Bibliographic citations
Velasco, M., (2018). Modelo de predicción para infección de sitio quirúrgico en pacientes post operados de cirugía colorrectal [Tesis, Universidad Privada Antenor Orrego - UPAO]. https://hdl.handle.net/20.500.12759/4013
Velasco, M., Modelo de predicción para infección de sitio quirúrgico en pacientes post operados de cirugía colorrectal [Tesis]. : Universidad Privada Antenor Orrego - UPAO; 2018. https://hdl.handle.net/20.500.12759/4013
@misc{sunedu/4046261,
title = "Modelo de predicción para infección de sitio quirúrgico en pacientes post operados de cirugía colorrectal",
author = "Velasco Rodriguez, Manuel Fabian",
publisher = "Universidad Privada Antenor Orrego - UPAO",
year = "2018"
}
OBJECTIVE: Create a model to predict surgical site infection (SSI) in patients undergoing colorectal surgery. MATERIAL AND METHODS: An observational, analytical, cross-sectional study was carried out in the general surgery service of the Hospital Nacional Dos de Mayo in Lima and 158 patients undergoing colorectal surgery between January 2012 and December 2017 were included. The data was analyzed in bivariate and multivariate form to later elaborate a prediction model. RESULTS: The mean age was 68.47 ± 15.55 years in the SSI group and 57.01 ± 15.46 years in the no SSI group (p = 0.001). The female gender was 50.94% in the SSI group and the male gender 65.71% in the no SSI group (p = 0.03, ORc 1.99, 95% CI [1.02-3.90 ]). The dirty surgical wound type was 54.72% in the SSI group and in the no SSI group the contaminated wound was 40% (p = 0.001). In the multivariate analysis, the variables that showed statistical significance were age (p = 0.018, ORa 1.03, 95% CI [1.01-1.06]), hypoalbuminemia (p = 0.001; ORa 10.62; 95% [3,14-35,97], intraoperative blood transfusion (p = 0,001, ORa 6,39, 95% CI [2,09-19,48]), emergency surgery (p = 0,005; ORa 6 , 01, 95% CI [1.74-20.76]) and the high-risk wound (p = 0.006, ORa 5.14, 95% CI [1.62-16.39]). CONCLUSIONS: The predictive model constructed with clinical and surgical variables allows the prediction of surgical site infection with an accuracy of 84%.
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