Abstract
Effective early detection of impending relapse may offer opportunities for early interventions to prevent full relapse in schizophrenia patients. Previously reported early warning signs were not consistently validated by prospective studies. It remains unclear which symptoms are most predictive of relapse. To prioritize the symptoms to be captured by periodic self-report in technology-enabled remote assessment solutions for monitoring symptoms and detecting relapse early, we analyzed data from three relapse-prevention studies to identify individual items of the Positive and Negative Syndrome Scale (PANSS) that changed the most prior to relapse and to understand exactly when these symptoms manifested. Relapse was defined by a composite endpoint: hospitalization, suicidal/homicidal ideation, violent behavior, a 25% increase in the PANSS total score, or a significant increase in at least one of several pre-specified PANSS items. Longitudinal mixed effect models were applied to model the trajectories of individual PANSS items before relapse. Among 267 relapsed patients, the PANSS items that increased the most at relapse from randomization did not differ much by different relapse reasons or medications. A subset of seven PANSS items, including delusions, suspiciousness, hallucinations, anxiety, excitement, tension, and conceptual disorganization, had on average > 1-point of increase at relapse. The trajectories of these items suggested these items started to increase 7-10 days before relapse and reached on average 1-point of increase 0.3 ~ 1.2 days before relapse. Our results indicated that a subset of PANSS items could be leveraged to develop remote assessment solutions for monitoring symptoms and detecting relapse early in schizophrenia patients.