Can We Improve the Prediction of Early Onset Mania and Hypomania in the Community?

BACKGROUND: There is broad agreement that the onset of bipolar disorders (BD) can be predicted by using combined estimates of familial, genetic and clinical risk. However, there is a lack of consensus about the operationalisation of different risk attributes (e.g., symptoms vs. sub-threshold syndromes; disorder-specific polygenic risk scores [PRS] vs. multiple-disorder PRS dimensions) and their utility for predicting bipolar type 1 (BD-I) and 2 (BD-II). Likewise, analyses often fail to consider the optimal model for predicting outcomes where true cases will be in the minority. METHODOLOGY: Proof of concept study employing an ensemble machine learning approach (Boosting) to develop models for classifying BD cases vs. non-cases using different combinations of risk attributes extracted from a database from a prospective longitudinal follow-up of twin and non-twin siblings in the peak age range for onset of major mental disorders. RESULTS: Of 1473 participants (mean age 26.3; female = 866), 104 developed BD-I (n = 30) or BD-II (n = 74). The best performing Boosting classification had an overall area under the receiver operating curve (AUROC) of 85.1% (95% Confidence Intervals: 80%, 88%); correctly identifying 86.7% BD cases. Variables with greatest relative influence were, in rank order: depressive symptoms, psychotic symptoms, a BD-Schizophrenia PRS dimension, hypomanic symptoms, and family history of BD. The model accurately classified 89% of manic cases but only 68% of hypomanic cases. CONCLUSIONS: Improving the accurate prediction of BD onset would benefit from greater consensus regarding the operationalisation of known risk attributes and selection of analytic models that consider sample imbalances.