Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T03:11:56.211Z Has data issue: false hasContentIssue false

73 Identification of 24-Month Cognitive Trajectories Among Clinical High Risk for Psychosis (CHR-P) Using Latent Class Mixture Modeling

Published online by Cambridge University Press:  21 December 2023

Ryan M. Guest*
Affiliation:
Department of Psychology, Emory University, Atlanta, GA, USA.
Jean Addington
Affiliation:
Department of Psychiatry, University of Calgary, Calgary, Canada.
Carrie E. Bearden
Affiliation:
Department of Psychiatry and Biobehavioral Sciences and Psychology, University of California Los Angeles, Los Angeles, CA, USA.
Kristin S. Cadenhead
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, CA, USA.
Barbara A. Cornblatt
Affiliation:
Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, NY, USA.
Daniel H. Mathalon
Affiliation:
Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
Diana O. Perkins
Affiliation:
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Ming T. Tsuang
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, CA, USA.
Scott W. Woods
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA. Department of Psychiatry, Yale University, New Haven, CT, USA.
Tyrone D. Cannon
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA.
Matcheri S. Keshavan
Affiliation:
Department of Psychiatry, Beth Israel Deaconess Medical Center at Harvard Medical School, Boston, MA, USA.
William S. Stone
Affiliation:
Department of Psychiatry, Beth Israel Deaconess Medical Center at Harvard Medical School, Boston, MA, USA.
Elaine F. Walker
Affiliation:
Department of Psychology, Emory University, Atlanta, GA, USA.
*
Correspondence: Ryan M. Guest, Emory University (rmguest@emory.edu)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Objective:

Cohort studies demonstrate that people who later develop schizophrenia, on average, present with mild cognitive deficits in childhood and endure a decline in adolescence and adulthood. Yet, tremendous heterogeneity exists during the course of psychotic disorders, including the prodromal period. Individuals identified to be in this period (known as CHR-P) are at heightened risk for developing psychosis (~35%) and begin to exhibit cognitive deficits. Cognitive impairments in CHR-P (as a singular group) appear to be relatively stable or ameliorate over time. A sizeable proportion has been described to decline on measures related to processing speed or verbal learning. The purpose of this analysis is to use data-driven approaches to identify latent subgroups among CHR-P based on cognitive trajectories. This will yield a clearer understanding of the timing and presentation of both general and domain-specific deficits.

Participants and Methods:

Participants included 684 young people at CHR-P (ages 12–35) from the second cohort of the North American Prodromal Longitudinal Study. Performance on the MATRICS Consensus Cognitive Battery (MCCB) and the Wechsler Abbreviated Scale of Intelligence (WASI-I) was assessed at baseline, 12-, and 24-months. Tested MCCB domains include verbal learning, speed of processing, working memory, and reasoning & problem-solving. Sex- and age-based norms were utilized. The Oral Reading subtest on the Wide Range Achievement Test (WRAT4) indexed pre-morbid IQ at baseline. Latent class mixture models were used to identify distinct trajectories of cognitive performance across two years. One- to 5-class solutions were compared to decide the best solution. This determination depended on goodness-of-fit metrics, interpretability of latent trajectories, and proportion of subgroup membership (>5%).

Results:

A one-class solution was found for WASI-I Full-Scale IQ, as people at CHR-P predominantly demonstrated an average IQ that increased gradually over time. For individual domains, one-class solutions also best fit the trajectories for speed of processing, verbal learning, and working memory domains. Two distinct subgroups were identified on one of the executive functioning domains, reasoning and problem-solving (NAB Mazes). The sample divided into unimpaired performance with mild improvement over time (Class I, 74%) and persistent performance two standard deviations below average (Class II, 26%). Between these classes, no significant differences were found for biological sex, age, years of education, or likelihood of conversion to psychosis (OR = 1.68, 95% CI 0.86 to 3.14). Individuals assigned to Class II did demonstrate a lower WASI-I IQ at baseline (96.3 vs. 106.3) and a lower premorbid IQ (100.8 vs. 106.2).

Conclusions:

Youth at CHR-P demonstrate relatively homogeneous trajectories across time in terms of general cognition and most individual domains. In contrast, two distinct subgroups were observed with higher cognitive skills involving planning and foresight, and they notably exist independent of conversion outcome. Overall, these findings replicate and extend results from a recently published latent class analysis that examined 12-month trajectories among CHR-P using a different cognitive battery (Allott et al., 2022). Findings inform which individuals at CHR-P may be most likely to benefit from cognitive remediation and can inform about the substrates of deficits by establishing meaningful subtypes.

Type
Poster Session 09: Psychiatric Disorders | Mood & Anxiety Disorders | Addiction | Social Cognition | Cognitive Neuroscience | Emotional and Social Processing
Copyright
Copyright © INS. Published by Cambridge University Press, 2023