Machine Learning to Predict Executive Function in Adolescents with Repaired d-Transposition of the Great Arteries, Tetralogy of Fallot, and Fontan Palliation.

ABSTRACT:

Objective

To identify predictors of impaired executive function in adolescents after surgical repair of critical congenital heart disease (CHD).

Study design

We analyzed patient factors, medical and surgical history, and family social class from 3 single-center studies of adolescents with d-transposition of the great arteries (d-TGA), tetralogy of Fallot (TOF), and Fontan repair. Machine learning models were developed using recursive partitioning to predict an executive function composite score based on five subtests (population mean 10, SD 3) of the Delis-Kaplan Executive Function System.

Results

The sample included 386 patients (139 d-TGA, 91 TOF, 156 Fontan) of age 15.1 ± 2.1 (mean ± SD) years and an executive function composite score of 8.6 ± 2.4. Family social cl

SUBMITTER: Majeed A 

PROVIDER: S-EPMC12414249 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature