Models Predicting Psychosis in Patients With High Clinical Risk: A Systematic Review.
ABSTRACT: Objective:The present study reviews predictive models used to improve prediction of psychosis onset in individuals at clinical high risk for psychosis (CHR), using clinical, biological, neurocognitive, environmental, and combinations of predictors. Methods:A systematic literature search on PubMed was carried out (from 1998 through 2019) to find all studies that developed or validated a model predicting the transition to psychosis in CHR subjects. Results:We found 1,406 records. Thirty-eight of them met the inclusion criteria; 11 studies using clinical predictive models, seven studies using biological models, five studies using neurocognitive models, five studies using environmental models, and 18 studies using combinations of predictive models across different domains. While the highest positive predictive value (PPV) in clinical, biological, neurocognitive, and combined predictive models were relatively high (all above 83), the highest PPV across environmental predictive models was modest (63%). Moreover, none of the combined models showed a superiority when compared with more parsimonious models (using only neurocognitive, clinical, biological, or environmental factors). Conclusions:The use of predictive models may allow high prognostic accuracy for psychosis prediction in CHR individuals. However, only ten studies had performed an internal validation of their models. Among the models with the highest PPVs, only the biological and neurocognitive but not the combined models underwent validation. Further validation of predicted models is needed to ensure external validity.
Project description:Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%-82% (NNT = 3) with 2 positive tests, 12%-21% (NNT = 11-18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.
Project description:Importance:Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. Objective:To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. Design, Setting, and Participants:This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. Main Outcomes and Measures:In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. Results:The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). Conclusions and Relevance:In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.
Project description:Individuals at clinical high risk for psychosis (CHR) exhibit neurocognitive deficits in multiple domains. The aim of this study is to investigate whether several components of neurocognition are predictive of conversion to psychosis.Fifty-two CHR individuals were assessed with the Structured Interview for Psychosis Risk Syndromes and completed a battery of neurocognitive tests at baseline including measures of executive functioning, attention, working memory, processing speed and reaction time. Neurocognitive functioning at baseline was scored based on an external normative control group. Most subjects were followed for 2.5 years to determine conversion status.Significant differences in neurocognitive functioning between CHR individuals and the control group were present in all domains. Twenty-six per cent of the participants converted to psychosis within 9.8 (standard deviation?=?8.0) months on average (median 9 months), but there were no significant differences in neurocognition converters and non-converters.Individuals at CHR have deficits in neurocognitive functioning, but such deficits do not appear to be related to conversion risk.
Project description:BACKGROUND:Findings from neurodevelopmental studies indicate that adolescents with psychosis spectrum disorders have delayed neurocognitive performance relative to the maturational state of their healthy peers. Using machine learning, we generated a model of neurocognitive age in healthy adults and investigated whether individuals in clinical high risk (CHR) for psychosis showed systematic neurocognitive age deviations that were accompanied by specific structural brain alterations. METHODS:First, a Support Vector Regression-based age prediction model was trained and cross-validated on the neurocognitive data of 36 healthy controls (HC). This produced Cognitive Age Gap Estimates (CogAGE) that measured each participant's deviation from the normal cognitive maturation as the difference between estimated neurocognitive and chronological age. Second, we employed voxel-based morphometry to explore the neuroanatomical gray and white matter correlates of CogAGE in HC, in CHR individuals with early (CHR-E) and late (CHR-L) high risk states. RESULTS:The age prediction model estimated age in HC subjects with a mean absolute error of ±2.2?years (SD?=?3.3; R2?=?0.33, P?<?.001). Mean (SD) CogAGE measured +4.3 (8.1) years in CHR individuals compared to HC (-0.1 (5.5) years, P?=?.006). CHR-L individuals differed significantly from HC subjects while this was not the case for the CHR-E group. CogAGE was associated with a distributed bilateral pattern of increased GM volume in the temporal and frontal areas and diffuse pattern of WM reductions. CONCLUSION:Although the generalizability of our findings might be limited due to the relatively small number of participants, CHR individuals exhibit a disturbed neurocognitive development as compared to healthy peers, which may be independent of conversion to psychosis and paralleled by an altered structural maturation process.
