A study in Translational Psychiatry now suggests that using a multimodal diagnostic approach that combines a person’s historical risk factors (such as family history of psychosis), clinical assessments (such as Positive and Negative Syndrome Scale), and blood biomarkers (including measures of oxidative stress and fatty acids) may be able to better predict UHR patients most likely to develop psychosis.
In an assessment of 40 people classified as UHR, researchers who used this multimodal approach were able to identify eight of the 11 people who transitioned to psychosis within one year; they also diagnosed one false positive among the 29 who did not transition. This yielded an accuracy of 70% compared with 28% identified by standard UHR criteria.
The authors also noted that their model classified 77% of patients as low or high risk using only the historical and clinical information.
“A staged approach to risk assessment would then be the most efficient, using fatty-acid markers only when the probability following history and clinical assessment is between 0.1 and 0.9, that is, 23% of participants in the current study,” the authors wrote. “Extending this staged approach, resource-intensive neuroimaging or electrophysiology could be reserved for cases that remain at intermediate risk based on clinical and blood biomarker assessments.”
For more on ongoing efforts to accurately identify people at highest risk of developing psychosis, see the American Journal of Psychiatry articles “An Individualized Risk Calculator for Research in Prodromal Psychosis” and “Personalized Prediction of Psychosis: External Validation of the NAPLS-2 Psychosis Risk Calculator With the EDIPPP Project,” and the Psychiatric News article “More Than Words: Automated Speech Analysis May Offer Clinical Insight.”