Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes.
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ABSTRACT:
SUBMITTER: Oliver D
PROVIDER: S-EPMC6478670 | biostudies-other | 2019
REPOSITORIES: biostudies-other
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Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes.
Oliver Dominic D Radua Joaquim J Reichenberg Abraham A Uher Rudolf R Fusar-Poli Paolo P
Frontiers in psychiatry 20190417
Primary prevention in individuals at Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of the field, which is mostly attributed to its epidemiological weakness. The latter, in turn, underlies the limited identification power of at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. In the first part, this perspective review dis ...[more]
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Project description:A major public health concern associated with schizophrenia and psychotic disorders is the long-term disability that involves impaired cognition, lack of social support, and an inability to function independently in the community. A critical goal of early detection and intervention studies in psychosis is therefore to understand the factors leading to this often profound impairment.To develop a predictive model of functional (social and role) outcome in a clinical high-risk sample for psychosis.Prospective, naturalistic, longitudinal 3- to 5-year follow-up study.The Recognition and Prevention Program in New York, a research clinic located in the Zucker Hillside Hospital in New York.One hundred one treatment-seeking patients at clinical high risk for psychosis. Ninety-two (91%) were followed up prospectively for a mean (SD) of 3 (1.6) years.Neurocognitive and clinical assessment.The primary outcome variables were social and role functioning at the last follow-up visit.Poor social outcome was predicted by reduced processing speed (odds ratio [OR], 1.38; 95% CI, 1.050-1.823; P?=?.02), impaired social functioning at baseline (OR, 1.85; 95% CI, 1.258-2.732; P?=?.002), and total disorganized symptoms (OR, 5.06; 95% CI, 1.548-16.527; P?=?.007). Reduced performance on tests for verbal memory (OR, 1.74; 95% CI, 1.169-2.594; P?=?.006), role functioning at baseline (OR, 1.34; 95% CI, 1.053-1.711; P?=?.02), and motor disturbances (OR, 1.77; 95% CI, 1.060-2.969; P?=?.03) predicted role outcome. The areas under the curve for the social and role prediction models were 0.824 (95% CI, 0.736-0.913; P?<?.001) and 0.77 (95% CI, 0.68-0.87; P?<?.001), respectively, demonstrating a high discriminative ability. In addition, poor functional outcomes were not entirely dependent on the development of psychosis, because 40.3% and 45.5% of nonconverters at clinical high risk had poor social and role outcomes, respectively.Results from this study support the increasing emphasis on functional decline as a critically important outcome that parallels conversion to psychosis and suggest that both psychosis and long-term functional disability are equally important targets for prevention. Reduced neurocognitive performance, functional impairments, and nonpositive attenuated symptoms at baseline were associated with an increased risk of poor functional outcomes in our sample. Poor functional outcomes were not entirely dependent on positive symptoms and the development of psychosis, further highlighting the need for intervention at this early stage of development for those who do and do not convert to a full-blown psychotic disorder.
| S-EPMC4469070 | biostudies-literature
Project description:Ongoing research suggests preliminary, though not entirely consistent, evidence of neural abnormalities in signalling prediction errors in schizophrenia. Supporting theories suggest mechanistic links between the disruption of these processes and the generation of psychotic symptoms. However, it is unknown at what stage in the pathogenesis of psychosis these impairments in prediction-error signalling develop. One major confound in prior studies is the use of medicated patients with strongly varying disease durations. Our study aims to investigate the involvement of the meso-cortico-striatal circuitry during reward prediction-error signalling in earliest stages of psychosis. We studied patients with first-episode psychosis (FEP) and help-seeking individuals at-risk for psychosis due to sub-threshold prodromal psychotic symptoms. Patients with either FEP (n?=?14), or at-risk for developing psychosis (n?=?30), and healthy volunteers (n?=?39) performed a reinforcement learning task during fMRI scanning. ANOVA revealed significant (p?<?0.05 family-wise error corrected) prediction-error signalling differences between groups in the dopaminergic midbrain and right middle frontal gyrus (dorsolateral prefrontal cortex, DLPFC). FEP patients showed disrupted reward prediction-error signalling compared to controls in both regions. At-risk patients showed intermediate activation in the midbrain that significantly differed from controls and from FEP patients, but DLPFC activation that did not differ from controls. Our study confirms that FEP patients have abnormal meso-cortical signalling of reward-prediction errors, whereas reward-prediction-error dysfunction in the at-risk patients appears to show a more nuanced pattern of activation with a degree of midbrain impairment but preserved cortical function.
| S-EPMC6006166 | biostudies-literature
Project description:OBJECTIVE:The 2-year risk of psychosis in persons who meet research criteria for a high-risk syndrome is about 15%-25%; improvements in risk prediction accuracy would benefit the development and implementation of preventive interventions. The authors sought to assess polygenic risk score (PRS) prediction of subsequent psychosis in persons at high risk and to determine the impact of adding the PRS to a previously validated psychosis risk calculator. METHODS:Persons meeting research criteria for psychosis high risk (N=764) and unaffected individuals (N=279) were followed for up to 2 years. The PRS was based on the latest schizophrenia and bipolar genome-wide association studies. Variables in the psychosis risk calculator included stressful life events, trauma, disordered thought content, verbal learning, information processing speed, and family history of psychosis. RESULTS:For Europeans, the PRS varied significantly by group and was higher in the psychosis converter group compared with both the nonconverter and unaffected groups, but was similar for the nonconverter group compared with the unaffected group. For non-Europeans, the PRS varied significantly by group; the difference between the converters and nonconverters was not significant, but the PRS was significantly higher in converters than in unaffected individuals, and it did not differ between nonconverters and unaffected individuals. The R2liability (R2 adjusted for the rate of disease risk in the population being studied, here assuming a 2-year psychosis risk between 10% and 30%) for Europeans varied between 9.2% and 12.3% and for non-Europeans between 3.5% and 4.8%. The amount of risk prediction information contributed by the addition of the PRS to the risk calculator was less than severity of disordered thoughts and similar to or greater than for other variables. For Europeans, the PRS was correlated with risk calculator variables of information processing speed and verbal memory. CONCLUSIONS:The PRS discriminates psychosis converters from nonconverters and modestly improves individualized psychosis risk prediction when added to a psychosis risk calculator. The schizophrenia PRS shows promise in enhancing risk prediction in persons at high risk for psychosis, although its potential utility is limited by poor performance in persons of non-European ancestry.
| S-EPMC7202227 | biostudies-literature
Project description:BackgroundThe benefits of indicated primary prevention among individuals at Clinical High Risk for Psychosis (CHR-P) are limited by the difficulty in detecting these individuals. To overcome this problem, a transdiagnostic, clinically based, individualized risk calculator has recently been developed and subjected to a first external validation in 2 different catchment areas of the South London and Maudsley (SLaM) NHS Trust.MethodsSecond external validation of real world, real-time electronic clinical register-based cohort study. All individuals who received a first ICD-10 index diagnosis of nonorganic and nonpsychotic mental disorder within the Camden and Islington (C&I) NHS Trust between 2009 and 2016 were included. The model previously validated included age, gender, ethnicity, age by gender, and ICD-10 index diagnosis to predict the development of any ICD-10 nonorganic psychosis. The model's performance was measured using Harrell's C-index.ResultsThis study included a total of 13702 patients with an average age of 40 (range 16-99), 52% were female, and most were of white ethnicity (64%). There were no CHR-P or child/adolescent services in the C&I Trust. The C&I and SLaM Trust samples also differed significantly in terms of age, gender, ethnicity, and distribution of index diagnosis. Despite these significant differences, the original model retained an acceptable predictive performance (Harrell's C of 0.73), which is comparable to that of CHR-P tools currently recommended for clinical use.ConclusionsThis risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided.
