The heterogeneity of antipsychotic response in the treatment of schizophrenia

Abstract

Background: Schizophrenia is a heterogeneous disorder in terms of patient response to antipsychotic treatment. Understanding the heterogeneity of treatment response may help to guide treatment decisions. This study was undertaken to capture inherent patterns of response to antipsychotic treatment in patients with schizophrenia, characterize the subgroups of patients with similar courses of response, and examine illness characteristics at baseline as possible predictors of response.

Method: Growth mixture modeling (GMM) was applied to data from a randomized, double-blind, 12-week study of 628 patients with schizophrenia or schizo-affective disorder treated with risperidone or olanzapine.

Results: Four distinct response trajectories based on Positive and Negative Syndrome Scale (PANSS) total score over 12 weeks were identified: Class 1 (420 patients, 80.6%) with moderate average baseline PANSS total score showing gradual symptom improvement; Class 2 (65 patients, 12.5%) showing rapid symptom improvement; Class 3 (24 patients, 4.6%) with high average baseline PANSS total score showing gradual symptom improvement; and Class 4 (12 patients, 2.3%) showing unsustained symptom improvement. Latent class membership of early responders (ER) and early non-responders (ENR) was determined based on 20% symptom improvement criteria at 2 weeks and ultimate responders (UR) and ultimate non-responders (UNR) based on 40% symptom improvement criteria at 12 weeks. Baseline factors with potential influence on latent class membership were identified.

Conclusions: This study identified four distinct treatment response patterns with predominant representation of responders or non-responders to treatment in these classes. This heterogeneity may represent discrete endophenotypes of response to treatment with different etiologic underpinnings.

Fig. 1

Growth mixture modeling (GMM) analysis of the Positive and Negative Syndrome Scale (PANSS) total score reveals four distinct latent classes of treatment response. ER, early responders (⩾20% improvement in PANSS total at week 2); ENR, early non-responders.

Fig. 2

The observed trajectories of individuals classified into the four latent classes are shown as broken lines and the solid line represents the model-estimated means shown in Fig. 1. PANSS, Positive and Negative Syndrome Scale.

Fig. 3

Treatment response characteristics of patients in the four classes. ER, early responders (⩾20% improvement in PANSS total at week 2); ENR, early non-responders; UR, ultimate responders (⩾40% improvement in PANSS total at 12 weeks); UNR, ultimate non-responders; PANSS, Positive and Negative Syndrome Scale.