An imaging-based risk calculator for prediction of conversion to psychosis in clinical high-risk individuals using glutamate 1H MRS

Abstract Background Antipsychotics are widely used for treating patients with psychosis, and target threshold psychotic symptoms. Individuals at clinical high risk (CHR) for psychosis are characterized by subthreshold psychotic symptoms. It is currently unclear who might benefit from antipsychotic treatment. Our objective was to apply a risk calculator (RC) to identify people that would benefit from antipsychotics. Methods Drawing on 400 CHR individuals recruited between 2011 and 2016, 208 individuals who received antipsychotic treatment were included. Clinical and cognitive variables were entered into an individualized RC for psychosis; personal risk was estimated and 4 risk components (negative symptoms-RC-NS, general function-RC-GF, cognitive performance-RC-CP, and positive symptoms-RC-PS) were constructed. The sample was further stratified according to the risk level. Higher risk was defined based on the estimated risk score (20% or higher). Results In total, 208 CHR individuals received daily antipsychotic treatment of an olanzapine-equivalent dose of 8.7 mg with a mean administration duration of 58.4 weeks. Of these, 39 (18.8%) developed psychosis within 2 years. A new index of factors ratio (FR), which was derived from the ratio of RC-PS plus RC-GF to RC-NS plus RC-CP, was generated. In the higher-risk group, as FR increased, the conversion rate decreased. A small group (15%) of CHR individuals at higher-risk and an FR >1 benefitted from the antipsychotic treatment. Conclusions Through applying a personal risk assessment, the administration of antipsychotics should be limited to CHR individuals with predominantly positive symptoms and related function decline. A strict antipsychotic prescription strategy should be introduced to reduce inappropriate use.

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Reduced willingness to expend effort for rewards is associated with risk for conversion and negative symptom severity in youth at clinical high-risk for psychosis

Psychological Medicine ◽

10.1017/s003329172100204x ◽

2021 ◽

pp. 1-8

Author(s):

Gregory P. Strauss ◽

Lisa A. Bartolomeo ◽

Lauren Luther

Keyword(s):

High Risk ◽

Negative Symptom ◽

Symptom Severity ◽

High Probability ◽

Negative Symptoms ◽

Positive Symptoms ◽

Clinical High Risk ◽

Risk Calculator ◽

Cross Sectional ◽

High Effort

Abstract Background Schizophrenia (SZ) is typically preceded by a prodromal (i.e. pre-illness) period characterized by attenuated positive symptoms and declining functional outcome. Negative symptoms are prominent among individuals at clinical high-risk (CHR) for psychosis (i.e. those with prodromal syndromes) and predictive of conversion to illness. Mechanisms underlying negative symptoms are unclear in the CHR population. Methods The current study evaluated whether CHR participants demonstrated deficits in the willingness to expend effort for rewards and whether these impairments are associated with negative symptoms and greater risk for conversion. Participants included 44 CHR participants and 32 healthy controls (CN) who completed the Effort Expenditure for Reward Task (EEfRT). Results Compared to CN, CHR participants displayed reduced likelihood of exerting high effort for high probability and magnitude rewards. Among CHR participants, reduced effort expenditure was associated with greater negative symptom severity and greater probability of conversion to a psychotic disorder on a cross-sectional risk calculator. Conclusions Findings suggest that effort-cost computation is a marker of illness liability and a transphasic mechanism underlying negative symptoms in the SZ spectrum.

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S105. 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

Schizophrenia Bulletin ◽

10.1093/schbul/sby018.892 ◽

2018 ◽

Vol 44 (suppl_1) ◽

pp. S366-S366

Author(s):

TianHong Zhang ◽

HuiJun Li ◽

LiHua Xu ◽

YingYing Tang ◽

HuiRu Cui ◽

...

Keyword(s):

At Risk ◽

High Risk ◽

Predictive Accuracy ◽

Clinical High Risk ◽

Risk Calculator ◽

Psychosis Risk

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Predictors and mechanisms of conversion to psychosis

Die Psychiatrie ◽

10.1055/s-0038-1672283 ◽

2016 ◽

Vol 13 (03) ◽

pp. 130-135

Author(s):

T. D. Cannon

Keyword(s):

Risk Factors ◽

Prefrontal Cortex ◽

High Risk ◽

Gray Matter ◽

Microglial Activation ◽

Clinical High Risk ◽

Preventive Interventions ◽

Risk Calculator ◽

Synaptic Pruning ◽

Pro Inflammatory Cytokines

Summary Background: Identifying predictors and elucidating mechanisms underlying onset of psychosis are critical for the development of preventive interventions. Methods: This paper reviews findings on risk prediction algorithms and potential mechanisms of onset in youth at clinical high-risk for psychosis. Results: An independently validated individualized risk calculator is available that incorporates risk factors from clinical, demographic, neurocognitive, and psychosocial assessments to predict likelihood of conversion to psychosis among individuals who meet criteria for a prodromal risk syndrome. At risk individuals who convert to psychosis show a steeper rate of gray matter reduction, most prominent in prefrontal cortex, compared with those who do not. Higher levels of pro-inflammatory cytokines at baseline predicted the accelerated reduction in prefrontal gray matter. These same markers are associated with microglial-mediated synaptic pruning and dendritic retraction in animal models. Conclusions: Processes that modulate microglial activation, such as dysregulated immune function and deficient synaptic plasticity, may represent convergent mechanisms that influence brain dysconnectivity and risk for onset of psychosis.

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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

American Journal of Psychiatry ◽

10.1176/appi.ajp.2018.18010036 ◽

2018 ◽

Vol 175 (9) ◽

pp. 906-908 ◽

Cited By ~ 24

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

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

Psychological Medicine ◽

10.1017/s0033291719002174 ◽

2019 ◽

Vol 50 (9) ◽

pp. 1578-1584 ◽

Cited By ~ 4

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.

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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

Psychological Medicine ◽

10.1017/s003329171900360x ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 6

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.

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