Model for Unplanned Self Extubation of ICU Patients Using System Dynamics Approach.
10.4040/jkan.2015.45.2.280
- Author:
Yu Gil SONG
1
;
Eun Kyoung YUN
Author Information
1. College of Nursing Science, Kyung Hee University, Seoul, Korea. ekyun@khu.ac.kr
- Publication Type:Original Article
- Keywords:
Airway extubation;
Intensive care units;
Nonlinear dynamics
- MeSH:
Adult;
Airway Extubation/*psychology;
Female;
Humans;
Intensive Care Units;
Intubation, Intratracheal;
Male;
Middle Aged;
*Models, Theoretical;
Nonlinear Dynamics;
Risk Factors
- From:Journal of Korean Academy of Nursing
2015;45(2):280-292
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: In this study a system dynamics methodology was used to identify correlation and nonlinear feedback structure among factors affecting unplanned extubation (UE) of ICU patients and to construct and verify a simulation model. METHODS: Factors affecting UE were identified through a theoretical background established by reviewing literature and preceding studies and referencing various statistical data. Related variables were decided through verification of content validity by an expert group. A causal loop diagram (CLD) was made based on the variables. Stock & Flow modeling using Vensim PLE Plus Version 6.0b was performed to establish a model for UE. RESULTS: Based on the literature review and expert verification, 18 variables associated with UE were identified and CLD was prepared. From the prepared CLD, a model was developed by converting to the Stock & Flow Diagram. Results of the simulation showed that patient stress, patient in an agitated state, restraint application, patient movability, and individual intensive nursing were variables giving the greatest effect to UE probability. To verify agreement of the UE model with real situations, simulation with 5 cases was performed. Equation check and sensitivity analysis on TIME STEP were executed to validate model integrity. CONCLUSION: Results show that identification of a proper model enables prediction of UE probability. This prediction allows for adjustment of related factors, and provides basic data do develop nursing interventions to decrease UE.
