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Automated Prediction of Surgical Site Infection Coronary Artery Bypass (CABG) Grafting Surgery

Published online by Cambridge University Press:  02 November 2020

Flávio Souza
Affiliation:
Centro Universitário de Belo Horizonte (UNIBH)
Braulio Couto
Affiliation:
Centro Universitário de Belo Horizonte – UniBH
Felipe Leandro Andrade da Conceição
Affiliation:
Centro Universitário de Belo Horizonte
Gabriel Henrique Silvestre da Silva
Affiliation:
Centro Universitário de Belo Horizonte
Igor Gonçalves Dias
Affiliation:
Centro Universitário de Belo Horizonte
Rafael Vieira Magno Rigueira
Affiliation:
Centro Universitário de Belo Horizonte
Gustavo Maciel Pimenta
Affiliation:
Centro Universitário de Belo Horizonte
Maurilio Martins
Affiliation:
Centro Universitário de Belo Horizontel Julio Cesar Mendes, Centro Universitário de Belo Horizonte
Aline Castro de Almeida
Affiliation:
Centro Universitário de Belo Horizonte
Filipe Batista do Amaral
Affiliation:
Centro Universitário de Belo Horizonte
Guilherme Brangioni Januário
Affiliation:
Centro Universitário de Belo Horizonte
Maria Luiza Neves Caldeira
Affiliation:
Centro Universitário de Belo Horizonte
Miriam Alice Guerra
Affiliation:
Centro Universitário de Belo Horizonte
Rayane Thamires Oliveira Moraes
Affiliation:
Centro Universitário de Belo Horizonte
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Abstract

Background: In 5 hospitals located in Belo Horizonte city (>3,000,000 inhabitants) a focused survey on surgical site infection (SSI) was performed in patients undergoing CABG surgery. We statistically evaluated such incidences to enable study of the prediction power of SSI through pattern recognition algorithms, in this case the multilayer perceptron (MLP) artificial neural networks. Methods: Data were collected between July 2016 and June 2018 on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. We collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which uses the SACIH (Automated Hospital Infection Control System) software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the collected database for use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing and 35% or 25% for validation). They were compared by measuring the AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: From 666 initial data, only 278 were able for analysis. We obtained the following statistics: 9.35% manifested SSIs; length of stay varied from 1 to 119 days, with ~40% staying between 10 and 19 days; 15.1% of the patients died. Regarding the prediction power of SSI, the experiments have a maximum value of 0.713. Conclusions: Despite the considerable loss rate of >50% of the database samples due to the presence of noise, it was possible to have a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some configurations had results equal to 0.5, others reached 0.713, which indicates that the automated SSI monitoring framework for patients undergoing coronary artery bypass grafting surgery is promising. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available at www.sacihweb.com), a mobile application was developed.

Funding: None

Disclosures: None

Type
Poster Presentations
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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