Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Abstract

Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized 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 modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-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, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%-82% (NNT = 3) with 2 positive tests, 12%-21% (NNT = 11-18) with 1 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. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.

Keywords: clinical high-risk; early interventions; prediction; prognostic accuracy; psychosis; treatment prognosis.

Fig. 1.

PRISMA flow chart.

Fig. 2.

Probabilistic risk assessment diagram illustrating the 3-stage sequential testing of the best combination of complementary tests identified by our simulation analyses: step 1: EEG + clinical test, step 2: structural MRI test, and step 3: blood markers test. The x-axis shows the 3 sequential tests following the initial clinical high-risk assessment and the y-axis the probability of transition to psychosis during 36 months of follow-up, before and after knowing the results of each test. Each bifurcation in the plot represents the update in the probability of transition to psychosis after knowing that the test yielded a positive result (ascending solid line) or after knowing that the test yielded a negative result (descending dashed line). The color of the lines reflects the level of risk for psychosis as previously suggested: high (in red) when the probability of transition to psychosis (PT) was >80%, medium when PT was 20%–80% and low (in green) when PT was <20%; we further subdivided medium in medium-high (in orange, when PT was between 70% and 80%) and medium-low (in brown, when PT was 20%–30%). The diagram also illustrates the number needed to treat (NNT) at each node.