Optimizing Early Prediction for Antipsychotic Response in Schizophrenia

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Abstract
Objective: Researchers, by studying first-generation antipsychotics, have established an early prediction model, which had a favorable specificity but a low sensitivity. This study aims to optimize early prediction of treatment response for schizophrenia using a novel statistic method that can be done even under the Microsoft Excel system of a personal computer. Methods: One hundred twenty-three inpatients with acutely exacerbated schizophrenia were given optimal therapy of risperidone, a commonly used second-generation antipsychotic agent. Response was defined as a reduction of 20% or more in the Positive and Negative Syndrome Scale total score. We applied the generalized estimating equation method's logistic regression to establish an early prediction model based on the treatment results of the first and the second weeks. Results: The proposed method correctly predicted nonresponse at 4 and 6 weeks in 80.8% and 81.8% of the patients, respectively. The method also identified responder at 4 and 6 weeks in 80.0% and 82.8%, respectively. The predictive powers (or correct prediction rates) at 4 and 6 weeks were 80.3% and 82.4%, respectively. In addition, the results based on the responses in Positive and Negative Syndrome Scale scores were slightly better than those in Brief Psychiatric Rating Scale scores. Conclusions: Using the first 2 weeks' treatment results to predict the fourth or sixth week's treatment response is acceptable in terms of specificity, sensitivity, and predictive power. Further studies are needed. Moreover, whether this model could be applied to establish a prediction system for other psychotropics, such as antidepressants, also deserves research.