Abstract
Background
Primary prevention in Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of the field, which is mostly attributed to its epidemiological weakness. This underlies the limited identification power for at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. One potential avenue for improving identification of individuals at risk for psychosis is a Psychosis Polyrisk Score (PPS) integrating genetic and non-genetic risk and protective factors for psychosis. The PPS hinges on recent findings that risk enrichment in CHR-P samples is accounted for by the accumulation of non-genetic factors e.g. parental and sociodemographic risk factors, perinatal risk factors, later risk factors, and antecedents.
Methods
A prototype of the PPS has been developed encompassing 26 non-genetic risk and protective factors, utilising Relative Risks (RR) from an umbrella review of risk and protective factors for psychosis onset in the general population. This was combined with prevalence data to ensure positive scores indicated increased psychosis risk and negative scores indicated decreased psychosis risk. To pilot this, patients referred for a CHR-P assessment (n=15) and healthy controls (n=66) were recruited and assessed with the PPS. Additionally, to investigate the range and distribution of these scores in the general population, 10,000,000 permutations were run utilising prevalence data to produce a simulated dataset.
Results
In the simulated general population data, scores ranged from -15 (least risk, equivalent RR = 0.03) to 39.5 (highest risk, RR = 8912.51). 50% of individuals had an RR < 1 (PPS < 0), 26.7% of individuals had an RR > 3 (PPS > 5), and 2.7% RR > 30 (PPS > 15). Patients referred for a CHR-P assessment had higher PPS scores (median=9, IQR=12.75) than healthy controls (median=-1.75, IQR=8.875). PPS scores in the simulated general population dataset (median=0, IQR=9.5) were similarly lower than patients.
Discussion
The PPS has potential for improving identification of individuals at risk for psychosis. Its distribution in a simulated general population is reflective of expected psychosis risk, with the vast majority of people not being at-risk and very few being at high risk. In addition to supplementing current assessments for CHR-P, this could be implemented at an earlier stage to stratify individuals based on psychosis risk and inform prognoses and clinical decision-making. This promise warrants further research to ascertain its prognostic accuracy and optimal thresholds for clinical intervention.
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