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Effect of renal replacement therapy on acute kidney injury in sepsis patients

Author(s)
Argaw, Peniel N. (Peniel Neway)
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Leo A. Celi.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
According to the Centers for Disease Control and Prevention, there are approximately 1.5 million cases of sepsis and over 250,000 resultant deaths each year [1]. One of the major effects of sepsis is organ failure, notably in the kidneys, lungs, liver, and brain. In the case where the kidneys fail, renal replacement therapy (RRT) may be performed in order to sustain the functionality of the kidneys and overall ameliorate patients' outcomes. The goal of this work is to determine the relationship between undergoing RRT and patient outcome. The Philips-MIT eICU Collaborative Research Database was used to identify patients with sepsis and acute kidney injury, and split the cohort into those who had undergone RRT and those who did not. Multivariate logistic regression and propensity score analysis were utilized to evaluate the treatment effect on mortality. The patients who underwent RRT had a significantly better outcome than those who did not (odds ratio = 0.260465, 95% confidence interval = 0.211568 to 0.320664, p<0.001). From the filtered patients, the percentage of men to women increased with those who underwent RRT (55.08% vs. 53.78%) as well as the percentage of African Americans (25% vs. 15.63%) and Other (5.86% vs. 4.04%) ethnicities. In addition to gender and ethnicity, other covariates such as Sequential Organ Failure Assessment score, cirrhosis, and metastatic cancer had a great impact on patient outcomes. This work concludes that RRT does in fact benefit the patient outcome and dialysis is a statistically significant feature within the dataset.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
 
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 39-40).
 
Date issued
2018
URI
http://hdl.handle.net/1721.1/119740
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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