Longitudinal trajectory of early functional recovery in patients with first episode psychosis

Abstract

Background: There is a large variability in the recovery trajectory and outcome of first episode of psychosis [FEP] patients. To date, individuals' outcome trajectories at early stage of illness and potential risk factors associated with a poor outcome trajectory are largely unknown. This study aims to apply three separate predictors (positive symptoms, negative symptoms, and soft neurological signs) to identify homogeneous function outcome trajectories in patients with FEP using objective data-driven methods, and to explore the potential risk /protective factors associated with each trajectory. Methods: A total of 369 first episode patients (93% antipsychotic naive) were included in the baseline assessments and followed-up at 4-8 weeks, 6 months, and 1 year. K means cluster modelling for longitudinal data (kml3d) was used to identify distinct, homogeneous clusters of functional outcome trajectories. Patients with at least 3 assessments were included in the trajectory analyses (N=129). The Scale for the Assessment of Negative Symptoms (SANS), Scale for the Assessment of Positive Symptoms (SAPS), and Neurological examination abnormalities (NEA) were used as predictors against Global Assessment of Functioning Scale (GAF). Results: In each of the three predictor models, four distinct functional outcome trajectories emerged: "Poor", "Intermediate", "Good" and "Catch-up". Individuals with male gender; ethnic minority status; low premorbid adjustment; low executive function/IQ, low SES, personality disorder, substance use history may be risk factors for poor recovery.

Exploring Risk and Resilient Profiles for Functional Impairment and Baseline Predictors in a 2-Year Follow-Up First-Episode Psychosis Cohort Using Latent Class Growth Analysis

“…Statistical methods like latent class growth analysis (LCGA) can help to provide a more accurate picture of the heterogeneous course in psychosocial functioning that can be observed following FEP, as it allows considering different outcomes of the same characteristic simultaneously [ 12 , 13 ]. To our knowledge, only few studies so far have applied these statistical techniques to assess functional outcomes in FEP samples [ 14 , 15 , 16 ], and none of them has considered simultaneously sociodemographic variables, clinical features and an extensive set of cognitive domains, all of them previously related to poor functional outcomes [ 17 ]. Therefore, our main aim was to identify different trajectories of functional impairment in the 24-month follow-up of a FEP cohort and to assess putative predictors of these diverse trajectories, with a special focus on resilient trajectories.…”

Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns

“…The combination of clustering techniques and multi-channel MMN activity employed in the current study facilitates the identification of neurobiological homogeneous subgroups. In two prior studies, we have used K-means analyses and identified distinct “Bio-classes” ( 38 ) among patients and controls and unique functional trajectories ( 4 ) among FEP patients that do not respect clinical diagnosis boundaries. Within each class, individuals shared a similar neurobiological profile or functional outcome trajectories that uniquely distinguished among the groups.…”

“…Early intervention can play a substantial role in improving long-term outcomes, although there is a large variability in treatment responsiveness in the first episode of psychosis (FEP) patients ( 3 ). FEP patients demonstrate a wide range of cognitive and neurophysiological impairments and are considerably heterogeneous in the functional outcome trajectories ( 4 – 6 ). Progress is undoubtedly hampered by considerable biological and clinical heterogeneity across FEP: effective treatments are unlikely to advance substantially until disease mechanisms are better understood, and biologically-based objective markers are available to tag the cardinal dysfunction, not the diagnoses or symptoms.…”

The Core Deficit of “Classical” Schizophrenia Cuts Across the Psychosis Spectrum

“…A recent longitudinal study using a data-driven machine learning unsupervised approach has identified four homogeneous early function outcome trajectories in patients with first episode of psychosis. 30 Longitudinal data-driven trajectory analysis is useful because it looks beyond conventional diagnostic categories to better understand heterogeneity in the longitudinal course of psychosis syndromes and provide insight into clinically meaningful subgroups of patients. 31 Such a strategy may likely facilitate personalized approach to prediction and treatment.…”