Objectives: Most bipolar disorder (BD) patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study aims to identify features predictive of the conversion from Major Depressive Disorder (MDD) to BD by leveraging electronic health record (EHR) data from the Clínica San Juan de Dios Manizales in Colombia.
Methods: We employed a multivariable Cox regression model to identify important predictors of conversion from MDD to BD.
Results: Analyzing 15 years of EHR data from 13,607 patients diagnosed with MDD, a total of 1610 (11.8%) transitioned to BD. Predictive features of the conversion to BD included severity of the initial MDD episode, presence of psychosis and hospitalization at first episode, family history of BD, and female gender. Additionally, we observed associations with medication classes (positive associations with prescriptions of mood stabilizers, antipsychotics, and negative associations with antidepressants) and a positive association with suicidality, a feature derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within 5 years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%.
Conclusions: Our study confirms previously identified risk factors identified through registry-based studies (female gender and psychotic depression at the index MDD episode) and identifies novel ones (suicidality extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion and underscore its potential for the identification of novel risk factors, thereby improving early diagnosis.
Keywords: EHR; bipolar disorder; diagnostic conversion; major depressive disorder.
© 2024 The Author(s). Bipolar Disorders published by John Wiley & Sons Ltd.