Real-world predictors of relapse in patients with schizophrenia and schizoaffective disorder in a large health system

Abstract Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes and reduce healthcare costs. Prior work has identified a few consistent predictors of relapse in schizophrenia, however, studies to date have been limited to insurance claims data or small patient populations. Thus, this study used a large sample of health systems electronic health record (EHR) data to analyze relationships between patient-level factors and relapse and model a set of factors that can be used to identify the increased prevalence of relapse, a severe and preventable reality of schizophrenia. This retrospective, observational cohort study utilized EHR data extracted from the largest Midwestern U.S. non-profit healthcare system to identify predictors of relapse. The study included patients with a diagnosis of schizophrenia (ICD-10 F20) or schizoaffective disorder (ICD-10 F25) who were treated within the system between October 15, 2016, and December 31, 2021, and received care for at least 12 months. A relapse episode was defined as an emergency room or inpatient encounter with a pre-determined behavioral health-related ICD code. Patients’ baseline characteristics, comorbidities and healthcare utilization were described. Modified log-Poisson regression (i.e. log Poisson regression with a robust variance estimation) analyses were utilized to estimate the prevalence of relapse across patient characteristics, comorbidities and healthcare utilization and to ultimately identify an adjusted model predicting relapse. Among the 8119 unique patients included in the study, 2478 (30.52%) experienced relapse and 5641 (69.48%) experienced no relapse. Patients were primarily male (54.72%), White Non-Hispanic or Latino (54.23%), with Medicare insurance (51.40%), and had baseline diagnoses of substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism disorder (30.66%), hypertension (26.85%), and diabetes (19.08%). Many differences in patient characteristics, baseline comorbidities, and utilization were revealed between patients who relapsed and patients who did not relapse. Through model building, the final adjusted model with all significant predictors of relapse included the following variables: insurance, age, race/ethnicity, substance use diagnosis, extrapyramidal symptoms, number of emergency room encounters, behavioral health inpatient encounters, prior relapses episodes, and long-acting injectable prescriptions written. Prevention of relapse is a priority in schizophrenia care. Challenges related to historical health record data have limited the knowledge of real-world predictors of relapse. This study offers a set of variables that could conceivably be used to construct algorithms or models to proactively monitor demographic, comorbidity, medication, and healthcare utilization parameters which place patients at risk for relapse and to modify approaches to care to avoid future relapse.