Model-driven survival prediction after congenital heart surgery.

ABSTRACT:

Objectives

The objective of the study was to improve postoperative risk assessment in congenital heart surgery by developing a machine-learning model based on readily available peri- and postoperative parameters.

Methods

Our bicentric retrospective data analysis from January 2014 to December 2019 of established risk parameters for dismal outcome was used to train and test a model to predict postoperative survival within the first 30 days. The Freiburg training data consisted of 780 procedures; the Heidelberg test data comprised 985 procedures. STAT mortality score, age, aortic cross-clamp time and postoperative lactate values over 24 h were considered.

Results

Our model showed an area under the curve (AUC) of 94.86%, specificity of 89.48% and sensitivity of 85.00%, re

SUBMITTER: Zurn C 

PROVIDER: S-EPMC10493173 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature