A hybrid model for predicting response to risperidone after first-episode psychosis.

OBJECTIVE

Patient response to antipsychotic drugs varies and may be related to clinical and genetic heterogeneity. This study aimed to determine the performance of clinical, genetic, and hybrid models to predict the response of patients in first-episode psychosis (FEP) to the antipsychotic risperidone.

METHODS

We evaluated 141 antipsychotic-naive FEP patients before and after 10 weeks of risperidone treatment. Patients who had a response rate equal to or higher than 50% on the Positive and Negative Syndrome Scale (PANSS) were considered responders (n=72; 51%). Analyses were performed using a support vector machine (SVM), k-nearest neighbors (kNN), and random forests (RF). Clinical and genetic (with single-nucleotide variants [SNVs]) models were created separately. Hybrid models (clinical + genetic factors) with and without feature selection were created.

RESULTS

Clinical models presented greater balanced accuracy (63.3%; CI 0.46-0.69) with the SVM algorithm than the genetic models (balanced accuracy: 58.5% [CI 0.41-0.76], kNN algorithm). The hybrid model, which included duration of untreated psychosis, Clinical Global Impression-Severity (CGI-S) scale scores, age, cannabis use, and 406 SNVs, showed the best performance (balanced accuracy: 72.9% [CI 0.62-0.84], RF algorithm).

CONCLUSION

A hybrid model including clinical and genetic predictors can enhance prediction of response to antipsychotic treatment.