Validation of disease states in schizophrenia: comparison of cluster analysis between US and European populations

BACKGROUND There is controversy as to whether use of statistical clustering methods to identify common disease patterns in schizophrenia identifies patterns generalizable across countries. OBJECTIVE The goal of this study was to compare disease states identified in a published study (Mohr/Lenert, 2004) considering US patients to disease states in a European cohort (EuroSC) considering English, French, and German patients. METHODS Using methods paralleling those in Mohr/Lenert, we conducted a principal component analysis (PCA) on Positive and Negative Syndrome Scale items in the EuroSC data set (n=1,208), followed by k-means cluster analyses and a search for an optimal k. The optimal model structure was compared to Mohr/Lenert by assigning discrete severity levels to each cluster in each factor based on the cluster center. A harmonized model was created and patients were assigned to health states using both approaches; agreement rates in state assignment were then calculated. RESULTS Five factors accounting for 56% of total variance were obtained from PCA. These factors corresponded to positive symptoms (Factor 1), negative symptoms (Factor 2), cognitive impairment (Factor 3), hostility/aggression (Factor 4), and mood disorder (Factor 5) (as in Mohr/Lenert). The optimal number of cluster states was six. The kappa statistic (95% confidence interval) for agreement in state assignment was 0.686 (0.670-0.703). CONCLUSION The patterns of schizophrenia effects identified using clustering in two different data sets were reasonably similar. Results suggest the Mohr/Lenert health state model is potentially generalizable to other populations.