Predictors of admission in first-episode psychosis: developing a risk adjustment model for service comparisons.

OBJECTIVE: The aim of this study was to develop a risk adjustment model based on hospital admissions that would enable comparison between services for patients with a first episode of psychosis.
METHODS: Candidate predictor variables for hospital admission were identified in a literature review, from which an expert panel selected 12 potential risk adjustment variables by using a structured process, the Template for Risk Adjustment Information Transfer. Multivariable logistic regression modeling with the 12 variables was used to develop models in one cohort of first-episode psychosis patients (N=297); these models were validated with data from a second cohort (N=309). The C statistic, a measure of model discrimination, was calculated to assess model performance.
RESULTS: In the data from the development sample, prior hospitalization was the only significant predictor of hospital admissions within one year of enrollment in the first-episode psychosis program (odds ratio [OR]=1.88, p=.05). Hospital admissions after two and three years from admission to the program were significantly associated with higher levels of initial positive symptoms (OR=1.07, p=.02; OR=1.06, p=.02, respectively), and prior hospitalizations (OR=2.72, p=.001; OR=3.34, p<.001, respectively). The logistic models performed well, with C statistics ranging from .72 to .74 for the three outcomes, where a value of 1.0 implies perfect model discrimination. In the validation data the C statistics were slightly lower, ranging from .67 to .72.
CONCLUSIONS: According to the C statistic estimates, the model developed provided good discrimination and was relatively robust in predicting hospitalization of first-episode psychosis patients.