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Abstract
Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD’s timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five “training” US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model’s area under the curve (AUC) varied 0.633–0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570–0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.
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1 University of New Mexico Health Sciences Center, Department of Internal Medicine, Center for Global Health, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502)
2 Janssen Research and Development, Raritan, USA (GRID:grid.497530.c) (ISNI:0000 0004 0389 4927)
3 Vanderbilt University, Department of Biomedical Informatics, Department of Medicine, Department of Biostatistics, Nashville, USA (GRID:grid.152326.1) (ISNI:0000 0001 2264 7217); Tennessee Valley Healthcare System VA, Nashville, USA (GRID:grid.152326.1)
4 Veterans Affairs Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA (GRID:grid.280807.5) (ISNI:0000 0000 9555 3716); University of Utah, Department of Internal Medicine, Salt Lake City, USA (GRID:grid.223827.e) (ISNI:0000 0001 2193 0096)
5 Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
6 Stanford University, Stanford Center for Biomedical Informatics Research, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
7 IQVIA, Real World Solutions, Brighton, UK (GRID:grid.482783.2)
8 IQVIA, Real World Solutions, Cambridge, USA (GRID:grid.418848.9) (ISNI:0000 0004 0458 4007)
9 Ajou University School of Medicine, Department of Psychiatry, Suwon, Republic of Korea (GRID:grid.251916.8) (ISNI:0000 0004 0532 3933)
10 Ajou University School of Medicine, Department of Biomedical Informatics, Suwon, Republic of Korea (GRID:grid.251916.8) (ISNI:0000 0004 0532 3933)
11 Janssen Research and Development, Raritan, USA (GRID:grid.497530.c) (ISNI:0000 0004 0389 4927); Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
12 University of New Mexico Health Sciences Center, Department of Internal Medicine, Center for Global Health, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502); University of New Mexico Health Sciences Center, Department of Internal Medicine, Center for Global Health, Division of Translational Informatics, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502)