Skip to main content Accessibility help
×
Home
Hostname: page-component-78dcdb465f-8p2q5 Total loading time: 21.427 Render date: 2021-04-15T07:48:16.970Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm

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
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
Gabriele Maria Braga
Affiliation:
Centro Universitário de Belo Horizonte
Jéssica Angelina Teixeira
Affiliation:
Centro Universitário de Belo Horizonte
Renata Carvalho Santos
Affiliation:
Centro Universitário de Belo Horizonte
Julia Maria Campos Martins
Affiliation:
Centro Universitário de Belo Horizonte
Karla Silvia de Sousa
Affiliation:
Centro Universitário de Belo Horizonte
Douglas Nascimento de Souza
Affiliation:
Centro Universitário de Belo Horizonte
Gustavo Barros Alves
Affiliation:
Centro Universitário de Belo Horizonte
Vladimir Alexei Rodrigues Rocha
Affiliation:
Centro Universitário de Belo Horizonte
Rights & Permissions[Opens in a new window]

Abstract

Background: In 5 hospitals in Belo Horizonte (population, 3 million) between July 2016 and June 2018, a survey was performed regarding surgical site infection (SSI). We statistically evaluated SSI incidents and optimized the power to predict SSI through pattern recognition algorithms based on support vector machines (SVMs). Methods: Data were collected on SSIs at 5 different hospitals. The hospital infection control committees (CCIHs) of the hospitals collected all data used in the analysis during their routine SSI surveillance procedures; these data were sent to the NOIS (Nosocomial Infection Study) Project. NOIS uses SACIH software (an automated hospital infection control system) to collect data from hospitals that participate voluntarily in the project. In the NOIS, 3 procedures were performed: (1) a treatment of the database collected 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 SVM with a nonlinear separation process varying in configurations including kernel function (Laplace, Radial Basis, Hyperbolic Tangent and Bessel) and the k-fold cross-validation–based resampling process (ie, the use of data varied according to the amount of folders that cross and combine the evaluated data, being k = 3, 5, 6, 7, and 10). The data were compared by measuring the area under the curve (AUC; range, 0–1) for each of the configurations. Results: From 13,383 records, 7,565 were usable, and SSI incidence was 2.0%. Most patients were aged 35–62 years; the average duration of surgery was 101 minutes, but 76% of surgeries lasted >2 hours. The mean hospital length of stay without SSI was 4 days versus 17 days for the SSI cases. The survey data showed that even with a low number of SSI cases, the prediction rate for this specific surgery was 0.74, which was 14% higher than the rate reported in the literature. Conclusions: Despite the high noise index of the database, it was possible to sample relevant data for the evaluation of general surgery patients. For the predictive process, our results were >0.50 and were 14% better than those reported in the literature. However, the database requires more SSI case samples because only 2% of positive samples unbalanced the database. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed (available at www.sacihweb.com).

Funding: None

Disclosures: None

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

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 0
Total number of PDF views: 72 *
View data table for this chart

* Views captured on Cambridge Core between 02nd November 2020 - 15th April 2021. This data will be updated every 24 hours.

You have Access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *