Predicting the risk of psychosis onset: advances and prospects

Internalized stigma mediates the relation between psychosis-risk symptoms and subjective quality of life in a help-seeking sample

Subjective quality of life can be compromised in individuals with psychosis-risk symptoms, with poorer quality of life being associated with worse functioning and later transition to psychosis. Individuals who experience psychosis-related symptoms also tend to endorse more internalized (or self-) mental health stigma when compared to controls, potentially contributing to delays in seeking treatment and increased duration of untreated psychosis, as well as interfering with treatment engagement and retention in those already receiving care. Despite these findings, and the growing recognition for prevention in earlier phases of psychotic illness, few studies have examined the relation between psychosis-risk symptoms, internalized stigma, and subjective quality of life in a younger, help-seeking sample. The present study examined whether internalized stigma mediates the relation between psychosis-risk symptoms and subjective quality of life in a transdiagnostic sample of youth (M age = 17.93, SD = 2.90) at clinical high-risk for psychosis (CHR), with early psychosis, or with non-psychotic disorders (N = 72). Psychosis-risk symptom severity was assessed using the Structured Interview for Psychosis-Risk Syndromes (SIPS). Internalized stigma was assessed using the Internalized Stigma of Mental Illness Inventory (ISMI), and subjective quality of life was assessed using the Youth Quality of Life Instrument - Short Form (YQOL-SF). Internalized stigma fully mediated the relation between psychosis-risk symptoms and subjective quality of life across the full sample (p < .05, f² = 0.06). Findings suggest that internalized stigma may be an important target in efforts to improve quality of life for individuals in early stages of psychosis.

Social reward processing: A biomarker for predicting psychosis risk?

The desire to obtain social rewards (e.g. positive feedback) features prominently in our lives and relationships, and is relevant to understanding psychopathology - where behavior is often impaired. Investigating social rewards within the psychosis-spectrum offers an especially useful opportunity, given the high rates of impaired social functioning and social isolation. The goal of this study was to investigate hedonic experience associated with social reward processing as a potential biomarker for psychosis risk. This study used a task-based functional magnetic resonance imaging (fMRI) paradigm in adolescents at clinical high-risk for the development of psychosis (CHR, n = 19) and healthy unaffected peers (healthy controls - HC, n = 20). Regional activation and connectivity of the ventromedial prefrontal cortex and ventral striatum were examined in response to receiving positive social feedback relative to an ambiguous feedback condition. Expectations of impaired hedonic processes in CHR youth were generally not supported, as there were no group differences in neural response or task-based connectivity. Although interesting relationships were found linking neural reward response and connectivity with social, anticipatory, and consummatory anhedonia in the CHR group, results are difficult to interpret in light of task limitations. We discuss potential implications for future study designs that seek to investigate social reward processing as a biomarker for psychosis risk.

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

BACKGROUND

The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied.

METHODS

We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality.

RESULTS

A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects.

CONCLUSIONS

Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

Study protocol for a prospective cohort study examining the predictive potential of dynamic symptom networks for the onset and progression of psychosis: the Mapping Individual Routes of Risk and Resilience (Mirorr) study

INTRODUCTION

Our current ability to predict the course and outcome of early psychotic symptoms is limited, hampering timely treatment. To improve our understanding of the development of psychosis, a different approach to psychopathology may be productive. We propose to reconceptualise psychopathology from a network perspective, according to which symptoms act as a dynamic, interconnected system, impacting on each other over time and across diagnostic boundaries to form symptom networks. Adopting this network approach, the Mapping Individual Routes of Risk and Resilience study aims to determine whether characteristics of symptom networks can predict illness course and outcome of early psychotic symptoms.

METHODS AND ANALYSIS

The sample consists of n=100 participants aged 18-35 years, divided into four subgroups (n=4×25) with increasing levels of severity of psychopathology, representing successive stages of clinical progression. Individuals representing the initial stage have a relatively low expression of psychotic experiences (general population), whereas individuals representing the end stage are help seeking and display a psychometric expression of psychosis, putting them at ultra-high risk for transition to psychotic disorder. At baseline and 1-year follow-up, participants report their symptoms, affective states and experiences for three consecutive months in short, daily questionnaires on their smartphone, which will be used to map individual networks. Network parameters, including the strength and directionality of symptom connections and centrality indices, will be estimated and associated to individual differences in and within-individual progression through stages of clinical severity and functioning over the next 3 years.

ETHICS AND DISSEMINATION

The study has been approved by the local medical ethical committee (ABR no. NL52974.042.15). The results of the study will be published in (inter)national peer-reviewed journals, presented at research, clinical and general public conferences. The results will assist in improving and fine-tuning dynamic models of psychopathology, stimulating both clinical and scientific progress.

TRIAL REGISTRATION NUMBER

NTR6205 ; Pre-results.

Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

BACKGROUND

To enhance indicated prevention in patients with a clinical high risk (CHR) for psychosis, recent research efforts have been increasingly directed towards estimating the risk of developing psychosis on an individual level using multivariable clinical prediction models. The aim of this study was to systematically review the methodological quality and reporting of studies developing or validating such models.

METHOD

A systematic literature search was carried out (up to 14 March 2016) to find all studies that developed or validated a clinical prediction model predicting the transition to psychosis in CHR patients. Data were extracted using a comprehensive item list which was based on current methodological recommendations.

RESULTS

A total of 91 studies met the inclusion criteria. None of the retrieved studies performed a true external validation of an existing model. Only three studies (3.5%) had an event per variable ratio of at least 10, which is the recommended minimum to avoid overfitting. Internal validation was performed in only 14 studies (15%) and seven of these used biased internal validation strategies. Other frequently observed modeling approaches not recommended by methodologists included univariable screening of candidate predictors, stepwise variable selection, categorization of continuous variables, and poor handling and reporting of missing data.

CONCLUSIONS

Our systematic review revealed that poor methods and reporting are widespread in prediction of psychosis research. Since most studies relied on small sample sizes, did not perform internal or external cross-validation, and used poor model development strategies, most published models are probably overfitted and their reported predictive accuracy is likely to be overoptimistic.

Classifying risk status of non-clinical adolescents using psychometric indicators for psychosis spectrum disorders

This study is an attempt to evaluate extant psychometric indicators using latent profile analysis for classifying community-derived individuals based on a set of clinical, behavioural, and personality traits considered risk markers for psychosis spectrum disorders. The present investigation included four hundred and forty-nine high-school students between the ages of 12 and 19. We used the following to assess risk: the Prodromal Questionnaire-Brief (PQ-B), Oviedo Schizotypy Assessment Questionnaire (ESQUIZO-Q), Anticipatory and Consummatory Interpersonal Pleasure Scale-Adolescent version (ACIPS-A), and General Health Questionnaire 12 (GHQ-12). Using Latent profile analysis six latent classes (LC) were identified: participants in class 1 (LC1) displayed little or no symptoms and accounted for 38.53% of the sample; class 2 (LC2), who accounted for 28.06%, also produced low mean scores across most measures though they expressed somewhat higher levels of subjective distress; LC3, a positive schizotypy group (10.24%); LC4 (13.36%), a psychosis high-risk group; LC5, a high positive and negative schizotypy group (4.45%); and LC6, a very high distress, severe clinical high-risk group, comprised 5.34% of the sample. The current research indicates that different latent classes of early individuals at risk can be empirically defined in adolescent community samples using psychometric indicators for psychosis spectrum disorders. These findings may have implications for early detection and prevention strategies in psychosis spectrum disorders.

Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.

Applicability of the Chinese version of the 16-item Prodromal Questionnaire (CPQ-16) for identifying attenuated psychosis syndrome in a college population

AIM

The aim of this study was to examine the reliability, validity, sensitivity and specificity of the Chinese version of the 16-item Prodromal Questionnaire (CPQ-16) for identifying attenuated psychosis syndrome (APS) in a college population.

METHODS

The participants were recruited from a university. Five hundred seventy-nine students completed the CPQ-16 and the Symptom Checklist-90. One class (n = 79) was randomly selected to be retested with the CPQ-16 after 2 weeks. A randomly selected group of 49 individuals who tested positive and 50 individuals who tested negative were interviewed using the Structured Interview for Prodromal Syndromes (SIPS).

RESULTS

The internal consistency reliability was good (Cronbach's α = 0.72). The test-retest reliability was 0.88. The total score on the CPQ-16 was moderately to highly correlated with the total score on the Symptom Checklist-90 and all of the subscales (r = 0.39-0.67, P < 0.001). A cut-off CPQ-16 score of 9 was used to differentiate between those with a APS diagnosis on the SIPS versus those with no SIPS diagnoses; this cut-off value yielded 85% sensitivity, 87% specificity, a positive predictive value of 63% and a positive likelihood ratio of 6.69. The area under the ROC curve (AUC) was significant for the CPQ-16 total score (AUC = 0.93, SE = 0.026, 95% CI = 0.87-0.98, P < 0.001). Based on the proposed cut-off score, the CPQ-16 yielded a positive rate of 5.0% (29/579).

CONCLUSIONS

The CPQ-16, administered in a face-to-face interview, demonstrated high reliability and the ability to identify college students at risk for psychosis.

Typologies of positive psychotic symptoms in methamphetamine dependence

BACKGROUND AND OBJECTIVES

Understanding methamphetamine associated psychotic (MAP) symptom typologies could aid in identifying individuals at risk of progressing to schizophrenia and guide early intervention.

METHODS

Latent class analysis (LCA) of psychotic symptoms collected from 40 (n = 40) methamphetamine dependent individuals with a history of psychotic symptoms but no history of a primary psychotic disorder.

RESULTS

Three typologies were identified. In one, persecutory delusions dominated (Type 1), in another persecutory delusions were accompanied by hallucinations (Type 2), and in the third a high frequency of all the assessed hallucinatory and delusional symptoms was observed (Type 3).

DISCUSSION AND CONCLUSION

MAP is a heterogeneous syndrome with positive symptom typologies.

SCIENTIFIC SIGNIFICANCE

This study represents the first attempt at identifying typologies of MAP and highlights the potential utility of LCA in future large-scale studies.

Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome

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 high- intermediate-, 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.

Psychosis prediction and clinical utility in familial high-risk studies: selective review, synthesis, and implications for early detection and intervention

AIM

Accurate prediction of which high-risk individuals will go on to develop psychosis would assist early intervention and prevention paradigms. We sought to review investigations of prospective psychosis prediction based on markers and variables examined in longitudinal familial high-risk (FHR) studies.

METHODS

We performed literature searches in MedLine, PubMed and PsycINFO for articles assessing performance characteristics of predictive clinical tests in FHR studies of psychosis. Studies were included if they reported on one or more predictive variables in subjects at FHR for psychosis. We complemented this search strategy with references drawn from articles, reviews, book chapters and monographs.

RESULTS

Across generations of FHR projects, predictive studies have investigated behavioural, cognitive, psychometric, clinical, neuroimaging and other markers. Recent analyses have incorporated multivariate and multi-domain approaches to risk ascertainment, with generally modest results.

CONCLUSIONS

Although a broad range of risk factors has been identified, no individual marker or combination of markers can at this time enable accurate prospective prediction of emerging psychosis for individuals at FHR. We outline the complex and multi-level nature of psychotic illness, the myriad of factors influencing its development, and methodological hurdles to accurate and reliable prediction. Prospects and challenges for future generations of FHR studies are discussed in the context of early detection and intervention strategies.

Family-wise automatic classification in schizophrenia

Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component.

The relationship between psychosocial functioning and resilience and negative symptoms in individuals at ultra-high risk for psychosis

OBJECTIVE

Decline in psychosocial functioning seems to be a core feature in schizophrenia across various phases of the disorder. Little is known about the relationship between psychosocial functioning and protective factors or psychopathologies in individuals in the prodrome phase of psychosis. We aimed to investigate whether psychosocial functioning is impaired in individuals in the putative prodromal phase of schizophrenia, and, if so, to identify factors associated with compromised psychosocial functioning.

METHOD

Sixty participants at ultra-high risk (UHR) for psychosis and 47 healthy controls were recruited. All subjects were assessed in terms of psychosocial functioning using the Quality of Life Scale. A clinical assessment of psychopathology and protective factors, including resilience and coping style, was also conducted.

RESULTS

Psychosocial functioning in UHR participants was found to be compromised; this dysfunction was associated with negative symptoms, adaptive coping, and resilience. In addition, baseline resilience was lower among those in the UHR group who converted to frank psychosis than among those who did not.

CONCLUSIONS

These findings imply that treatment strategies for individuals at UHR for psychosis should be comprehensive, promoting resilience as well as targeting the reduction of positive and negative symptoms to foster social reintegration and recovery.

Effects of NRG1 and DAOA genetic variation on transition to psychosis in individuals at ultra-high risk for psychosis

Prospective studies have suggested genetic variation in the neuregulin 1 (NRG1) and D-amino-acid oxidase activator (DAOA) genes may assist in differentiating high-risk individuals who will or will not transition to psychosis. In a prospective cohort (follow-up=2.4-14.9 years) of 225 individuals at ultra-high risk (UHR) for psychosis, we assessed haplotype-tagging single-nucleotide polymorphisms (htSNPs) spanning NRG1 and DAOA for their association with transition to psychosis, using Cox regression analysis. Two NRG1 htSNPs (rs12155594 and rs4281084) predicted transition to psychosis. Carriers of the rs12155594 T/T or T/C genotype had a 2.34 (95% confidence interval (CI)=1.37-4.00) times greater risk of transition compared with C/C carriers. For every rs4281084 A-allele the risk of transition increased by 1.55 (95% CI=1.05-2.27). For every additional rs4281084-A and/or rs12155594-T allele carried the risk increased ∼1.5-fold, with 71.4% of those carrying a combination of 3 of these alleles transitioning to psychosis. None of the assessed DAOA htSNPs were associated with transition. Our findings suggest NRG1 genetic variation may improve our ability to identify UHR individuals at risk for transition to psychosis.