Machine learning-based mortality risk assessment in first-episode bipolar disorder: a transdiagnostic external validation study.

ABSTRACT:

Background

Accurate mortality risk prediction could enhance treatment planning in bipolar disorder, where mortality rates rival those of many cancers. Such prognostic tools are lacking in psychiatry, where assessments typically emphasize immediate suicidality while neglecting long-term mortality risks, and their clinical use is debated. We evaluated the recently developed machine learning model MIRACLE-FEP, initially developed for first-episode psychosis, in predicting all-cause mortality in patients with first-episode bipolar disorder (FEBD), hypothesizing that it would provide accurate risk prediction and guide pharmacotherapy decisions.

Methods

We utilized national register-based cohorts of FEBD patients from Sweden (N = 31,013, followed 2006-2021) and Finland (N = 13,956

SUBMITTER: Lieslehto J 

PROVIDER: S-EPMC11874523 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature