897. Prediction of Surgical Site Infections in Colon Surgery in Belo Horizonte Hospitals
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 , Barnabé Felipe Augusto Pereira , Cunha Bruna Soares , Lemos Júlia Ferreira , Da Fonseca Laize Rodrigues , Vieira Júnior Luciano Cunha , de Pinho Vitor Barroso 
Published: January 2021

Background

In Belo Horizonte (a 3,000,000 inhabitants city) a survey was carried out in five hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing colon surgery procedures. The general objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms.

Methods

Through the Hospital Infection Control Committees (CCIH) of the hospitals a data collection on SSI was carried out. Such data was used in the analysis during your routine SSI surveillance procedures. So, three procedures were performed: a treatment of the collected database 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) presented for each of the configurations.

Results

From 2126 records, 638 were complete for analysis. It was found: the average age is 55 years (from 1 to 94 years); the surgeries had an average time of approximately 197 minutes; the average hospital stay is 8 days, the death rate reached 5.625% and the SSI rate reached 6%. Regarding the predictive power, a maximum predictive power of 0.8316 was found.

Conclusion

There was a loss of 70% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these five hospitals. The predictive process presented some configurations with results that reached 0.8316, which promises the use of the structure for the monitoring of automated SSI for patients undergoing colon surgeries. 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 the other for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br.

Disclosures

All Authors: No reported disclosures