Perfil microbiológico de las infecciones asociadas a la atención en salud del Hospital Regional de Loreto, enero-diciembre 2021-2022

• Autor: Mori Meza, Jorge Luis
• Formato: tesis de maestría
• Fecha de Publicación: 2025
• Institución: Universidad Nacional De La Amazonía Peruana
• Repositorio: UNAPIquitos-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.unapiquitos.edu.pe:20.500.12737/11533
• Enlace del recurso: https://hdl.handle.net/20.500.12737/11533
• Nivel de acceso: acceso abierto
• Materia: Infección hospitalaria; Perfil; Microbiología; Signos y síntomas; Epidemiología; https://purl.org/pe-repo/ocde/ford#3.03.08

Healthcare-Associated Infections (HAIs) are acquired in hospitals after 48-72 hours of patient admission and represent a significant public health issue, with a prevalence of up to 8.7% in some countries. Intensive care units and surgical wards show the highest incidence, particularly among vulnerable patients. The use of invasive devices, such as urinary catheters and mechanical ventilation, increases the risk of developing HAIs. These infections, which include ventilator-associated pneumonia, urinary tract infections, and surgical site infections, prolong hospital stays, increase mortality, and generate high costs, highlighting the importance of preventive measures and analytical studies. This study aims to analyze the association between clinical-epidemiological characteristics and the microbiological profile of Healthcare-Associated Infections at the Loreto Regional Hospital, January-December 2021-2022. An analytical observational study will be conducted. The population will include hospitalized patients during the specified period, with a calculated sample size of 152 medical records, using probabilistic simple random sampling. Data will be analyzed using SPSS software version 22.0. For categorical variables, absolute and relative frequencies will be reported; for quantitative variables, means and standard deviations will be calculated. Inferential statistics, including logistic regression, will be applied to identify clinical-epidemiological characteristics associated with the microbiological patterns of HAIs (CI: 95%, p<0.05).