Identifying and characterizing treatment-resistant schizophrenia in observational database studies

Area of Science:

• Psychiatry
• Health Informatics
• Epidemiology

Background:

• Treatment-resistant schizophrenia (TRS) presents a significant clinical challenge, defined by inadequate response to standard antipsychotic treatments.
• Accurate identification of TRS patients is crucial for understanding disease burden and optimizing care pathways.

Purpose of the Study:

• To develop and validate an algorithm for identifying patients with treatment-resistant schizophrenia (TRS) within large-scale healthcare claims datasets.
• To compare the sensitivity of a newly developed registry algorithm against existing criteria for TRS identification.

Main Methods:

• Patients diagnosed with schizophrenia (SZ) aged 13 years and older were identified in Swedish and US claims datasets.
• The registry algorithm and other criteria (clozapine prescription, published methods) were used to identify TRS patients.
• Sensitivity of the registry algorithm was assessed, and patients with and without TRS were compared for psychiatric/hospital burden and Global Assessment of Functioning (GAF) scores.

Main Results:

• The registry algorithm demonstrated higher sensitivity in identifying TRS patients, particularly those with multiple inpatient stays and psychiatric comorbidities.
• Swedish registry TRS patients diagnosed before age 45 showed significantly lower GAF scores and earlier disease onset compared to non-TRS patients.
• Patients identified as TRS generally exhibited a greater psychiatric and hospital burden across all algorithms.

Conclusions:

• The developed registry algorithm is a more sensitive tool for identifying patients with treatment-resistant schizophrenia (TRS).
• This enhanced sensitivity allows for better characterization of the burden associated with TRS, including psychiatric and hospital utilization.