Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

ABSTRACT:

Background

Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited.

Method

We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30

SUBMITTER: Hayes JF 

PROVIDER: S-EPMC11485101 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature