Prediction of postoperative final degree and recurrence of pectus excavatum using machine learning algorithms

Chest wall re-depression after bar removal (BR) in pectus excavatum (PE) is insufficiently investigated. However, it is not easy to investigate chest wall re-depression due to its multifactorial characteristics. Herein, we investigated chest wall re-depression after BR using machine learning algorit...

Full description

Saved in:
Bibliographic Details
Published in:Journal of thoracic disease 2024-01, Vol.16 (1), p.311-320
Main Authors: Hyun, Kwanyong, Kim, Jae Jun, Choi, Won Kyu, Kim, Yoon Ho, Han, Sang Chul
Format: Article
Language:English
Subjects:
Original
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Chest wall re-depression after bar removal (BR) in pectus excavatum (PE) is insufficiently investigated. However, it is not easy to investigate chest wall re-depression due to its multifactorial characteristics. Herein, we investigated chest wall re-depression after BR using machine learning algorithms. To the best of my knowledge, this is the first study of chest wall re-depression after BR using machine learning algorithms. We retrospectively reviewed 199 consecutive subjects who underwent both minimally invasive repair of pectus excavatum (MIRPE) and BR at a single hospital from March 2012 to June 2020. We investigated attributes of chest wall re-depression and risk factors for recurrence after BR, predicted final degree and recurrence of PE after BR, and suggested the optimal age at the time of MIRPE based on recurrence. Data for the chest wall re-depression were analyzed to discover differences according to age group [
ISSN:2072-1439
2077-6624
DOI:10.21037/jtd-23-1430