Risk Factors Affecting Death from Hospital-Acquired Infections in Trauma Patients: Association Rule Mining

Document Type : Original Article

Authors

1 School of Management & Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

2 Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran

3 Department of Health Information Management, School of Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

Abstract

Introduction: Trauma patients are potentially at high risk of acquiring infections in hospitals,
which is the main cause of in-hospital mortality. The aim of this study was to identify the risk
factors contributing to death from hospital-acquired infections in trauma patients by data
mining techniques.
Methods: This is a cohort study. A total of 549 trauma patients with nosocomial infection
who were admitted to Shiraz trauma hospital between 2017 and 2018 were studied. Sex,
age, mechanism of injury, body region injured, injury severity score, length of stay, type
of intervention, infection day after admission, microorganism cause of infections, and
the outcomes were collected. Association rule mining techniques were applied to extract
knowledge from the data set. The IBM SPSS Modeler data mining software version 18.0 was
used as a tool for data mining of the trauma patients with hospital queried infections database.
Results: The age older than 65, surgical site infection skin, bloodstream infection, mechanism
injury of car accident, invasive intervention of tracheal intubation, injury severity score higher
than 16, and multiple injuries with higher than 71 percent confidence level were associated
with in-hospital mortality. The relationship between those predicators and death among
hospital-acquired infection was strong (Lift value >1).
Conclusion: Factors such as increasing age, tracheal intubation, mechanical ventilator,
surgical site infection skin, upper respiratory infection are associated with death from
hospital-acquired infections in trauma patients by data mining.

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