Machine learning in a real-world PFO study: analysis of data from multi-centers in China.

ABSTRACT:

Purpose

The association of patent foreman ovale (PFO) and cryptogenic stroke has been studied for years. Although device closure overall decreases the risk for recurrent stroke, treatment effects varied across different studies. In this study, we aimed to detect sub-clusters in post-closure PFO patients and identify potential predictors for adverse outcomes.

Methods

We analyzed patients with embolic stroke of undetermined sources and PFO from 7 centers in China. Machine learning and Cox regression analysis were used.

Results

Using unsupervised hierarchical clustering on principal components, two main clusters were identified and a total of 196 patients were included. The average age was 42.7 (12.37) years and 64.80% (127/196) were female. During a median follow-up of

SUBMITTER: Luo D 

PROVIDER: S-EPMC9694545 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature