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Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation

Abstract

We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications. The main outcome was at least one diagnostic code reflective of a bipolar disorder diagnosis within 3 months of index antidepressant prescription. Logistic regression and random forests using diagnostic and procedure codes as well as sociodemographic features were used to predict this outcome, with discrimination and calibration assessed in a held-out test set and then a second academic medical center. Among 67,807 individuals who received at least one antidepressant medication, 925 (1.36%) subsequently received a diagnosis of bipolar disorder within 3 months. Models incorporating coded diagnoses and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80). Standard supervised machine learning methods enabled development of discriminative and transferable models to predict transition to bipolar disorder. With further validation, these scores may enable physicians to more precisely calibrate follow-up intensity for high-risk patients after antidepressant initiation.

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Fig. 1: Bipolar rates among all index prescriptions between 2008 and 2017 for different antidepressant categories.
Fig. 2: Area under the curve (AUC) in test set for the logistic regression classifier (left) and random forest classifier (right) for Site a and Site b.
Fig. 3: Lift histogram for the random forest classifier (1st row) and logistic regression classifier (2nd row) in Site a (1st column) and Site b (2nd column) for a single split.
Fig. 4: Positive predictive values (PPV) versus negative predictive values (NPV) for the random forest classifier (1st row) and logistic regression classifier (2nd row) in Site a (1st column) and Site b (2nd column) for a single split.

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Acknowledgements

The authors thank Victor Castro for assistance with dataset generation.

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MFP: Analyzed data, interpreted results, and assisted in drafting manuscript. MH: Interpreted results and edited manuscript. THM: Interpreted results and edited manuscript. SAB: Assisted in drafting manuscript. FD-V: Oversaw analysis and edited manuscript. RHP: Conceived and drafted manuscript.

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Correspondence to Finale Doshi-Velez or Roy H. Perlis.

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Pradier, M.F., Hughes, M.C., McCoy, T.H. et al. Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation. Neuropsychopharmacol. 46, 455–461 (2021). https://doi.org/10.1038/s41386-020-00838-x

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