Predicción del efecto inóculo a Cefazolina en Staphylococcus Aureus susceptible a Meticilina por un método de aprendizaje automático

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dc.contributor.advisorDuitama Leal, Alejandro
dc.contributor.advisorReyes Manrique, Jinnethe Cristina
dc.contributor.authorMartín López, Zaidy Ocnary
dc.contributor.authorQuiroga Calderon, Cesar Hobany
dc.contributor.authorReyes Manrique, Lynda Jehny
dc.contributor.authorBermudez Munar, Jose Alejandro
dc.date.accessioned2024-08-03T00:35:22Z
dc.date.available2024-08-03T00:35:22Z
dc.date.issued2024-06
dc.description.abstractLa resistencia a antibióticos constituye un desafío de importancia clínica, no solo en términos de tratamiento biológico y terapéutico de las infecciones, sino también debido a su impacto en la salud pública (1). El Staphylococcus aureus, es un agente bacteriano común en el microbioma humano. Sin embargo, tambiénocasiona gran variedad de entidades infecciosas, incluyendo, bacteriemia, endocarditis, así como infecciones osteoarticulares, cutáneas, de tejidos blandos, pleuropulmonares y relacionadas con dispositivos (2). La incidencia de bacteriemia por Staphylococcus aureus (SAB) en Estados Unidos oscila entre 20 y 50 casos por cada 100.000 habitantes al año, con una tasa de mortalidad entre el 10% y el 30%, superando en número de muertes combinadas al VIH/SIDA, la tuberculosis y la hepatitis viral, lo que representa un considerable costo en términos de salud pública (3,4). La Sociedad Americana de Enfermedades Infecciosas (IDSA) recomienda los antibióticos betalactámicos como tratamiento fundamental para infecciones causadas por Staphylococcus aureus susceptible a meticilina (SASM) (5,6). La cefazolina se ha convertido en una excelente alternativa de tratamiento por sus bajos efectos adversos y su costo (6). Sin embargo, ha surgido un fenómeno de resistencia conocido como el efecto inóculo a cefazolina (CzIE), asociado a la producción de la betalactamasa (BlaZ) (7), lo que plantea la necesidad de explorar alternativas terapéuticas. El uso de técnicas de aprendizaje automático (Machine Learning - ML) se presenta como una vía prometedora para evaluar la capacidad predictiva de modelos en este contexto, lo que podría tener implicaciones significativas en la práctica médica, permitiendo el uso adecuado de la cefazolina y por ende optimizando la toma de decisiones para el tratamiento antibiótico.
dc.description.abstractenglishAntibiotic resistance constitutes a challenge of clinical importance, not only in terms of biological and therapeutic treatment of infections, but also due to its impact on public health (1). Staphylococcus aureus is common in the human microbiome. However, it also causes a wide variety of infections, including bacteremia, infective endocarditis, as well as osteoarticular, cutaneous, soft tissue, pleuropulmonary, and device-related infections (2). The incidence in U.S of Staphylococcus aureus bacteremia (SAB) ranges between 20 and 50 cases per 100,000 inhabitants per year, with a mortality rate between 10% and 30%, surpassing HIV/AIDS, tuberculosis in the number of combined deaths and viral hepatitis, which represents a considerable cost in terms of public health (3,4). The Infectious Diseases Society of America (IDSA) recommends beta-lactam antibiotics as the primary treatment for infections caused by methicillin-susceptible Staphylococcus aureus (MSSA) (5,6). Cefazolin has become an excellent treatment alternative due to its low adverse effects and cost (6). However, a resistance phenomenon known as the cefazolin inoculum effect (CzIE) has emerged, associated with the production of beta-lactamase (BlaZ) (7), which raises the need to explore therapeutic alternatives. The use of machine learning techniques (ML) is presented as a promising way to evaluate the predictive capacity of models in this context, which could have significant implications in medical practice, allowing the appropriate use of cefazolin and therefore optimizing decision making for antibiotic treatment.
dc.identifier.urihttps://hdl.handle.net/20.500.12495/12815
dc.language.isoes
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dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectResistencia a antibióticos
dc.subjectStaphylococcus aureus
dc.subjectStaphylococcus aureus susceptible a meticilina
dc.subjectAprendizaje automático
dc.subjectSASM
dc.subject.keywordsAntibiotic resistance
dc.subject.keywordsStaphylococcus aureus
dc.subject.keywordsMethicillin-susceptible Staphylococcus aureus
dc.subject.keywordsMachine learning
dc.subject.keywordsMSSA
dc.titlePredicción del efecto inóculo a Cefazolina en Staphylococcus Aureus susceptible a Meticilina por un método de aprendizaje automático
dc.title.translatedPrediction of the inoculum effect to Cefazolin in Methicillin susceptible Staphylococcus Aureus using a machine learning method

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