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Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network

Crossref DOI link: https://doi.org/10.1016/j.bjid.2025.104510

Published Print: 2025-03

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Cedeno, Julio Alejandro https://orcid.org/0009-0000-7583-2707
Strabelli, Tania Mara Varejão

Besen, Bruno Adler Maccagnan Pinheiro

Souza, Rafael de Freitas https://orcid.org/0000-0001-6298-5325
Sierra, Denise Blini https://orcid.org/0009-0004-1746-524X
de Souza, Leticia Rodrigues Goulart https://orcid.org/0000-0003-2571-9727
Gallafrio, Samuel Terra

Abboud, Cely Saad

Feriani, Diego

Siciliano, Rinaldo Focaccia
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Text and Data Mining valid from 2025-03-01

Text and Data Mining valid from 2025-03-01

Version of Record valid from 2025-02-05
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This article is maintained by: Elsevier

Article Title: Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network

Journal Title: The Brazilian Journal of Infectious Diseases

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.bjid.2025.104510

Content Type: article

Copyright: © 2025 Sociedade Brasileira de Infectologia. Published by Elsevier España, S.L.U.

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