880. Artificial Neural Networks to Predict Surgical Site Infection in Aorta Artery Aneurysm Correction
Authors: de Souza Flávio Henrique Batista , Couto Braulio Roberto Gonçalves Marinho , da Conceição Felipe Leandro Andrade , da Silva Gabriel Henrique Silvestre , Dias Igor Gonçalves , Rigueira Rafael Vieira Magno , Pimenta Gustavo Maciel , Martins Maurilio B , Mendes Júlio César O , Januário Guilherme Brangioni , Oliveira Rayane Thamires , de Vasconcelos Laura Ferraz , de Araújo Laís L , Rodrigues Ana Clara Resende , Silva Camila Morais Oliveira E , De Souza Eduarda Viana , Melo Júlia Faria , Assunção De Sá Maria Cláudia , Silva Walquíria Magalhães , Araujo Allana Luiza Padilha , Magalhães Bianca Braga , Caetano Caio Vieira , Guerra Carine Marina Dias , Braga Giovanna Lima 
Published: January 2021

Abstract

Background
A survey was conducted in three hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing surgeries to correct aortic artery aneurysms in the city of Belo Horizonte, with more than 3,000,000 of inhabitants. The general objective is to statistically evaluate such incidences and enable an analysis of the predictive power of SSI, through MLP (Multilayer Perceptron) pattern recognition algorithms.

Methods
Through the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research, data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. Thus, three procedures were performed: a treatment of the database collected for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) for each of the configurations.

Results
From 600 records, 575 were complete for analysis. It was found that: the average age is 68 years (from 24 to 98 years); the average hospital stay is 9 days (with a maximum of 127 days), the death rate reached 6.43% and the SSI rate 2.78%. A maximum prediction power of 0.75 was found.

Conclusion
There was a loss of 4% of the database samples due to the presence of noise. It was possible to evaluate the profile of the three hospitals. The predictive process presented configurations with results that reached 0.75, which promises the use of the structure for the monitoring of automated SSI for patients undergoing surgery to correct aortic artery aneurysms. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.

Disclosures
All Authors: No reported disclosures