Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources.

This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center).

Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015.

In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites.

In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

The authors have declared no competing interest.

: All investigators were supported in part by NIMH R01MH118233 (PIs Smoller/Davis). Dr. Smoller is also supported in part by a gift from the Ryan Licht Sang Bipolar Foundation. Dr. Davis is also supported in part by R56MH120736. Dr. Walsh is also supported in part by NIMH R01MH121455 and R01MH116269. Dr. Choi is supported in part by a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation. Funders played no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The methods were performed in accordance with relevant guidelines and regulations and approved by Institutional Review Boards at each participating study site: Vanderbilt University Medical Center, Geisinger Health System, Mass General Brigham.

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Study data including de-identified electronic health records linked to biobanks. However, complete anonymization to prevent inadvertent or intentional reidentification is not possible with granular healthcare data as those used here. Study-related analytic code and trained algorithms will be made available with publication as per the manuscript text.