Looking for childhood-onset schizophrenia: diagnostic algorithms for classifying children and adolescents with psychosis.

Abstract

Among children <13 years of age with persistent psychosis and contemporaneous decline in functioning, it is often difficult to determine if the diagnosis of childhood onset schizophrenia (COS) is warranted. Despite decades of experience, we have up to a 44% false positive screening diagnosis rate among patients identified as having probable or possible COS; final diagnoses are made following inpatient hospitalization and medication washout. Because our lengthy medication-free observation is not feasible in clinical practice, we constructed diagnostic classifiers using screening data to assist clinicians practicing in the community or academic centers. We used cross-validation, logistic regression, receiver operating characteristic (ROC) analysis, and random forest to determine the best algorithm for classifying COS (n=85) versus histories of psychosis and impaired functioning in children and adolescents who, at screening, were considered likely to have COS, but who did not meet diagnostic criteria for schizophrenia after medication washout and inpatient observation (n=53). We used demographics, clinical history measures, intelligence quotient (IQ) and screening rating scales, and number of typical and atypical antipsychotic medications as our predictors. Logistic regression models using nine, four, and two predictors performed well with positive predictive values>90%, overall accuracy>77%, and areas under the curve (AUCs)>86%. COS can be distinguished from alternate disorders with psychosis in children and adolescents; greater levels of positive and negative symptoms and lower levels of depression combine to make COS more likely. We include a worksheet so that clinicians in the community and academic centers can predict the probability that a young patient may be schizophrenic, using only two ratings.