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Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries

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 Horizonte
Julio Cesar Mendes
Affiliation:
Centro Universitário de Belo Horizonte
Ana Flavia Viana Quintão
Affiliation:
Centro Universitário de Belo Horizonte
Camila Vieira Brandão
Affiliation:
Centro Universitário de Belo Horizonte
Débora Martins Borges
Affiliation:
Centro Universitário de Belo Horizonte
Eduarda Muzzi Torres Lage
Affiliation:
Centro Universitário de Belo Horizonte
Luiza da Conceição Sabadini
Affiliation:
Centro Universitário de Belo Horizonte
Sabrina de Almeida Lopes
Affiliation:
Centro Universitário de Belo Horizonte
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Abstract

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Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used 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 database collected for the 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 (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). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in 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.