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The sepsis syndrome and the "one size fits all" construct: the emperor has not clothes!!

Gomez, Hernando (2014) The sepsis syndrome and the "one size fits all" construct: the emperor has not clothes!! Master's Thesis, University of Pittsburgh.

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Background: Sepsis, a syndrome characterized by a systemic, (often overwhelming) inflammatory response to infection is an under recognized, potentially lethal, public health problem in developed and developing countries. Furthermore, is unlikely that it will improve, as other than standard critical care support, there are no effective specific treatment strategies. Most of the therapeutic trials conducted in the last four decades, other than the lack of benefit, have consistently shown that subgroups of septic patients respond differently to the same treatment. This has lead to the thought that sepsis may not be a unique syndrome only differentiated by grades of severity, but rather a syndrome that encompasses diverse phenotypes that behave differently and thus may respond different not only to injury but also to treatment. Thus, the aim of this study is to explore if distinct phenotypes exist in a cohort of critically ill patients with suspected sepsis, and if these can be identified through clinical available data. This is highly relevant to the public health aspect of Sepsis, as it challenges the current paradigm, and provides the basis to develop a new approach that may lead finally to an effective reduction of morbidity and mortality. Methods: We used a large database of critically ill patients (HiDenIC-8). We selected a population of patients with “suspected sepsis” defined as having blood cultures sent or being started on antibiotics within 24 hours of admission to the ICU. We defined demographic, clinical and available laboratory variables to include in the clustering algorithm, and selected them on the basis of availability, and absence of redundancy. We used hierarchical clustering to evaluate the possible number of clusters according to the data structure, and then ran K-means method to determine the actual cluster schedule. Results: We found 13 clusters, 8 of which included more than 70 subjects (~2.5% of entire population). We found important differences in demographic, clinical and laboratory data at admission, and also, different clinical trajectories in terms of patterns of organ dysfunction and mortality. Conclusion: The present study has demonstrated that an unsupervised clustering technique based on frequently collected demographic, clinical and physiologic data can be used to derive distinct, biologically sound clusters of patients who clinically behave differently from each other.


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Item Type: Other Thesis, Dissertation, or Long Paper (Master's Thesis)
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Gomez, Hernando
ContributionContributors NameEmailPitt UsernameORCID
Committee ChairFinegold, David N.dnf@pitt.eduDNFUNSPECIFIED
Committee MemberKellum, John A.kellumja@ccm.upmc.eduKELLUMUNSPECIFIED
Committee MemberWeissfeld, Lisa A.lweis@pitt.eduLWEISUNSPECIFIED
Committee MemberPinsky, Michael R.pinskymr@ccm.upmc.eduPINSKYUNSPECIFIED
Date: August 2014
Date Type: Publication
Defense Date: 2014
Submission Date: 18 July 2014
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Publisher: University of Pittsburgh
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Multidisciplinary MPH
Degree: MPH - Master of Public Health
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Sepsis, phenotypes, clusters, organ, dysfunction
Date Deposited: 14 Nov 2014 16:03
Last Modified: 22 May 2024 10:55


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