Format

Send to

Choose Destination
PLoS One. 2017 Jan 6;12(1):e0169772. doi: 10.1371/journal.pone.0169772. eCollection 2017.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

Author information

1
Réanimation Polyvalente, Centre Hospitalier Universitaire Félix Guyon, Saint-Denis, France.
2
Département d'Anesthésie Réanimation, APHP, CHU Bichat-Claude Bernard, Paris, France.
3
Département d'Anesthésie Réanimation, Institut Mutualiste Montsouris, 42 boulevard Jourdan, Paris, France.
4
Université Denis Diderot, PRESS Sorbonne Cité, Paris, France.
5
Unité de Soutien Méthodologique, CHU de La Réunion, Saint-Denis, France.
6
INSERM, CIC 1410, Saint-Pierre, France.

Abstract

BACKGROUND:

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models.

METHODS AND FINDING:

We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.

CONCLUSIONS:

According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

PMID:
28060903
PMCID:
PMC5218502
DOI:
10.1371/journal.pone.0169772
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center