| S-EPMC6483570 | biostudies-literature
Project description:The real-world impact of psychosis prevention is reliant on effective strategies for identifying individuals at risk. A transdiagnostic, individualized, clinically-based risk calculator to improve this has been developed and externally validated twice in two different UK healthcare trusts with convincing results. The prognostic performance of this risk calculator outside the UK is unknown. All individuals who accessed primary or secondary health care services belonging to the IBM® MarketScan® Commercial Database between January 2015 and December 2017, and received a first ICD-10 index diagnosis of nonorganic/nonpsychotic mental disorder, were included. According to the risk calculator, age, gender, ethnicity, age-by-gender, and ICD-10 cluster diagnosis at index date were used to predict development of any ICD-10 nonorganic psychotic disorder. Because patient-level ethnicity data were not available city-level ethnicity proportions were used as proxy. The study included 2,430,333 patients with a mean follow-up of 15.36 months and cumulative incidence of psychosis at two years of 1.43%. There were profound differences compared to the original development UK database in terms of case-mix, psychosis incidence, distribution of baseline predictors (ICD-10 cluster diagnoses), availability of patient-level ethnicity data, follow-up time and availability of specialized clinical services for at-risk individuals. Despite these important differences, the model retained accuracy significantly above chance (Harrell's C = 0.676, 95% CI: 0.672-0.679). To date, this is the largest international external replication of an individualized prognostic model in the field of psychiatry. This risk calculator is transportable on an international scale to improve the automatic detection of individuals at risk of psychosis.
| S-EPMC7596040 | biostudies-literature
Project description:BackgroundRisk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders.MethodsWe reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured.ResultsSix risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.ConclusionsThis is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data.
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Project description:ObjectiveWe compared the accuracy of clinicians and a risk score (iScore) to predict observed outcomes following an acute ischemic stroke.MethodsThe JURaSSiC (Clinician JUdgment vs Risk Score to predict Stroke outComes) study assigned 111 clinicians with expertise in acute stroke care to predict the probability of outcomes of 5 ischemic stroke case scenarios. Cases (n = 1,415) were selected as being representative of the 10 most common clinical presentations from a pool of more than 12,000 stroke patients admitted to 12 stroke centers. The primary outcome was prediction of death or disability (modified Rankin Scale [mRS] ≥3) at discharge within the 95% confidence interval (CI) of observed outcomes. Secondary outcomes included 30-day mortality and death or institutionalization at discharge.ResultsClinicians made 1,661 predictions with overall accuracy of 16.9% for death or disability at discharge, 46.9% for 30-day mortality, and 33.1% for death or institutionalization at discharge. In contrast, 90% of the iScore-based estimates were within the 95% CI of observed outcomes. Nearly half (n = 53 of 111; 48%) of participants were unable to accurately predict the probability of the primary outcome in any of the 5 rated cases. Less than 1% (n = 1) provided accurate predictions in 4 of the 5 cases and none accurately predicted all 5 case outcomes. In multivariable analyses, the presence of patient characteristics associated with poor outcomes (mRS ≥3 or death) in previous studies (older age, high NIH Stroke Scale score, and nonlacunar subtype) were associated with more accurate clinician predictions of death at 30 days (odds ratio [OR] 2.40, 95% CI 1.57-3.67) and with a trend for more accurate predictions of death or disability at discharge (OR 1.85, 95% CI 0.99-3.46).ConclusionsClinicians with expertise in stroke performed poorly compared to a validated tool in predicting the outcomes of patients with an acute ischemic stroke. Use of the risk stroke outcome tool may be superior for decision-making following an acute ischemic stroke.
