Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

ABSTRACT:

Background

Traditional risk stratification tools do not describe the complex principle determinant relationships that exist amongst pre-operative and peri-operative factors and their influence on cardiac surgical outcomes. This paper reports on the use of Bayesian networks to investigate such outcomes.

Methods

Data were prospectively collected from 4776 adult patients undergoing cardiac surgery at a single UK institute between April 2012 and May 2019. Machine learning techniques were used to construct Bayesian networks for four key short-term outcomes including death, stroke and renal failure.

Results

Duration of operation was the most important determinant of death irrespective of EuroSCORE. Duration of cardiopulmonary bypass was the most important determinant of re-

SUBMITTER: Mazhar K 

PROVIDER: S-EPMC9903419 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature