Poor functional recovery is better predicted than conversion in studies of outcomes of clinical high risk of psychosis: insight from SHARP

2019 ◽  
Vol 50 (9) ◽  
pp. 1578-1584 ◽  
Author(s):  
TianHong Zhang ◽  
ShuWen Yang ◽  
LiHua Xu ◽  
XiaoChen Tang ◽  
YanYan Wei ◽  
...  
Keyword(s):  
High Risk ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Structured Interview ◽  
Clinical High Risk ◽  
Risk Calculator ◽  
Subsequent Conversion ◽  
Treatment Responses ◽  
Current Risk

AbstractBackgroundFew of the previous studies of clinical high risk of psychosis (CHR) have explored whether outcomes other than conversion, such as poor functioning or treatment responses, are better predicted when using risk calculators. To answer this question, we compared the predictive accuracy between the outcome of conversion and poor functioning by using the NAPLS-2 risk calculator.MethodsThree hundred CHR individuals were identified using the Chinese version of the Structured Interview for Prodromal Symptoms. Of these, 228 (76.0%) completed neurocognitive assessments at baseline and 199 (66.3%) had at least a 1-year follow-up assessment. The latter group was used in the NAPLS-2 risk calculator.ResultsWe divided the sample into two broad categories based on different outcome definitions, conversion (n = 46) v. non-conversion (n = 153) or recovery (n = 138) v. poor functioning (n = 61). Interestingly, the NAPLS-2 risk calculator showed moderate discrimination of subsequent conversion to psychosis in this sample with an area under the receiver operating characteristic curve (AUC) of 0.631 (p = 0.007). However, for discriminating poor functioning, the AUC of the model increased to 0.754 (p < 0.001).ConclusionsOur results suggest that the current risk calculator was a better fit for predicting a poor functional outcome and treatment response than it was in the prediction of conversion to psychosis.

scholarly journals Validating the Predictive Accuracy of the NAPLS-2 Psychosis Risk Calculator in a Clinical High-Risk Sample From the SHARP (Shanghai At Risk for Psychosis) Program

2018 ◽  
Vol 175 (9) ◽  
pp. 906-908 ◽  
Author(s):  
TianHong Zhang ◽  
HuiJun Li ◽  
YingYing Tang ◽  
Margaret A. Niznikiewicz ◽  
Martha E. Shenton ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Predictive Accuracy ◽  
Clinical High Risk ◽  
Risk Calculator ◽  
Psychosis Risk

scholarly journals Impaired Sensorimotor Gating Using the Acoustic Prepulse Inhibition Paradigm in Individuals at a Clinical High Risk for Psychosis

2020 ◽  
Author(s):  
Qijing Bo ◽  
Zhen Mao ◽  
Qing Tian ◽  
Ningbo Yang ◽  
Xianbin Li ◽  
...  
Keyword(s):  
High Risk ◽  
Prepulse Inhibition ◽  
Characteristic Curve ◽  
Structured Interview ◽  
First Episode ◽  
Enhancement Effect ◽  
Clinical High Risk ◽  
Stimulus Interval ◽  
Cohen’S D ◽  
Cohen's D

Abstract Many robust studies have investigated prepulse inhibition (PPI) in patients with schizophrenia. Recent evidence indicates that PPI may help identify individuals who are at clinical high risk for psychosis (CHR). Selective attention to prepulse stimulus can specifically enhance PPI in healthy subjects; however, this enhancement effect is not observed in patients with schizophrenia. Modified PPI measurement with selective attentional modulation using perceived spatial separation (PSS) condition may be a more robust and sensitive index of PPI impairment in CHR individuals. The current study investigated an improved PSSPPI condition in CHR individuals compared with patients with first-episode schizophrenia (FES) and healthy controls (HC) and evaluated the accuracy of PPI in predicting CHR from HC. We included 53 FESs, 55 CHR individuals, and 53 HCs. CHRs were rated on the Structured Interview for Prodromal Syndromes. The measures of perceived spatial co-location PPI (PSCPPI) and PSSPPI conditions were applied using 60- and 120-ms lead intervals. Compared with HC, the CHR group had lower PSSPPI level (Inter-stimulus interval [ISI] = 60 ms, P &lt; .001; ISI = 120 ms, P &lt; .001). PSSPPI showed an effect size (ES) between CHR and HC (ISI = 60 ms, Cohen’s d = 0.91; ISI = 120 ms, Cohen’s d = 0.98); on PSSPPI using 60-ms lead interval, ES grade increased from CHR to FES. The area under the receiver operating characteristic curve for PSSPPI was greater than that for PSCPPI. CHR individuals showed a PSSPPI deficit similar to FES, with greater ES and sensitivity. PSSPPI appears a promising objective approach for preliminary identification of CHR individuals.

Calculating individualized risk components using a mobile app-based risk calculator for clinical high risk of psychosis: findings from ShangHai At Risk for Psychosis (SHARP) program

2019 ◽  
pp. 1-8 ◽  
Author(s):  
TianHong Zhang ◽  
LiHua Xu ◽  
HuiJun Li ◽  
Kristen A. Woodberry ◽  
Emily R. Kline ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Structured Interview ◽  
Risk Identification ◽  
Mobile App ◽  
Individual Risk ◽  
Second Phase ◽  
Clinical High Risk ◽  
Risk Calculator ◽  
Psychosis Risk

Abstract Background Only 30% or fewer of individuals at clinical high risk (CHR) convert to full psychosis within 2 years. Efforts are thus underway to refine risk identification strategies to increase their predictive power. Our objective was to develop and validate the predictive accuracy and individualized risk components of a mobile app-based psychosis risk calculator (RC) in a CHR sample from the SHARP (ShangHai At Risk for Psychosis) program. Method In total, 400 CHR individuals were identified by the Chinese version of the Structured Interview for Prodromal Syndromes. In the first phase of 300 CHR individuals, 196 subjects (65.3%) who completed neurocognitive assessments and had at least a 2-year follow-up assessment were included in the construction of an RC for psychosis. In the second phase of the SHARP sample of 100 subjects, 93 with data integrity were included to validate the performance of the SHARP-RC. Results The SHARP-RC showed good discrimination of subsequent transition to psychosis with an AUC of 0.78 (p < 0.001). The individualized risk generated by the SHARP-RC provided a solid estimation of conversion in the independent validation sample, with an AUC of 0.80 (p = 0.003). A risk estimate of 20% or higher had excellent sensitivity (84%) and moderate specificity (63%) for the prediction of psychosis. The relative contribution of individual risk components can be simultaneously generated. The mobile app-based SHARP-RC was developed as a convenient tool for individualized psychosis risk appraisal. Conclusions The SHARP-RC provides a practical tool not only for assessing the probability that an individual at CHR will develop full psychosis, but also personal risk components that might be targeted in early intervention.

Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): when things are not what they seem

2020 ◽  
pp. 1-9
Author(s):  
Andrea Raballo ◽  
Michele Poletti ◽  
Antonio Preti
Keyword(s):  
High Risk ◽  
Meta Analysis ◽  
Prognostic Impact ◽  
Clinical High Risk ◽  
Qualitative Synthesis ◽  
Prognostic Effect ◽  
Inverse Variance ◽  
Arcsine Transformation ◽  
Estimate Prevalence

Abstract Background The clinical high-risk (CHR) for psychosis paradigm is changing psychiatric practice. However, a widespread confounder, i.e. baseline exposure to antipsychotics (AP) in CHR samples, is systematically overlooked. Such exposure might mitigate the initial clinical presentation, increase the heterogeneity within CHR populations, and confound the evaluation of transition to psychosis at follow-up. This is the first meta-analysis examining the prevalence and the prognostic impact on transition to psychosis of ongoing AP treatment at baseline in CHR cohorts. Methods Major databases were searched for articles published until 20 April 2020. The variance-stabilizing Freeman-Tukey double arcsine transformation was used to estimate prevalence. The binary outcome of transition to psychosis by group was estimated with risk ratio (RR) and the inverse variance method was used for pooling. Results Fourteen studies were eligible for qualitative synthesis, including 1588 CHR individuals. Out of the pooled CHR sample, 370 individuals (i.e. 23.3%) were already exposed to AP at the time of CHR status ascription. Transition toward full-blown psychosis at follow-up intervened in 112 (29%; 95% CI 24–34%) of the AP-exposed CHR as compared to 235 (16%; 14–19%) of the AP-naïve CHR participants. AP-exposed CHR had higher RR of transition to psychosis (RR = 1.47; 95% CI 1.18–1.83; z = 3.48; p = 0.0005), without influence by age, gender ratio, overall sample size, duration of the follow-up, or quality of the studies. Conclusions Baseline AP exposure in CHR samples is substantial and is associated with a higher imminent risk of transition to psychosis. Therefore, such exposure should be regarded as a non-negligible red flag for clinical risk management.

