TY - JOUR
T1 - Improving prognostic accuracy in subjects at clinical high risk for psychosis
T2 - systematic review of predictive models and meta-analytical sequential testing simulation
AU - Schmidt, André
AU - Cappucciati, Marco
AU - Radua, Joaquim
AU - Rutigliano, Grazia
AU - Rocchetti, Matteo
AU - Dell'Osso, Liliana
AU - Politi, Pierluigi
AU - Borgwardt, Stefan
AU - Reilly, Thomas
AU - Valmaggia, Lucia
AU - McGuire, Philip
AU - Fusar-Poli, Paolo
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Discriminating subjects at clinical high risk for psychosis (CHR) who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalised prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modelling studies using clinical, biological, neurocognitive, environmental and combinations of predictors. In a second step we performed statistical simulations to test different probabilistic sequential three-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive and combined models) yielded PPVs of above 82%. Using only data from validated models, three-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + EEG), then structural MRI and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT=2) for an individual with three positive sequential tests, 71-82% (NNT=3) with two positive tests, 12-21% (NNT=11-18) with one positive test, and 1% (NNT=219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multi-stage sequential testing may allow individual risk stratification of CHR individuals and optimise the prediction of psychosis.
AB - Discriminating subjects at clinical high risk for psychosis (CHR) who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalised prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modelling studies using clinical, biological, neurocognitive, environmental and combinations of predictors. In a second step we performed statistical simulations to test different probabilistic sequential three-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive and combined models) yielded PPVs of above 82%. Using only data from validated models, three-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + EEG), then structural MRI and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT=2) for an individual with three positive sequential tests, 71-82% (NNT=3) with two positive tests, 12-21% (NNT=11-18) with one positive test, and 1% (NNT=219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multi-stage sequential testing may allow individual risk stratification of CHR individuals and optimise the prediction of psychosis.
UR - http://www.scopus.com/inward/record.url?scp=85018281114&partnerID=8YFLogxK
U2 - 10.1093/schbul/sbw098
DO - 10.1093/schbul/sbw098
M3 - Article
SN - 0586-7614
VL - 43
SP - 375
EP - 388
JO - Schizophrenia Bulletin
JF - Schizophrenia Bulletin
IS - 2
ER -