| S-EPMC3776534 | biostudies-literature
Project description:ImportancePreclinical and human data suggest that hippocampal dysfunction plays a critical role in the onset of psychosis. Neural hyperactivity in the hippocampus is thought to drive an increase in subcortical dopamine function through glutamatergic projections to the striatum.ObjectiveTo examine the association between hippocampal glutamate levels in individuals at clinical high risk for psychosis and their subsequent clinical outcomes.Design, setting, and participantsThis cross-sectional study of 86 individuals at clinical high risk for psychosis and 30 healthy control individuals, with a mean follow-up of 18.5 months, was conducted between November 1, 2011, and November 1, 2017, at early detection services in London and Cambridge, United Kingdom.Main outcomes and measuresConcentrations of glutamate and other metabolites were measured in the left hippocampus using 3-T proton magnetic resonance spectroscopy at the first clinical presentation. At follow-up, clinical outcomes were assessed in terms of transition or nontransition to psychosis using the Comprehensive Assessment of the At-Risk Mental State criteria and the level of overall functioning using the Global Assessment of Function scale.ResultsOf 116 total participants, 86 were at clinical high risk for psychosis (50 [58%] male; mean [SD] age, 22.4 [3.5] years) and 30 were healthy controls (14 [47%] male; mean [SD] age, 24.7 [3.8] years). At follow-up, 12 clinical high-risk individuals developed a first episode of psychosis whereas 74 clinical high-risk individuals did not; 19 clinical high-risk individuals showed good overall functioning (Global Assessment of Function ≥65), whereas 38 clinical high-risk individuals had a poor functional outcome (Global Assessment of Function <65). Compared with clinical high-risk individuals who did not become psychotic, clinical high-risk individuals who developed psychosis showed higher hippocampal glutamate levels (mean [SD], 8.33 [1.48] vs 9.16 [1.28] glutamate levels; P = .048). The clinical high-risk individuals who developed psychosis also had higher myo-inositol levels (mean [SD], 7.60 [1.23] vs 6.24 [1.36] myo-inositol levels; P = .002) and higher creatine levels (mean [SD], 8.18 [0.74] vs 7.32 [1.09] creatine levels; P = .01) compared with clinical high-risk individuals who did not become psychotic, and higher myo-inositol levels compared with healthy controls (mean [SD], 7.60 [1.23] vs 6.19 [1.51] myo-inositol levels; P = .005). Higher hippocampal glutamate levels in clinical high-risk individuals were also associated with a poor functional outcome (mean [SD], 8.83 [1.43] vs 7.76 [1.40] glutamate levels; P = .02). In the logistic regression analyses, hippocampal glutamate levels were significantly associated with clinical outcome in terms of transition and nontransition to psychosis (β = 0.48; odds ratio = 1.61; 95% CI, 1.00-2.59; P = .05) and overall functioning (β = 0.53; odds ratio = 1.71; 95% CI, 1.10-2.66; P = .02).Conclusions and relevanceThe findings indicate that adverse clinical outcomes in individuals at clinical high risk for psychosis may be associated with an increase in baseline hippocampal glutamate levels, as well as an increase in myo-inositol and creatine levels. This conclusion suggests that these measures could contribute to the stratification of clinical high-risk individuals according to future clinical outcomes.
| S-EPMC6440239 | biostudies-literature
Project description:BackgroundRisk estimation models integrated into Electronic Health Records (EHRs) can deliver innovative approaches in psychiatry, but clinicians' endorsement and their real-world usability are unknown. This study aimed to investigate the real-world feasibility of implementing an individualised, transdiagnostic risk calculator to automatically screen EHRs and detect individuals at-risk for psychosis.MethodsFeasibility implementation study encompassing an in-vitro phase (March 2018 to May 2018) and in-vivo phase (May 2018 to April 2019). The in-vitro phase addressed implementation barriers and embedded the risk calculator (predictors: age, gender, ethnicity, index cluster diagnosis, age*gender) into the local EHR. The in-vivo phase investigated the real-world feasibility of screening individuals accessing secondary mental healthcare at the South London and Maudsley NHS Trust. The primary outcome was adherence of clinicians to automatic EHR screening, defined by the proportion of clinicians who responded to alerts from the risk calculator, over those contacted.ResultsIn-vitro phase: implementation barriers were identified/overcome with clinician and service user engagement, and the calculator was successfully integrated into the local EHR through the CogStack platform. In-vivo phase: 3722 individuals were automatically screened and 115 were detected. Clinician adherence was 74% without outreach and 85% with outreach. One-third of clinicians responded to the first email (37.1%) or phone calls (33.7%). Among those detected, cumulative risk of developing psychosis was 12% at six-month follow-up.ConclusionThis is the first implementation study suggesting that combining precision psychiatry and EHR methods to improve detection of individuals with emerging psychosis is feasible. Future psychiatric implementation research is urgently needed.
| S-EPMC7875179 | biostudies-literature
Project description:ImportanceSocial and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses.ObjectiveTo determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning.Design, setting, and participantsThis multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018.ain outcomes and measuresPerformance and generalizability of prognostic models.ResultsA total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD.Conclusions and relevancePrecision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.
| S-EPMC6248111 | biostudies-literature