Objectives: The objective of this study was to develop and validate an approach to accurately identify incident pediatric neuropsychiatric events (NPEs) requiring hospitalization by using administrative data.
Methods: We performed a cross-sectional, multicenter study of children 5 to 18 years of age hospitalized at two US children's hospitals with an NPE. We developed and evaluated 3 NPE identification algorithms: (1) primary or secondary NPE International Classification of Diseases, 10th Revision diagnosis alone, (2) NPE diagnosis, the NPE was present on admission, and the primary diagnosis was not malignancy- or surgery-related, and (3) identical to algorithm 2 but without requiring the NPE be present on admission. The positive predictive value (PPV) of each algorithm was calculated overall and by diagnosis field (primary or secondary), clinical significance, and NPE subtype.
Results: There were 1098 NPE hospitalizations included in the study. A total of 857 confirmed NPEs were identified for algorithm 1, yielding a PPV of 0.78 (95% confidence interval [CI] 0.76-0.80). Algorithm 2 (n = 846) had an overall PPV of 0.89 (95% CI 0.87-0.91). For algorithm 3 (n = 938), the overall PPV was 0.86 (95% CI 0.83-0.88). PPVs varied by diagnosis order, NPE clinical significance, and subtype. The PPV for critical clinical significance was 0.99 (0.97-0.99) for all 3 algorithms.
Conclusions: We identified a highly accurate method to identify neuropsychiatric adverse events in children and adolescents. The use of these approaches will improve the rigor of future studies of NPE, including the necessary evaluations of medication adverse events, infections, and chronic conditions.
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