Development and validation of a patient-level model to predict dementia across a network of observational databases.

ABSTRACT:

Background

A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement.

Methods

Logistic regression models were developed across a network

SUBMITTER: John LH 

PROVIDER: S-EPMC11288076 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature