Neural network prediction model of risk for infection with carbapenem resistant Enterobacteriaceae among ICU patients in Colombia

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dc.contributor.advisorPorras Ramírez, Alexandra
dc.contributor.authorBarrera Godoy, Rodrigo
dc.date.accessioned2024-09-05T20:50:01Z
dc.date.available2024-09-05T20:50:01Z
dc.date.issued2023-08
dc.description.abstractAntibiotic resistance, particularly the emergence of carbapenem-resistant Enterobacteriaceae (CRE), poses a significant threat to global public health. This study aimed to develop a predictive model to estimate the clinical risk of CRE infection in patients admitted to an intensive care unit (ICU). A matched case-control study was conducted at a hospital in Bogotá, Colombia, involving 128 patients with CRE infections and 256 controls. The findings showed cardiovascular diseases and diabetes were the most common comorbidities among both groups. Univariate analysis revealed that patients in the case group were more likely to have undergone invasive procedures and medical devices such as central venous catheter insertion, urinary and foley catheter, also had a higher median number of hospitalization days. Moreover, patients with CRE infections had higher APACHE II scores. Previous infections caused by Enterobacteriaceae, hospital-acquired infections, and previous antibiotic treatments were significantly associated with CRE infections. The predictive model was developed using artificial neural networks (ANNs) and incorporated the identified risk factors. The model's performance was evaluated based on sensitivity, specificity, and accuracy, and different ANN configurations were compared. The model showed promise in accurately predicting the clinical risk of CRE infection in ICU patients. This study contributes to the understanding of risk factors associated with CRE infections in ICU settings and provides a practical tool for infection prevention and control strategies. The use of predictive models based on neural networks in public health can revolutionize disease surveillance, resource allocation, and personalized healthcare interventions, ultimately enhancing population health outcomes.
dc.description.abstractenglishAntibiotic resistance, particularly the emergence of carbapenem-resistant Enterobacteriaceae (CRE), poses a significant threat to global public health. This study aimed to develop a predictive model to estimate the clinical risk of CRE infection in patients admitted to an intensive care unit (ICU). A matched case-control study was conducted at a hospital in Bogotá, Colombia, involving 128 patients with CRE infections and 256 controls. The findings showed cardiovascular diseases and diabetes were the most common comorbidities among both groups. Univariate analysis revealed that patients in the case group were more likely to have undergone invasive procedures and medical devices such as central venous catheter insertion, urinary and foley catheter, also had a higher median number of hospitalization days. Moreover, patients with CRE infections had higher APACHE II scores. Previous infections caused by Enterobacteriaceae, hospital-acquired infections, and previous antibiotic treatments were significantly associated with CRE infections. The predictive model was developed using artificial neural networks (ANNs) and incorporated the identified risk factors. The model's performance was evaluated based on sensitivity, specificity, and accuracy, and different ANN configurations were compared. The model showed promise in accurately predicting the clinical risk of CRE infection in ICU patients. This study contributes to the understanding of risk factors associated with CRE infections in ICU settings and provides a practical tool for infection prevention and control strategies. The use of predictive models based on neural networks in public health can revolutionize disease surveillance, resource allocation, and personalized healthcare interventions, ultimately enhancing population health outcomes.
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Epidemiologíaspa
dc.description.sponsorshipClínica Los Nogalesspa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/12955
dc.language.isoeng
dc.publisher.facultyFacultad de Medicinaspa
dc.publisher.grantorUniversidad El Bosquespa
dc.publisher.programMaestría en Epidemiologíaspa
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dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf2
dc.rights.localAcceso abiertospa
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCarbapenem-resistant Enterobacteriaceae
dc.subjectRisk prediction model
dc.subjectIntensive care unit
dc.subjectRisk factors
dc.subjectArtificial neural networks
dc.subject.keywordsEnterobacterias resistentes a carbapenemes
dc.subject.keywordsModelo de predicción de riesgos
dc.subject.keywordsUnidad de cuidados intensivos
dc.subject.keywordsFactores de riesgo
dc.subject.keywordsRedes neuronales artificiales
dc.subject.nlmWA 105
dc.titleNeural network prediction model of risk for infection with carbapenem resistant Enterobacteriaceae among ICU patients in Colombiaspa
dc.title.translatedModelo de predicción de red neuronal del riesgo de infección por enterobacterias resistentes a carbapenémicos en pacientes de UCI en Colombia
dc.type.coarhttps://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttps://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis/Trabajo de grado - Monografía - Maestríaspa

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