Meeting Abstract
Details
Citation
Lapp L, Young D, Kavanagh K, Bouamrane M & Schraag S (2018) P06-3 - Using machine learning for predicting severe postoperative complications after cardiac surgery. Journal of Cardiothoracic and Vascular Anesthesia, 32 (Supplement 1), pp. S84-S85. https://doi.org/10.1053/j.jvca.2018.08.156
Abstract
Introduction:
Complications after cardiac surgery are becoming
more prevalent, being a major contributing factor in patients’
quality of life after surgery, delayed discharges, and resource
usage, particularly due to aging population, all of which can be improved by optimising perioperative medicine patient care pathways, using data analytics and predictive modelling.
Method:
All patients in Golden Jubilee National Hospital
undergoing cardiac surgery between 1 st April 2012 and 31st
March 2016, reported in clinical audit dataset CaTHI, were
analysed. The database included preoperative variables and
outcomes such as death, hospital length of stay, and if patient
had complications. Three machine learning methods were explored in order to develop a predictive model for severe postoperative complications after coronary artery bypass graft (CABG) and/or valve
surgery. These methods were random forest (RF), naïve Bayes
(NB) and support vector machines (SVM). The performance
was assessed using receiver operating characteristic (ROC)
curves, and was compared with our logistic regression (LR)
model developed in a previous study .
Results:
Out of 3700 records, 59.65% of the patients had a
CABG surgery, 26.49% had valve surgery, and 13.86% had a
combined CABG and valve surgery. Of all admissions, 4.95%
had severe postoperative complications. Twenty-five preoperative
variables were analysed to find their association with severe postoperative complications, including patient characteristics,
comorbidities and information about surgery.
RF had the best discriminative ability (AUC ¼ 0.717),
compared to LR, NB and SVM (Graph 1). LR and SVM
had by far the highest sensitivity (86.9% and 82.0%, respec-
tively), compared to RF and NB. RF the highest specificity
(77.2%), followed by NB, LR and SVM.
Discussion:
Overall, RF model has the highest discriminative
ability compared to other models. A predictive model could
help identifying patients who will potentially have severe
complications. This study is a starting point for developing a
decision support tool for use in clinical practice.
Keywords
Predictive modelling; Machine Learning; Cardiac surgery
Journal
Journal of Cardiothoracic and Vascular Anesthesia: Volume 32, Issue Supplement 1
Status | Published |
---|---|
Funders | NHS Greater Glasgow & Clyde |
Publication date | 31/08/2018 |
Publication date online | 01/08/2018 |
Date accepted by journal | 14/05/2018 |
Publisher | Elsevier BV |
ISSN | 1053-0770 |
eISSN | 1532-8422 |
People (1)
Professor Matt-Mouley Bouamrane
Professor in Health/Social Informatics, Computing Science