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Personalized Prediction of Psychosis: External Validation of the NAPLS-2 Psychosis Risk Calculator With the EDIPPP Project

Published on Oct 1, 2016in American Journal of Psychiatry13.655
· DOI :10.1176/appi.ajp.2016.15121565
Ricardo E. Carrión17
Estimated H-index: 17
(UM: University of Michigan),
Barbara A. Cornblatt38
Estimated H-index: 38
(UM: University of Michigan)
+ 9 AuthorsWilliam R. McFarlane27
Estimated H-index: 27
(UM: University of Michigan)
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Abstract
Objective:As part of the second phase of the North American Prodrome Longitudinal Study (NAPLS-2), Cannon and colleagues report, concurrently with the present article, on a risk calculator for the individualized prediction of a psychotic disorder in a 2-year period. The present study represents an external validation of the NAPLS-2 psychosis risk calculator using an independent sample of patients at clinical high risk for psychosis collected as part of the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP).Method:Of the total EDIPPP sample of 210 subjects rated as being at clinical high risk based on the Structured Interview for Prodromal Syndromes, 176 had at least one follow-up assessment and were included in the construction of a new prediction model with six predictor variables in the NAPLS-2 psychosis risk calculator (unusual thoughts and suspiciousness, symbol coding test performance, verbal learning test performance, decline in social functioning, baseline age, and family h...
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#2Carrie E. Bearden (Semel Institute for Neuroscience and Human Behavior)H-Index: 59
Last. Albert R. Powers (Yale University)H-Index: 10
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Abstract Background Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis. Method Response to points of critique. Results We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c)...
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#1Łukasz Gawęda (PAN: Polish Academy of Sciences)
#2Barnaby Nelson (University of Melbourne)H-Index: 44
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#1Rachele Sanfelici (LMU: Ludwig Maximilian University of Munich)H-Index: 1
#2Dominic B. Dwyer (LMU: Ludwig Maximilian University of Munich)H-Index: 12
Last. Nikolaos Koutsouleris (LMU: Ludwig Maximilian University of Munich)H-Index: 36
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Abstract Background The Clinical High Risk(CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods like machine learning(ML) and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic, i.e., distinguishing CHR from healthy individuals, and prognostic models, i.e., predicti...
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Personalization is becoming an important feature in many predictive applications. We introduce a penalized regression method implementing personalization inherently in the penalty. Personalized angle (PAN) regression constructs regression coefficients that are specific to the covariate vector for which one is producing a prediction, thus personalizing the regression model itself. This is achieved by penalizing the angles in a hyperspherical parametrization of the regression coefficients. For an ...
#2David J. Miklowitz (UCLA: University of California, Los Angeles)H-Index: 63
Last. Tyrone D. CannonH-Index: 89
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AIM: Recent findings suggest that family-focused therapy (FFT) is effective for individuals at clinical high-risk for psychosis (CHR-P). As outcomes of CHR-P individuals are quite varied, certain psychosocial interventions may be differentially effective in subgroups. The present study examined change in positive symptoms for CHR-P individuals at different levels of predicted risk for conversion to psychosis who received either FFT, a brief form of family education termed enhanced care (EC) or t...
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#1Thomas E. Smith (Columbia University)H-Index: 20
#2Lisa B. Dixon (Columbia University)H-Index: 56
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#1Tianhong ZhangH-Index: 14
#2Lihua XuH-Index: 7
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#1Michelle A. Worthington (Yale University)H-Index: 1
#2Hengyi Cao (Yale University)H-Index: 8
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Abstract In the past two to three decades, clinicians have used the clinical high-risk for psychosis (CHR-P) paradigm to better understand factors that contribute to the onset of psychotic disorders. While this paradigm is useful to identify individuals at risk, the CHR-P criteria are not sufficient to predict outcomes from the CHR-P population. As approximately 25% of the CHR-P population will ultimately convert to psychosis, more precise methods of prediction are needed to account for heteroge...
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Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO a...
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