR criteria but performing normally in cognitive testing results in a risk for psychosis close to the initial base rate. The third investigation, MRI with SVM analysis, begins to more clearly differentiate low-, medium-, and high-risk groups. Those positive for all tests (UHR criteria, cognitive deficits, and MRI abnormalities) are now at nearly 70 % risk of transitioning to

Personalized treatment in clinical high risk of psychosis

psychosis. Similarly, individuals positive for one of the tests but negative for the other two are likely to remain in the low-risk group, regardless of the outcome of a fourth test. However, for individuals who were positive for two tests and negative for one, the risk status remains uncertain, ranging between 0.2 and 0.3, until a fourth investigation is added. For this low-intermediate group, a fourth investigation, electrophysiological analysis of the P300 waveform, appears particularly helpful for determining risk status. A negative P300 test lowers risk for psychosis below 0.2 (low-risk status), whereas a positive P300 will raise risk to the high-intermediate level (0.5–0.6). Interestingly, for the group positive for all three previous tests, a negative P300 reduces risk from 0.68 (high-intermediate) to 0.3 (lowintermediate). In contrast, being positive on all 4 tests indicates a risk of transition to psychosis of nearly 90 %. In summary, our simulation suggests that for members of the general population reporting occasional psychotic symptoms on brief screening questionnaires, at least two follow-up assessments (clinical interview for UHR criteria ? cognitive assessment) are necessary to confidently determine low risk for transition to psychosis. To confidently identify individuals at high, high-intermediate and low-intermediate risk for transition, at least four multimodal assessments are required. Clinical scenario 2: help-seeking individuals presenting to psychiatric services In our second analysis (Fig. 5), we simulated a situation likely to be encountered in mental health services, where help-seeking individuals with prodromal psychotic symptoms present because of perceived suffering or functional decline associated with their mental state. As outlined before, this self-selected group is likely at a much higher baseline risk of transition to psychosis than a sample drawn by screening from the general population. For the purpose of our analysis, we assumed a pre-test probability of conversion of 50 % (=0.5). The higher pre-test probability has a number of interesting effects on the development of risk prediction with sequential investigations. In the help-seeking group, it takes at least three consistently positive or negative results of sequential tests (UHR criteria, cognitive impairment, and MRI) to determine with relative confidence individuals who belong to the high- and low-risk groups for transition to psychosis. Only for these individuals, risk does not substantially change if the fourth investigation (P300) goes against the trend, and assessment could be concluded after three investigations. For all other individuals, who have one result going against the trend in the first three tests, a fourth test (P300) can still significantly change risk status,

transferring some individuals from the low-risk to the intermediate-risk group, the intermediate-risk to low- or high-risk groups, or the high-risk to the intermediate-risk group. In summary, an increasing base rate of transition to psychosis in a clinical sample makes accurate prediction of risk status more complex, requiring at least three modalities of investigation to arrive at clinically meaningful conclusions. Adding an additional investigation appears particularly important for the group of patients whose first three assessments do not yield consistent results. Impact of a change in sequence of assessments for CHR individuals on the requirement for sequential tests A further simulation was carried out with assessments ordered by the strength of their positive likelihood ratios (MRI [ electrophysiology [ clinical assessment [ cognitive assessment) (data not shown as graph). For the clinical scenario 1 (population screening), this ordering had little impact on the evolution of probability, and all four assessments were still required to be positive to reach high risk of transition. However, for the clinical scenario 2 (help-seeking individuals presenting to psychiatric services), an initial positive MRI produced a high probability of 86 % and a negative MRI a low probability of 17 %. If both MRI and electrophysiology were positive there was a 96 % probability of transition and if there were both negative the probability dropped to 4 %. Subsequent assessments had limited impact on these extreme groups, but were of value when results for MRI and electrophysiology were mixed and the post-test probability following these tests was intermediate. In summary, changing the sequence of assessments for our models has little impact on our overall findings for sample 1 (screened from the general populations). In sample 2 (help-seeking individuals attending mental health services), performing MRI and electrophysiology before clinical and cognitive assessments produces strong predictive accuracy for some individuals, but in those with inconsistent tests at least four assessments are still required to produce clinically meaningful prediction of psychosis risk.

Discussion Using data from published studies of predictive investigations for transition from CHR to psychosis (clinical interview for UHR criteria, neurocognitive testing, structural MRI with SVM analysis, and electrophysiological testing of the P300 wave form), we produced simulations of assessment scenarios where all four tests are carried out in

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sequence at first presentation. Applying a modelling approach based on the odds-ratio form of Bayes’ rule, our results show that several modalities of investigation are necessary to arrive at clinically meaningful risk predictions for conversion to psychosis at time of first presentation. This is particularly true for a group of patients whose initial test results (e.g. clinical and cognitive) are equivocal. For such individuals, at least four tests are required to determine their actual risk profile. Our first model assumed initial detection of pre-psychotic experiences on a population level through simple screening questionnaires (Kelleher et al. 2012). Because such experiences are common in populations of school children, the underlying base rate of transition to psychosis in this group is extremely low (Kaymaz et al. 2012). Our model shows that only comprehensive assessments with at least two assessment modalities at follow-up (clinical and cognitive) will confidently separate initially detected individuals who are at low risk of transitioning to psychosis. In contrast, to confidently detect individuals at high and intermediate risk of transition, at least four sequential tests are required (Fig. 4). Omitting just one of these modalities in the assessment process results in unsatisfactory estimates of transition risk (Fig. 4). Our second model assumed a substantially higher base rate of transition in a help-seeking population, presenting to psychiatric services for assessment. Help-seeking individuals are a self-selected group at higher risk of transition, and are more likely to have experienced some kind of functional or behavioural decline prior to presentation (Addington et al. 2011). In this patient sample, the effect of additional investigations is more complex (Fig. 5). At first sight, clinical and cognitive assessments alone seem sufficient to separate low-, intermediate-, and high-risk groups (Fig. 5). However, the addition of MRI SVM assessment is sufficient to substantially alter the risk evaluation within these groups. Only when at least three out of four tests follow the same trend, can the clinician begin to make accurate statements about the actual risk of transition to psychosis. Again, for those with unequivocal results in the initial three tests, a fourth test is inevitable if accurate risk prediction is to be achieved. In our third model we explored the impact of the sequence of assessments by ordering tests by the strength of their positive likelihood ratios, thereby optimizing the ability of the sequence to rule-in risk of transition. Given the low baseline rate of transition in the population sample simulation, this approach only impacted on the simulation of psychosis prediction in a help-seeking sample. In this case performing MRI and electrophysiology alone before clinical and cognitive assessments separated a proportion of patients into high- and low-risk groups. Those with inconsistent tests still required at least four assessments to

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produce clinically meaningful prediction of psychosis risk. This result suggests that the optimal investigation strategy will vary across the CHR population. For simplicity in this analysis, we chose an example that ordered investigations by the strength of their positive LRs rather than explore all 24 possible permutations of tests. In order to establish the most efficient order of testing, a formal decision analysis is required. Such an analysis investigates the utility of an investigation in the clinical context, considering the information available at the time of decision-making, the effectiveness of treatment options and any adverse effects of investigation or treatment (Yokota and Thompson 2004). Such calculations require more detailed data missing from current publications, including the number of CHR individuals that fall into each diagnostic trajectory, the probability of transition and the impact of interventions on outcomes within each diagnostic trajectory. The optimal test sequence for any given individual may vary with the expected value of information gained by the remaining investigation options available. Ultimately, there is a tradeoff between individual needs and overall service efficiency when developing assessment and treatment guidelines that is impacted by characteristics of the local patient population and the health service (Owens and Nease 1997). Our findings have several important clinical implications. On the population level, successful screening to prevent the onset of serious mental disorders is only likely to be effective if initial, low-level screening questionnaires for psychotic symptoms are followed up by a comprehensive assessment of those reporting symptoms. In order to reliably identify individuals at very high risk of transition to psychosis, such comprehensive assessments need to include multiple modalities of investigation, such as structured clinical interviews, neurocognitive testing, brain imaging with advanced computational analysis, and electrophysiology. Similarly, clinical services assessing helpseeking individuals for psychosis risk can substantially improve their predictive accuracy if multimodal investigations are offered in addition to standard clinical UHR interviews. Such improved accuracy would be particularly helpful to guide treatment decisions for the patient group considered at intermediate risk following initial clinical assessments. The cost implications of an extended diagnostic approach in CHR, as is implied by our models, require investigation. Previous economic analyses of early detection services in psychosis have shown that, while initial investment for these services is higher than ‘usual care’, significant savings to society over the long term are possible due to reduced numbers of psychiatric inpatient admissions, reduced length of admissions, and increased time in work (Valmaggia et al. 2009). Personal and societal benefits increase further when suicide (Melle et al. 2006),

