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Risk stratification tool for all surgical site infections after coronary artery bypass grafting

Published online by Cambridge University Press:  03 September 2020

Giuseppe Gatti Open the ORCID record for Giuseppe Gatti [Opens in a new window] ,
Antonio Fiore ,
Alessandro Ceschia ,
Fiona Ecarnot ,
Rim Chaara ,
Roberto Luzzati ,
Thierry Folliguet ,
Sidney Chocron ,
Aniello Pappalardo  and
Andrea Perrotti
Giuseppe Gatti*
Affiliation:
Cardio-Thoracic and Vascular Department, Trieste University Hospital, Trieste, Italy
Antonio Fiore
Affiliation:
Department of Cardio-Thoracic Surgery, Henri-Mondor University Hospital, Créteil, France
Alessandro Ceschia
Affiliation:
Cardio-Thoracic and Vascular Department, Trieste University Hospital, Trieste, Italy
Fiona Ecarnot
Affiliation:
Department of Thoracic and Cardiovascular Surgery, Jean Minjoz University Hospital, Besançon, France
Rim Chaara
Affiliation:
Department of Cardio-Thoracic Surgery, Henri-Mondor University Hospital, Créteil, France
Roberto Luzzati
Affiliation:
Department of Infectious Diseases, Trieste University Hospital, Trieste, Italy
Thierry Folliguet
Affiliation:
Department of Cardio-Thoracic Surgery, Henri-Mondor University Hospital, Créteil, France
Sidney Chocron
Affiliation:
Department of Thoracic and Cardiovascular Surgery, Jean Minjoz University Hospital, Besançon, France
Aniello Pappalardo
Affiliation:
Cardio-Thoracic and Vascular Department, Trieste University Hospital, Trieste, Italy
Andrea Perrotti
Affiliation:
Department of Thoracic and Cardiovascular Surgery, Jean Minjoz University Hospital, Besançon, France
*
Author for correspondence: Dr Giuseppe Gatti, E-mail: gius.gatti@gmail.com
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Abstract

Objective:

To develop a risk score for surgical site infections (SSIs) after coronary artery bypass grafting (CABG).

Design:

Retrospective study.

Setting:

University hospital.

Patients:

A derivation sample of 7,090 consecutive isolated or combined CABG patients and 2 validation samples (2,660 total patients).

Methods:

Predictors of SSIs were identified by multivariable analyses from the derivation sample, and a risk stratification tool (additive and logistic) for all SSIs after CABG (acronym, ASSIST) was created. Accuracy of prediction was evaluated with C-statistic and compared 1:1 (using the Hanley-McNeil method) with most relevant risk scores for SSIs after CABG. Both internal (1,000 bootstrap replications) and external validation were performed.

Results:

SSIs occurred in 724 (10.2%) cases and 2 models of ASSIST were created, including either baseline patient characteristics alone or combined with other perioperative factors. Female gender, body mass index >29.3 kg/m2, diabetes, chronic obstructive pulmonary disease, extracardiac arteriopathy, angina at rest, and nonelective surgical priority were predictors of SSIs common to both models, which outperformed (P < .0001) 6 specific risk scores (10 models) for SSIs after CABG. Although ASSIST performed differently in the 2 validation samples, in both, as well as in the derivation data set, the combined model outweighed (albeit not always significantly) the preoperative-only model, both for additive and logistic ASSIST.

Conclusions:

In the derivation data set, ASSIST outperformed specific risk scores in predicting SSIs after CABG. The combined model had a higher accuracy of prediction than the preoperative-only model both in the derivation and validation samples. Additive and logistic ASSIST showed equivalent performance.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 42 , Issue 2 , February 2021 , pp. 182 - 193
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Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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