Clinical high risk for psychosis in childhood and adolescence: findings from the 2-year follow-up of the ReARMS project

2018 ◽  
Vol 28 (7) ◽  
pp. 957-971 ◽  
Author(s):  
Michele Poletti ◽  
Lorenzo Pelizza ◽  
Silvia Azzali ◽  
Federica Paterlini ◽  
Sara Garlassi ◽  
...  
Keyword(s):  
High Risk ◽  
Childhood And Adolescence ◽  
Clinical High Risk

scholarly journals Clinical subtypes that predict conversion to psychosis: A canonical correlation analysis study from the ShangHai At Risk for Psychosis program

2019 ◽  
Vol 54 (5) ◽  
pp. 482-495 ◽  
Author(s):  
TianHong Zhang ◽  
XiaoChen Tang ◽  
HuiJun Li ◽  
Kristen A Woodberry ◽  
Emily R Kline ◽  
...  
Keyword(s):  
At Risk ◽  
Cluster Analysis ◽  
High Risk ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Clinical Symptoms ◽  
Structured Interview ◽  
Two Dimensions ◽  
Clinical High Risk

Objective: Since only 30% or fewer of individuals at clinical high risk convert to psychosis within 2 years, efforts are underway to refine risk identification strategies to increase their predictive power. The clinical high risk is a heterogeneous syndrome presenting with highly variable clinical symptoms and cognitive dysfunctions. This study investigated whether subtypes defined by baseline clinical and cognitive features improve the prediction of psychosis. Method: Four hundred clinical high-risk subjects from the ongoing ShangHai At Risk for Psychosis program were enrolled in a prospective cohort study. Canonical correlation analysis was applied to 289 clinical high-risk subjects with completed Structured Interview for Prodromal Syndromes and cognitive battery tests at baseline, and at least 1-year follow-up. Canonical variates were generated by canonical correlation analysis and then used for hierarchical cluster analysis to produce subtypes. Kaplan–Meier survival curves were constructed from the three subtypes to test their utility further in predicting psychosis. Results: Canonical correlation analysis determined two linear combinations: (1) negative symptom and functional deterioration-related cognitive features, and (2) Positive symptoms and emotional disorganization-related cognitive features. Cluster analysis revealed three subtypes defined by distinct and relatively homogeneous patterns along two dimensions, comprising 14.2% (subtype 1, n = 41), 37.4% (subtype 2, n = 108) and 48.4% (subtype 3, n = 140) of the sample, and each with distinctive features of clinical and cognitive performance. Those with subtype 1, which is characterized by extensive negative symptoms and cognitive deficits, appear to have the highest risk for psychosis. The conversion risk for subtypes 1–3 are 39.0%, 11.1% and 18.6%, respectively. Conclusion: Our results define important subtypes within clinical high-risk syndromes that highlight clinical symptoms and cognitive features that transcend current diagnostic boundaries. The three different subtypes reflect significant differences in clinical and cognitive characteristics as well as in the risk of conversion to psychosis.

scholarly journals The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

2020 ◽  
pp. 009385482096975
Author(s):  
Mehdi Ghasemi ◽  
Daniel Anvari ◽  
Mahshid Atapour ◽  
J. Stephen wormith ◽  
Keira C. Stockdale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Assessment Instrument ◽  
Support Vector ◽  
Data Set ◽  
Applied Machine Learning ◽  
Vector Machines ◽  
Individual Scores

The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.

scholarly journals T39. IMPAIRED SENSORIMOTOR GATING USING THE ACOUSTIC PREPULSE INHIBITION PARADIGM IN INDIVIDUALS AT A CLINICAL HIGH RISK FOR PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S246-S246
Author(s):  
Qijing Bo ◽  
Zhen Mao ◽  
Qing Tian ◽  
Weidi Li ◽  
Lei Zhao ◽  
...  
Keyword(s):  
High Risk ◽  
Prepulse Inhibition ◽  
Clinical Characteristics ◽  
Sensorimotor Gating ◽  
Spatial Separation ◽  
Structured Interview ◽  
First Episode ◽  
Clinical High Risk ◽  
Neurocognitive Functions ◽  
First Episode Schizophrenia

Abstract Background Many robust studies on prepulse inhibition (PPI) were conducted in patients with schizophrenia, and, increasingly, evidence has indicated individuals who are at clinical high risk for psychosis (CHR). The specificity of the PPI is insufficient with the classic paradigm. The current study investigated an improved perceived spatial separation PPI (PSSPPI) paradigm in CHR individuals, compared with patients of first-episode schizophrenia (FES) and healthy controls (HC), and the relationship between PPI, demographics, clinical characteristics, and cognitive performance. Methods We included 53 FESs, 55 CHR individuals, and 53 HCs. CHRs were rated on the Structured Interview for Prodromal Syndromes (SIPS). The prepulse inhibition measures of perceived spatial co-location PPI (PSCPPI) and PSSPPI paradigms were applied using 60- and 120-ms lead intervals. The MATRICS Consensus Cognitive Battery (MCCB) was used to assess neurocognitive functions. Results Compared with HC, the CHR group had lower PSSPPI level (ISI=60 ms, P&lt;0.001; ISI=120 ms, P&lt;.001). PSSPPI showed a large effect size (ES) between CHR and HC (ISI=60 ms, ES=0.91; ISI=120 ms, ES=0.98); on PSSPPI using 60-ms lead interval, ES ranged from small to large from CHR to FES. PPI deficits in CHR were unrelated to demographics, clinical characteristics, and cognition. Discussion CHR individuals show a sensorimotor gating deficit similar to FES patients on PSSPPI of the startle response, with greater sensitivity than the classic PPI paradigm. PSSPPI appears a promising objective approach for identifying individuals at clinical high risk for psychosis related to a high risk of transition to schizophrenia.

scholarly journals Two-year follow-up of a Chinese sample at clinical high risk for psychosis: timeline of symptoms, help-seeking and conversion

2016 ◽  
Vol 26 (3) ◽  
pp. 287-298 ◽  
Author(s):  
T. H. Zhang ◽  
H. J. Li ◽  
K. A. Woodberry ◽  
L. H. Xu ◽  
Y. Y. Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Symptom Onset ◽  
Help Seeking ◽  
Cox Regression ◽  
Clinical Population ◽  
Psychotic Symptoms ◽  
Clinical High Risk ◽  
Chinese Sample ◽  
Over Time

Background.Chinese psychiatrists have gradually started to focus on those who are deemed to be at ‘clinical high-risk (CHR)’ for psychosis; however, it is still unknown how often those individuals identified as CHR from a different country background than previously studied would transition to psychosis. The objectives of this study are to examine baseline characteristics and the timing of symptom onset, help-seeking, or transition to psychosis over a 2-year period in China.Method.The presence of CHR was determined with the Structured Interview for Prodromal Syndromes (SIPS) at the participants' first visit to the mental health services. A total of 86 (of 117) CHR participants completed the clinical follow-up of at least 2 years (73.5%). Conversion was determined using the criteria of presence of psychotic symptoms (in SIPS). Analyses examined baseline demographic and clinical predictors of psychosis and trajectory of symptoms over time. Survival analysis (Kaplan–Meier) methods along with Log-rank tests were performed to illustrate the relationship of baseline data to either conversion or non-conversion over time. Cox regression was performed to identify baseline predictors of conversion by the 2-year follow-up.Results.In total 25 (29.1%) of 86 completers transitioned to a psychotic disorder over the course of follow-up. Among the CHR sample, the mean time between attenuated symptom onset and professional help-seeking was about 4 months on average, and converters developed fully psychotic symptoms about 12 months after symptom onset. Compared with those CHR participants whose risk syndromes remitted over the course of the study, converters had significantly longer delays (p = 0.029) for their first visit to a professional in search of help. At baseline assessment, the conversion subgroup was younger, had poorer functioning, higher total SIPS positive symptom scores, longer duration of untreated prodromal symptoms, and were more often given psychosis-related diagnoses and subsequently prescribed antipsychotics in the clinic.Conclusions.Chinese CHR identified primarily by a novel clinical screening approach had a 2-year transition rate comparable with those of specialised help-seeking samples world-wide. Early clinical intervention with this functionally deteriorating clinical population who are suffering from attenuated psychotic symptoms, is a next step in applying the CHR construct in China.

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