Dataset Information

A stratified model for psychosis prediction in clinical practice.

ABSTRACT:

Objective

Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test norms with a risk stratification approach.

Method

Potential predictors of psychosis (neurocognitive deficits and at-risk criteria) over 24 months were investigated in 97 at-risk patients.

Results

The final prediction model included (1) at-risk criteria (attenuated psychotic symptoms plus subjective cognitive disturbances) and (2) a processing speed deficit (digit symbol test). The model was stratified into 4 risk classes with hazard rates between 0.0 (both predictors absent) and 1.29 (both predictors present).

Conclusions

The combination of a processing speed deficit and at-risk criteria provides an optimized stratified risk assessment. Based on neurocognitive test norms, the validity of our proposed 3 risk classes could easily be examined in independent at-risk samples and, pending positive validation results, our approach could easily be applied in clinical practice in the future.

SUBMITTER: Michel C

PROVIDER: S-EPMC4193710 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

A stratified model for psychosis prediction in clinical practice.

Michel Chantal C Ruhrmann Stephan S Schimmelmann Benno G BG Klosterkötter Joachim J Schultze-Lutter Frauke F

Schizophrenia bulletin 20140307 6

<h4>Objective</h4>Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test ...[more]

PMID: 24609300

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Dataset Information

A stratified model for psychosis prediction in clinical practice.

Objective

Method

Results

Conclusions

Publications

A stratified model for psychosis prediction in clinical practice.

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