Personalized treatment in clinical high risk of psychosis

or lost productivity (Mihalopoulos et al. 2009) are included in economic analyses. The ability of early psychosis services to target comprehensive therapies to those at highest risk of transition to psychosis is likely to further increase their cost-effectiveness. Reliable stratification into low-, medium-, and high-risk groups for transition to psychosis at first presentation is likely to have implications for clinical guidelines in CHR. Currently, international guidelines recommend a ‘watch and wait’-approach to CHR patients, involving serial clinical observation, and recommending pharmacological treatment, for example with antipsychotic medications, only when psychotic symptoms are relatively advanced (Fusar-Poli et al. 2014a). These recommendations are in contrast to emerging evidence of the effectiveness of active treatments in the prevention of transition to psychosis (van der Gaag et al. 2013). Reliable identification of high- and low-risk CHR groups would assist in guiding the personalized recommendation of preventive treatments, taking into consideration risk/benefit concerns for each individual. For those at the highest end of the risk spectrum, early commencement of preventive treatments may win time in combatting the pathophysiological processes associated with psychosis onset (de Koning et al. 2009; Pasternak et al. 2012). In contrast, for those in the low-risk group, reliable risk estimation will prevent the potentially stigmatizing consequences of a psychiatric diagnosis and exposure to potentially harmful effects of pharmacological and other interventions (McGlashan et al. 2006). Clinical assessments alone for determination of transition risk to psychosis are problematic due to the fluctuating course of symptoms in high-risk individuals (Ruhrmann et al. 2010a). The inclusion of neurocognition, electrophysiology, and structural MRI in the initial assessment process may ameliorate this problem, because changes detectable by these modalities have been shown to be in part genetically determined and relatively stable over time, even in the developing adolescent brain (Rapoport et al. 2012; Fusar-Poli et al. 2012b; Catts et al. 2013). Risk modelling according to the odds-ratio form of Bayes’ rule may provide a practical yet simple method to integrate research across various areas of investigation into a tool that may have utility in everyday clinical practice. Additional modes of investigation are easily integrated into such models, and are likely to further improve predictive accuracy. For example, serum biomarkers of transition (Stojanovic et al. 2014), underlying genetic signatures (Bousman et al. 2013), or functional brain imaging such as positron emission tomography (PET) (Howes et al. 2011; Banati and Hickie 2009), could be included in the future. The method has the advantage of being easily adapted and updated as new evidence emerges.

In recent years multivariate prognostic models have proliferated through the medical literature (Steyerberg et al. 2013), and other fields of medicine such as oncology have successfully adopted and translated such models to provide individualized risk assessment and treatment (Krishnan et al. 2013). We argue that psychiatry should consider a similar approach across the multiple modes of available data, to improve predictive accuracy for illness trajectory and outcomes. Limitations of this study We have based our extended Bayesian risk models on published data from a small number of studies, which have drawn their results from diverse study populations. Integrating heterogeneous data into risk models may lead to distortion of results because of heterogeneous sampling effects. This problem could be overcome if researchers routinely published data in a form that allows calculation of likelihood ratios, as has been pointed out before (Gale et al. 2013). Then, meta-analysis of large numbers of studies would provide more realistic estimation of risk. Another potential difficulty with drawing together data from different diagnostic modalities is that the interdependence of individual tests is unknown. For example, the results of MRI SVM analysis might be highly correlated with P300 abnormalities in the CHR group. Such interdependence would change the risk estimation for each test in our model. For clinical utility, it would also be important to learn about the real-world number of patients for each binary risk pathway in our model. If it became clear that only a very small number of patients had equivocal test results across multiple domains, the introduction of additional tests for these individuals may not be economically tenable. Ultimately, only clinical trials testing multimodal assessments in the same population will provide the highest level of evidence. As with all novel clinical approaches, the utility and safety of specific modelling algorithms in practice will require rigorous testing in well-designed randomized placebo-control trials, comparing the outcomes of standard treatment decisions versus decisions informed by the prognostic model. We did not include genetic information in our model due to the limited data available and the small impact that this had on overall probability of transition. Given the suspected polygenic nature of severe mental illness this result is not surprising (Wray et al. 2014). A well-designed and powered genome-wide association study is required to identify risk SNPs for transition from CHR to psychosis that could be combined in probabilistic polygenetic risk calculations and added to our model. Another limitation of our models is that the length of follow-up in the UHR studies used for risk analysis was

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relatively short, ranging between 6 months and 7 years. It is possible that longer follow-up periods would have provided different values of risk prediction for individual assessments. The longest follow-up studies in CHR individuals, lasting 10 years or more, indeed indicate that transition rates increase over time (Fusar-Poli et al. 2012a). It is important to note that no modelling technique is able to predict the impact of important life events such as losses, physical illness, or new relationships on an individual’s risk for transition from high risk to psychosis. While multimodal assessment of the risk for illness progression may serve as a tool of guidance, it cannot replace the empathic therapeutic relationship. Further, our analysis has only considered a simplified binary outcome of CHR: transition to psychosis. Follow-up studies have shown that those CHR patients who do not develop psychosis nevertheless are at elevated risk for developing other psychiatric illnesses such as mood and anxiety disorders (Fusar-Poli et al. 2014b). Non-converters also suffer long-term functional disadvantages compared to the general population (Addington et al. 2011), and are at increased risk of rare but extreme adverse life events such as suicide (Kelleher et al. 2013). It is therefore important that illness trajectories in UHR are systematically described and explored, and that preventive measures for nonpsychosis outcomes of UHR are developed and delivered to this vulnerable group.

Conclusions The significant variability in the structure and accuracy of clinical prediction models for transition from clinical high risk (CHR) to first episode psychosis (FEP) have led to uncertainty about the best clinical approach to CHR patients. At the same time, evidence is accumulating that indicated prevention of psychotic disorders is feasible. Simulating a comprehensive assessment at first presentation which incorporated risk data from clinical, neurocognitive, neuroanatomical, and electrophysiological investigations, we show that only the systematic and simultaneous use of multimodal patient evaluations is likely to produce satisfactory predictive accuracy. Further development of this approach could enable the implementation of personalized treatments to improve outcomes in CHR.

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