Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

In-hospital mortality of sepsis differs depending on the origin of infection: an investigation of predisposing factors

Pieroni, M, Olier-Caparroso, I, Ortega Martorell, S, Johnston, B and Welters, I In-hospital mortality of sepsis differs depending on the origin of infection: an investigation of predisposing factors. Frontiers in Medicine - Intensive care medicine and anesthesiology. ISSN 2296-858X (Accepted)

[img]
Preview
Text
In hospital mortality of sepsis differs depending on the origin of infection.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview
[img]
Preview
Text
Supplementary Material.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (193kB) | Preview

Abstract

Sepsis is a heterogeneous syndrome characterised by a variety of clinical features. Analysis of large clinical datasets may serve to define groups of sepsis with different risks of adverse outcomes. Clinical experience supports the concept that prognosis, treatment, severity and time course of sepsis vary depending on the source of infection. We analysed a large publicly available database to test this hypothesis. In addition, we developed prognostic models for the three main types of sepsis: pulmonary, urinary and abdominal sepsis. We used logistic regression using routinely available clinical data for mortality prediction in each of these groups.
The data was extracted from the eICU collaborative research database, a multi-centre intensive care unit with over 200,000 admissions. Sepsis cohorts were defined using admission diagnosis codes. We used univariate and multivariate analyses to establish factors relevant for outcome prediction in all three cohorts of sepsis (pulmonary, urinary and abdominal). For logistic regression, input variables were automatically selected using a sequential forward search algorithm over 10 dataset instances. Receiver operator characteristics were generated for each model and compared with established prognostication tools (APACHE IV and SOFA).
3958 sepsis admissions were included in the analysis. Sepsis in-hospital mortality differed depending on the cause of infection: abdominal 18.93%, pulmonary 19.27%, and renal 12.81%. Higher average heart rate was associated with increased mortality risk. Increased average Mean Arterial Pressure showed a reduced mortality risk across all sepsis groups. Results from the LR models found significant factors that were relevant for specific sepsis groups. Our models outperformed APACHE IV and SOFA scores with AUC between 0.63 and 0.74. Predictive power decreased over time, with the best results achieved for data extracted for the first 24h of admission.
Mortality varied significantly between the three sepsis groups. We also demonstrate that factors of importance show considerable heterogeneity depending on the source of infection. The factors influencing in-hospital mortality vary depending on the source of sepsis which may explain why most sepsis trials have failed to identify an effective treatment. The source of infection should be considered when considering mortality risk. Planning of sepsis treatment trials may benefit from risk stratification based on the source of infection.

Item Type: Article
Uncontrolled Keywords: sepsis; intensive care medicine; mortality risk; prognostic factors; origin of infection; logistic regression
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Frontiers Media
SWORD Depositor: A Symplectic
Date Deposited: 21 Jun 2022 10:26
Last Modified: 21 Jun 2022 10:30
URI: https://researchonline.ljmu.ac.uk/id/eprint/17133

Actions (login required)

View Item View Item