Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry

Abstract

Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable “risk calculator” models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.

On the Proportion of Patients Who Experience a Prodrome Prior to Psychosis Onset - A Systematic Review and Meta-analysis

“…With the ultimate objective being to identify patients and intervene early in order to maximize clinical benefit, interventions relying on APS-based definitions may overlook opportunities to identify or delay the onset of psychosis. Indeed, APS-specific interventions may have relatively limited effectiveness even with respect to reducing APS symptoms or transition rates for patients at the CHR stage (Davies et al, 2018;Worthington et al, 2021). As such, while APS may be a necessary and important part of an eventual gold-standard prodrome definition, they may not be adequate, especially when taking heterotypic trajectories into account.…”

“…Studies have now demonstrated that help-seeking individuals with these symptoms have an elevated risk of transition to psychosis for up to 10 years (Fusar-Poli et al, 2012;Salazar de Pablo et al, 2021a). These prospective definitions have also assisted in the development of novel service offerings -early intervention clinics, aimed at providing care to patients experiencing CHR states (Salazar de Pablo et al, 2021b much excitement, demonstrating evidence of effectiveness as measured by reductions in duration of untreated psychosis, improvement of symptomatic and functional outcomes, while being cost-effective (Killackey & Yung, 2007;Devoe et al, 2020), though there is more limited evidence for reduction of rates of CHR symptoms and of transition to psychosis (Fusar-Poli et al, 2019;Davies et al, 2018;Worthington & Cannon, 2021).…”

Does recency of prodromal symptom onset or worsening matter for psychosis prediction?

“…It is important to note that CHR-P status, however defined, is an initial 'filter' in ascertaining a risk population for study; the CHR-P cases so enrolled will still differ considerably among each other in individual-level risk. The North American Prodrome Longitudinal Study (NAPLS) risk calculator, which can be used to scale the risk of newly ascertained individuals, is based on a sample meeting SIPS criteria for a CHR-P syndrome (Cannon et al, 2016) and has been validated in multiple independent studies (Worthington & Cannon, 2021).…”

“…It is important to note that CHR‐P status, however defined, is an initial ‘filter’ in ascertaining a risk population for study; the CHR‐P cases so enrolled will still differ considerably among each other in individual‐level risk. The North American Prodrome Longitudinal Study (NAPLS) risk calculator, which can be used to scale the risk of newly ascertained individuals, is based on a sample meeting SIPS criteria for a CHR‐P syndrome (Cannon et al, 2016) and has been validated in multiple independent studies (Worthington & Cannon, 2021). Future work developing and validating individualized risk calculators may be facilitated by use of the PSYCHS and incorporation of particular temporal parameters (e.g., recency of onset or worsening of APS symptoms) either as direct predictors or as moderators of other factors in the risk calculation.…”

Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ): developing tools to enable early intervention in the psychosis high risk state

“…The biomarker data collected by ProNET and PRESCIENT will be analyzed by the DPACC to develop multimodal prediction models and risk calculators by drawing on recent theoretical and methodological advances (e.g., dynamic prediction, probabilis tic multimodal modeling). These models will leverage existing prediction models in the field 9 and guide selection and stratifica tion of CHR participants for future clinical trials based on the pri mary endpoint of interest. For example, stratification can identify a subset of CHR participants who are at higher risk of developing psychosis relative to the rest.…”