Índice neutrófilos-linfocitos como predictor de colecistectomía laparoscópica difícil en un hospital de la Amazonía Peruana, 2024

• Autor: Padilla Arévalo, Andy
• 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:20.500.12737/11977
• Enlace del recurso: https://hdl.handle.net/20.500.12737/11977
• Nivel de acceso: acceso abierto
• Materia: Colecistectomía laparoscópica; Biomarcadores; Índice de severidad de la enfermedad; Neutrófilos; Linfocitos; https://purl.org/pe-repo/ocde/ford#3.02.11

Introduction: Laparoscopic cholecystectomy, since its introduction, has transformed the management of biliary pathologies due to its lower invasiveness, reduced recovery time and lower morbidity compared to open surgery. However, in certain situations, this procedure becomes a ‘difficult cholecystectomy,’ characterised by complications such as severe inflammation, adhesions or anatomical variations. In the Peruvian Amazon, factors such as delayed access to health services and limited hospital resources increase the likelihood of facing these complex cases. In this context, the Neutrophil-Lymphocyte Index (NLI) has emerged as a useful inflammatory marker capable of predicting surgical difficulty, allowing better preoperative preparation and reducing the risk of intraoperative complications. Objective: To determine the Neutrophil-Lymphocyte Index as a predictor of difficult laparoscopic cholecystectomy in a hospital in the Peruvian Amazon during the year 2024. Methods: Observational, analytical, retrospective and cross-sectional. Population: Will include 982 patients undergoing laparoscopic cholecystectomy in the Regional Hospital of Loreto during the period 2023-2024, with a sample of 110 patients, divided into 55 cases of difficult cholecystectomy and 55 controls. Results: Data will be processed with IBM SPSS Statistics v29 software, performing descriptive, bivariate and multivariate analyses. Chi-square for categorical variables, Student's t-test or Mann-Whitney U test for numerical variables, and logistic regression will be used to identify independent predictors. A predictive model will be developed and its discriminative capacity will be evaluated by means of a ROC curve, considering a reliability of 95% and a p-value