Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura
Introducción: El uso excesivo de antibióticos de amplio espectro es una causa clave de resistencia antimicrobiana (RAM), asociada a alta morbilidad, mortalidad y costos sanitarios. Los modelos de predicción de RAM, basados en características clínicas del paciente, pueden guiar la terapia empírica y...
- Autores:
-
Morales Chaverra, Juan Pablo
- Tipo de recurso:
- Fecha de publicación:
- 2025
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/46125
- Acceso en línea:
- https://hdl.handle.net/10495/46125
- Palabra clave:
- Farmacorresistencia microbiana
Drug resistance, microbial
Infecciones urinarias
Urinary tract infections
Infecciones comunitarias adquiridas
Community-acquired infections
Métodos de predicción automáticos
Prediction methods, machine
Programas de optimización del uso de los antimicrobianos
Antimicrobial stewardship
Revisión sistemática
Systematic review
Terapia antibiótica empírica
https://id.nlm.nih.gov/mesh/D004352
https://id.nlm.nih.gov/mesh/D014552
https://id.nlm.nih.gov/mesh/D017714
https://id.nlm.nih.gov/mesh/D000098411
https://id.nlm.nih.gov/mesh/D000073602
https://id.nlm.nih.gov/mesh/D000078182
ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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| dc.title.spa.fl_str_mv |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| dc.title.translated.none.fl_str_mv |
Diagnostic Prediction Models of Antimicrobial Resistance to Guide Empirical Antibiotic Therapy Selection in Patients with Community-Acquired Symptomatic Urinary Tract Infection : A Systematic Literature Review |
| title |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| spellingShingle |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura Farmacorresistencia microbiana Drug resistance, microbial Infecciones urinarias Urinary tract infections Infecciones comunitarias adquiridas Community-acquired infections Métodos de predicción automáticos Prediction methods, machine Programas de optimización del uso de los antimicrobianos Antimicrobial stewardship Revisión sistemática Systematic review Terapia antibiótica empírica https://id.nlm.nih.gov/mesh/D004352 https://id.nlm.nih.gov/mesh/D014552 https://id.nlm.nih.gov/mesh/D017714 https://id.nlm.nih.gov/mesh/D000098411 https://id.nlm.nih.gov/mesh/D000073602 https://id.nlm.nih.gov/mesh/D000078182 ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades |
| title_short |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| title_full |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| title_fullStr |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| title_full_unstemmed |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| title_sort |
Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literatura |
| dc.creator.fl_str_mv |
Morales Chaverra, Juan Pablo |
| dc.contributor.advisor.none.fl_str_mv |
Cienfuegos Gallet, Astrid Vanessa Aguirre Acevedo, Daniel Camilo Restrepo Castro, Carlos Andrés |
| dc.contributor.author.none.fl_str_mv |
Morales Chaverra, Juan Pablo |
| dc.contributor.researchgroup.none.fl_str_mv |
Grupo de Investigación en Microbiología Básica y Aplicada-Microba |
| dc.contributor.jury.none.fl_str_mv |
Roncancio Villamil, Gustavo Eduardo Maya Restrepo, María Angélica |
| dc.subject.decs.none.fl_str_mv |
Farmacorresistencia microbiana Drug resistance, microbial Infecciones urinarias Urinary tract infections Infecciones comunitarias adquiridas Community-acquired infections Métodos de predicción automáticos Prediction methods, machine Programas de optimización del uso de los antimicrobianos Antimicrobial stewardship Revisión sistemática Systematic review |
| topic |
Farmacorresistencia microbiana Drug resistance, microbial Infecciones urinarias Urinary tract infections Infecciones comunitarias adquiridas Community-acquired infections Métodos de predicción automáticos Prediction methods, machine Programas de optimización del uso de los antimicrobianos Antimicrobial stewardship Revisión sistemática Systematic review Terapia antibiótica empírica https://id.nlm.nih.gov/mesh/D004352 https://id.nlm.nih.gov/mesh/D014552 https://id.nlm.nih.gov/mesh/D017714 https://id.nlm.nih.gov/mesh/D000098411 https://id.nlm.nih.gov/mesh/D000073602 https://id.nlm.nih.gov/mesh/D000078182 ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades |
| dc.subject.proposal.spa.fl_str_mv |
Terapia antibiótica empírica |
| dc.subject.meshuri.none.fl_str_mv |
https://id.nlm.nih.gov/mesh/D004352 https://id.nlm.nih.gov/mesh/D014552 https://id.nlm.nih.gov/mesh/D017714 https://id.nlm.nih.gov/mesh/D000098411 https://id.nlm.nih.gov/mesh/D000073602 https://id.nlm.nih.gov/mesh/D000078182 |
| dc.subject.ods.none.fl_str_mv |
ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades |
| description |
Introducción: El uso excesivo de antibióticos de amplio espectro es una causa clave de resistencia antimicrobiana (RAM), asociada a alta morbilidad, mortalidad y costos sanitarios. Los modelos de predicción de RAM, basados en características clínicas del paciente, pueden guiar la terapia empírica y reducir el uso inapropiado de antibióticos. Esta revisión sistemática evaluó modelos de predicción de RAM en pacientes con Infección del Tracto Urinario Sintomática Adquirida en la Comunidad (ISTU-C), analizando calidad del reporte, validez metodológica, predictores, modelos desarrollados y desempeño. Metodología: Se realizó una revisión sistemática siguiendo los lineamientos de PRISMA, CHARMS y TRIPOD-SRMA. Se incluyeron estudios de desarrollo y/o validación de modelos de predicción de RAM en ISTU-C. Se consultaron ocho bases de datos electrónicas (PubMed, ScienceDirect, Embase, Cochrane, entre otras), literatura gris y referencias de estudios. Dos investigadores realizaron selección, extracción y evaluación de calidad mediante TRIPOD y PROBAST. Resultados: De 1089 artículos, se incluyeron 23 estudios. Los desenlaces más comunes fueron BLEE (17.39%), microorganismos multidrogoresistentes (21.74%) y resistencia a betalactámicos, quinolonas, nitrofuranos e inhibidores de folato (47.82%). Los predictores más frecuentes fueron características demográficas (100%), exposición antibiótica (91.30%), comorbilidades (82.60%) y hospitalización previa (72.26%). El 65.22% usó estadística tradicional y 34.78% inteligencia artificial. El 56.52% no reportó manejo de datos faltantes. A pesar de que algunos alcanzaron un desempeño prometedor (AUC-ROC > 0.80), la mayoría se limitaron a validaciones internas. Solo nueve estudios realizaron validación externa. Conclusiones: Aunque prometedores, los modelos enfrentan limitaciones metodológicas. Mejorar la calidad del reporte, estandarizar definiciones de desenlaces y predictores, y validar externamente en poblaciones diversas es clave para su implementación clínica efectiva. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-05-27T13:32:58Z |
| dc.date.issued.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/draft |
| status_str |
draft |
| dc.identifier.citation.none.fl_str_mv |
Morales Chaverra, JP. Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con Infección Sintomática de Tracto Urinario Adquirida en la Comunidad: Revisión sistemática de la literatura [Tesis de maestría]. Medellín, Colombia. Universidad de Antioquia; 2025. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10495/46125 |
| identifier_str_mv |
Morales Chaverra, JP. Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con Infección Sintomática de Tracto Urinario Adquirida en la Comunidad: Revisión sistemática de la literatura [Tesis de maestría]. Medellín, Colombia. Universidad de Antioquia; 2025. |
| url |
https://hdl.handle.net/10495/46125 |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
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Cienfuegos Gallet, Astrid VanessaAguirre Acevedo, Daniel CamiloRestrepo Castro, Carlos AndrésMorales Chaverra, Juan PabloGrupo de Investigación en Microbiología Básica y Aplicada-MicrobaRoncancio Villamil, Gustavo EduardoMaya Restrepo, María Angélica2025-05-27T13:32:58Z2025Morales Chaverra, JP. Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con Infección Sintomática de Tracto Urinario Adquirida en la Comunidad: Revisión sistemática de la literatura [Tesis de maestría]. Medellín, Colombia. Universidad de Antioquia; 2025.https://hdl.handle.net/10495/46125Introducción: El uso excesivo de antibióticos de amplio espectro es una causa clave de resistencia antimicrobiana (RAM), asociada a alta morbilidad, mortalidad y costos sanitarios. Los modelos de predicción de RAM, basados en características clínicas del paciente, pueden guiar la terapia empírica y reducir el uso inapropiado de antibióticos. Esta revisión sistemática evaluó modelos de predicción de RAM en pacientes con Infección del Tracto Urinario Sintomática Adquirida en la Comunidad (ISTU-C), analizando calidad del reporte, validez metodológica, predictores, modelos desarrollados y desempeño. Metodología: Se realizó una revisión sistemática siguiendo los lineamientos de PRISMA, CHARMS y TRIPOD-SRMA. Se incluyeron estudios de desarrollo y/o validación de modelos de predicción de RAM en ISTU-C. Se consultaron ocho bases de datos electrónicas (PubMed, ScienceDirect, Embase, Cochrane, entre otras), literatura gris y referencias de estudios. Dos investigadores realizaron selección, extracción y evaluación de calidad mediante TRIPOD y PROBAST. Resultados: De 1089 artículos, se incluyeron 23 estudios. Los desenlaces más comunes fueron BLEE (17.39%), microorganismos multidrogoresistentes (21.74%) y resistencia a betalactámicos, quinolonas, nitrofuranos e inhibidores de folato (47.82%). Los predictores más frecuentes fueron características demográficas (100%), exposición antibiótica (91.30%), comorbilidades (82.60%) y hospitalización previa (72.26%). El 65.22% usó estadística tradicional y 34.78% inteligencia artificial. El 56.52% no reportó manejo de datos faltantes. A pesar de que algunos alcanzaron un desempeño prometedor (AUC-ROC > 0.80), la mayoría se limitaron a validaciones internas. Solo nueve estudios realizaron validación externa. Conclusiones: Aunque prometedores, los modelos enfrentan limitaciones metodológicas. Mejorar la calidad del reporte, estandarizar definiciones de desenlaces y predictores, y validar externamente en poblaciones diversas es clave para su implementación clínica efectiva.Introduction: The excessive use of broad-spectrum antibiotics is a key driver of antimicrobial resistance (AMR), leading to increased morbidity, mortality, and healthcare costs. AMR prediction models based on patient clinical characteristics can guide empirical therapy and reduce inappropriate antibiotic use. This systematic review evaluated AMR prediction models in patients with Community-Acquired Symptomatic Urinary Tract Infections (C-sUTI), analyzing reporting quality, methodological validity, predictors, developed models, and performance. Methods: A systematic review was conducted following PRISMA, CHARMS, and TRIPOD-SRMA guidelines. Studies involving the development and/or validation of AMR prediction models in C-sUTI were included. Eight electronic databases (PubMed, ScienceDirect, Embase, Cochrane, among others), gray literature, and reference lists of relevant studies were searched. Two reviewers independently performed study selection, data extraction, and quality assessment using TRIPOD and PROBAST. Results: Out of 1,089 articles, 23 studies were included. The most common outcomes were ESBL-producing organisms (17.39%), multidrug-resistant organisms (21.74%), and resistance to beta-lactams, quinolones, nitrofurans, and folate inhibitors (47.82%). Frequent predictors included demographics (100%), prior antibiotic exposure (91.30%), comorbidities (82.60%), and previous hospitalization (72.26%). Most models used traditional statistics (65.22%), and some applied artificial intelligence (34.78%). Although some achieved promising performance (AUC-ROC > 0.80), most relied solely on internal validation. Missing data handling was unreported in 56.52% of studies; only nine conducted external validation. Conclusions: Although promising, models face methodological limitations. Enhancing reporting quality, standardizing outcome and predictor definitions, and validating externally across diverse populations are essential for clinical implementation.Modelos de predicción diagnóstica de resistencia antimicrobianaCOL0126131MaestríaMagíster en Microbiología162 páginasapplication/pdfapplication/x-compressedspaUniversidad de AntioquiaMaestría en MicrobiologíaMedellín, ColombiaEscuela de MicrobiologíaCampus Medellín - Ciudad Universitariahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Modelos de predicción diagnóstica de resistencia antimicrobiana para orientar la selección de la terapia antibiótica empírica en pacientes con infección sintomática de tracto urinario adquirida en la comunidad : revisión sistemática de la literaturaDiagnostic Prediction Models of Antimicrobial Resistance to Guide Empirical Antibiotic Therapy Selection in Patients with Community-Acquired Symptomatic Urinary Tract Infection : A Systematic Literature ReviewTrabajo de grado - Maestríahttp://purl.org/redcol/resource_type/TMTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/draftMorrison L, Zembower TR. 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