Project description:Although cognitive deficits in patients with schizophrenia are rooted early in development, the impact of psychosis on the course of cognitive functioning remains unclear. In this study a nested case-control design was used to examine the relationship between emerging psychosis and the course of cognition in individuals ascertained as clinical high-risk (CHR) who developed psychosis during the study (CHR + T).Fifteen CHR + T subjects were administered a neurocognitive battery at baseline and post-psychosis onset (8.04 months, s.d. = 10.26). CHR + T subjects were matched on a case-by-case basis on age, gender, and time to retest with a group of healthy comparison subjects (CNTL, n = 15) and two groups of CHR subjects that did not transition: (1) subjects matched on medication treatment (i.e. antipsychotics and antidepressants) at both baseline and retesting (Meds-matched CHR + NT, n = 15); (2) subjects unmedicated at both assessments (Meds-free CHR + NT, n = 15).At baseline, CHR + T subjects showed large global neurocognitive and intellectual impairments, along with specific impairments in processing speed, verbal memory, sustained attention, and executive function. These impairments persisted after psychosis onset and did not further deteriorate. In contrast, CHR + NT subjects demonstrated stable mild to no impairments in neurocognitive and intellectual performance, independent of medication treatment.Cognition appears to be impaired prior to the emergence of psychotic symptoms, with no further deterioration associated with the onset of psychosis. Cognitive deficits represent trait risk markers, as opposed to state markers of disease status and may therefore serve as possible predictors of schizophrenia prior to the onset of the full illness.
Project description:Cognitive deficits have an important role in the neurodevelopment of schizophrenia and other psychotic disorders. However, there is a continuing debate as to whether cognitive impairments in the psychosis prodrome are stable predictors of eventual psychosis or undergo a decline due to the onset of psychosis. In the present study, to determine how cognition changes as illness emerges, we examined baseline neurocognitive performance in a large sample of helping-seeking youth ranging in clinical state from low-risk for psychosis through individuals at clinical high-risk (CHR) for illness to early first-episode patients (EFEP). At baseline, the MATRICS Cognitive Consensus battery was administered to 322 individuals (205 CHRs, 28 EFEPs, and 89 help-seeking controls, HSC) that were part of the larger Early Detection, Intervention and Prevention of Psychosis Program study. CHR individuals were further divided into those who did (CHR-T; n = 12, 6.8%) and did not (CHR-NT, n = 163) convert to psychosis over follow-up (Mean = 99.20 weeks, SD = 21.54). ANCOVAs revealed that there were significant overall group differences (CHR, EFEP, HSC) in processing speed, verbal learning, and overall neurocognition, relative to healthy controls (CNTL). In addition, the CHR-NTs performed similarly to the HSC group, with mild to moderate cognitive deficits relative to the CTRL group. The CHR-Ts mirrored the EFEP group, with large deficits in processing speed, working memory, attention/vigilance, and verbal learning (>1 SD below CNTLs). Interestingly, only verbal learning impairments predicted transition to psychosis, when adjusting for age, education, symptoms, antipsychotic medication, and neurocognitive performance in the other domains. Our findings suggest that large neurocognitive deficits are present prior to illness onset and represent vulnerability markers for psychosis. The results of this study further reinforce that verbal learning should be specifically targeted for preventive intervention for psychosis.
Project description:Prior studies have implicated baseline positive and negative symptoms as predictors of psychosis onset among individuals at clinical high risk (CHR), but none have evaluated latent trajectories of symptoms over time. This study evaluated the dynamic evolution of symptoms leading to psychosis onset in a CHR cohort.100 CHR participants were assessed quarterly for up to 2.5 years. Latent trajectory analysis was used to identify patterns of symptom change. Logistic and proportional hazards models were employed to evaluate the predictive value for psychosis onset of baseline symptoms and symptom trajectories.Transition rate to psychosis was 26%. Disorganized communication (i.e., subthreshold thought disorder) presented an increased hazard for psychosis onset, both at baseline (Hazard Ratio (95% CI)=1.4 (1.1-1.9)) and as a trajectory of high persistent disorganized communication (Hazard Ratio (95% CI)=2.2 (1.0-4.9)). Interval clinical data did not improve the predictive value of baseline symptoms for psychosis onset.High baseline disorganized communication evident at ascertainment tended to persist and lead to psychosis onset, consistent with prior behavioral and speech analysis studies in similar cohorts. Remediation of language dysfunction therefore may be a candidate strategy for preventive intervention.
Project description:Neurocognition is a central characteristic of schizophrenia and other psychotic disorders. Identifying the pattern and severity of neurocognitive functioning during the "near-psychotic," clinical high-risk (CHR) state of psychosis is necessary to develop accurate risk factors for psychosis and more effective and potentially preventive treatments.To identify core neurocognitive dysfunctions associated with the CHR phase, measure the ability of neurocognitive tests to predict transition to psychosis, and determine if neurocognitive deficits are robust or explained by potential confounders.In this case-control study across 8 sites, baseline neurocognitive data were collected from January 2009 to April 2013 in the second phase of the North American Prodrome Longitudinal Study (NAPLS 2). The dates of analysis were August 2015 to August 2016. The setting was a consortium of 8 university-based, outpatient programs studying the psychosis prodrome in North America. Participants were 264 healthy controls (HCs) and 689 CHR individuals, aged 12 to 35 years.Neurocognitive associations with transition to psychosis and effects of medication on neurocognition. Nineteen neuropsychological tests and 4 factors derived from factor analysis were used: executive and visuospatial abilities, verbal abilities, attention and working memory abilities, and declarative memory abilities.This study included 264 HCs (137 male and 127 female) and 689 CHR participants (398 male and 291 female). In the HCs, 145 (54.9%) were white and 119 (45.1%) were not, whereas 397 CHR participants (57.6%) were white and 291 (42.3%) were not. In the HCs, 45 (17%) were of Hispanic origin, whereas 127 CHR participants (18.4%) were of Hispanic origin. The CHR individuals were significantly impaired compared with HCs on attention and working memory abilities and declarative memory abilities. The CHR converters had large deficits in attention and working memory abilities and declarative memory abilities (Cohen d, approximately 0.80) compared with controls and performed significantly worse on these dimensions than nonconverters (Cohen d, 0.28 and 0.48, respectively). These results were not accounted for by general cognitive ability or medications. In Cox proportional hazards regression, time to conversion in those who transitioned to psychosis was significantly predicted by high verbal (premorbid) abilities (??=?0.40; hazard ratio [HR], 1.48; 95% CI, 1.08-2.04; P?=?.02), impaired declarative memory abilities (??=?-0.87; HR, 0.42; 95% CI, 0.31-0.56; P?<?.001), age (??=?-0.10; HR, 0.90; 95% CI, 0.84-0.97; P?=?.003), site, and a combined score of unusual thought content or delusional ideas and suspiciousness or persecutory ideas items (??=?0.44; HR, 1.56; 95% CI, 1.36-1.78; P?<?.001).Neurocognitive impairment, especially in attention and working memory abilities and declarative memory abilities, is a robust characteristic of CHR participants, especially those who later develop psychosis. Interventions targeting the enhancement of neurocognitive functioning are warranted in this population.
Project description:There is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not.To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals.This investigation was a cross-sectional magnetic resonance imaging (MRI) study with follow-up assessment to determine the transition status of CHR individuals. Participants were recruited from a specialized clinic for the early detection of psychosis at the Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland. Participants included individuals in the following 4 study groups: 44 healthy controls (HC group), 63 at-risk mental state (ARMS) individuals without later transition to psychosis (ARMS-NT group), 16 ARMS individuals with later transition to psychosis (ARMS-T group), and 38 antipsychotic-free patients with first-episode psychosis (FEP group). The study dates were November 2008 to November 2014. The dates of analysis were March to November 2017.Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures.The 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedges g range, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%).These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.
Project description:BACKGROUND:Most studies of neurocognitive functioning in Clinical High Risk (CHR) cohorts have examined group averages, likely concealing heterogeneous subgroups. We aimed to identify neurocognitive subgroups and to explore associated outcomes. METHODS:Data were acquired from 324 participants (mean age 18.4) in the first phase of the North American Prodrome Longitudinal Study (NAPLS-1), a multi-site consortium following individuals for up to 2 1/2?years. We applied Ward's method for hierarchical clustering data to 8 baseline neurocognitive measures, in 166 CHR individuals, 49 non-CHR youth with a family history of psychosis, and 109 healthy controls. We tested whether cluster membership was associated with conversion to psychosis, social and role functioning, and follow-up diagnosis. Analyses were repeated after data were clustered based on independently developed clinical decision rules. RESULTS:Four neurocognitive clusters were identified: Significantly Impaired (n?=?33); Mildly Impaired (n?=?82); Normal (n?=?145) and High (n?=?64). The Significantly Impaired subgroup demonstrated the largest deviations on processing speed and memory tasks and had a conversion rate of 58%, a 40% chance of developing a schizophrenia spectrum diagnosis (compared to 24.4% in the Mildly Impaired, and 10.3% in the other two groups combined), and significantly worse functioning at baseline and 12-months. Data clustered using clinical decision rules yielded similar results, pointing to high convergent validity. CONCLUSION:Neurocognitive profiles vary substantially in their severity and are associated with diagnostic and functional outcome, underscoring neurocognition as a predictor of illness outcomes. These findings, if replicated, are a first step toward personalized treatment for individuals at-risk for psychosis.