Author:
Deo Anthony J.,Castro Victor M.,Baker Ashley,Carroll Devon,Gonzalez-Heydrich Joseph,Henderson David C.,Holt Daphne J.,Hook Kimberly,Karmacharya Rakesh,Roffman Joshua L.,Madsen Emily M.,Song Eugene,Adams William G.,Camacho Luisa,Gasman Sarah,Gibbs Jada S.,Fortgang Rebecca G.,Kennedy Chris J.,Lozinski Galina,Perez Daisy C.,Wilson Marina,Reis Ben Y.,Smoller Jordan W.
Abstract
AbstractBackground and HypothesisEarly detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.Study DesignUsing EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings.Study ResultsPPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children’s Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08–0.62).ConclusionsWe found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.
Publisher
Cold Spring Harbor Laboratory