Prediction of Combined Symptomatic and Functional Outcome in Patients with Schizophrenia or Schizoaffective Disorder

Abstract

Purpose

An earlier analysis of data from six randomized, active-control studies involving 1449 patients identified five distinct clusters characterized by different combinations of psychiatric and functional outcomes. We explored baseline demographics, disease characteristics, early symptom response, treatment, and adverse events as possible predictors of clusters representing best and worst clinical outcomes.

Methods

At 6-month endpoint in combined treatment groups, good outcome (Cluster A) was associated with good functioning and limited psychopathology. Poor outcome was associated with poor functioning and moderate (Cluster D) or severe (Cluster E) psychopathology. Stepwise logistic regression was used to construct predictive models of cluster membership (N = 1260) for baseline predictors and with 2/4/8 weeks of treatment. Odds ratios were adjusted for study effects.

Results

Cluster A baseline predictors included female gender and higher levels of occupational and psychosocial functioning. Greater improvement across PANSS factors during early treatment also predicted good outcome. Cluster D baseline predictors included earlier onset of illness, older age, pseudoparkinsonism, and worse occupational and psychosocial functioning; subsequent worsening in PANSS depression and positive factor scores and in functioning predicted poor outcome for Cluster D. Predictors of Cluster E included earlier onset of illness, non-olanzapine treatment, and higher scores on the PANSS depression, hostility, and positive factors; subsequent worsening in PANSS disorganization, negative, and positive factor scores was predictive of poor outcome for Cluster E.

Conclusion

Early symptom improvement/worsening was predictive of outcome. Early monitoring of psychiatric symptoms and functioning may lead to better therapeutic decisions based on individual characteristics.