A Nomogram for Predicting Long Length of Stay in The Intensive Care Unit in Patients Undergoing CABG: Results From the Multicenter E-CABG Registry.
cardiac surgery
coronary artery bypass graft
intensive care unit
length of stay
nomogram
Journal
Journal of cardiothoracic and vascular anesthesia
ISSN: 1532-8422
Titre abrégé: J Cardiothorac Vasc Anesth
Pays: United States
ID NLM: 9110208
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
02
04
2020
revised:
03
06
2020
accepted:
04
06
2020
pubmed:
6
7
2020
medline:
28
4
2021
entrez:
5
7
2020
Statut:
ppublish
Résumé
Many papers evaluated predictive factors for prolonged intensive care unit (ICU) stay after cardiac surgery, but efforts in translating those models in practical clinical tools is lacking. The aim of this study was to build a new nomogram score and test its calibration and discrimination power for predicting a long length of stay in the ICU among patients undergoing coronary artery bypass graft surgery (CABG). Retrospective analysis of an international registry. Multicentric. Based on the european multicenter study on coronary artery bypass grafting (E-CABG) registry (NCT02319083), a total of 7,352 consecutive patients who underwent isolated CABG were analyzed. A "long length of stay" in the ICU was considered when equal to or more than 3 days. Predictive factors were analyzed through a multivariate logistic regression model that was used for the nomogram. Long length of ICU stay was observed in 2,665 patients (36.2%). Ten independent variables were included in the final regression model: the SYNTAX score class critical preoperative state, left ventricular ejection fraction class, angina at rest, poor mobility, recent potent antiplatelet use, estimated glomerular filtration rate class, body mass index, sex, and age. Based on this 10-risk factors logistic regression model, a nomogram has been designed. The authors defined a nomogram model that can provide an individual prediction of long length of ICU stay in cardiovascular surgical patients undergoing CABG. This type of model would allow an early recognition of high-risk patients who might receive different preoperative and postoperative treatments to improve outcomes.
Identifiants
pubmed: 32620494
pii: S1053-0770(20)30521-8
doi: 10.1053/j.jvca.2020.06.015
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
2951-2961Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.