Abstract:
Background: In multi-infusion therapy, multiple infusion pumps are connected to one single vascular access point. Interaction between pressure changes from different pumps may result in temporary dosing errors, which can be very harmful to the patient. It is known that these dosing errors occur. However, clinicians tend to find it hard to estimate the order of magnitude of these errors.
Methods: This research uses an existing mathematical model to create a bedside prediction tool that is able to provide clinicians with the dosing errors that will occur after flow rate changes in multi-infusion therapy. A panel of clinicians, consisting of both nurses and doctors, was formed, and, in order to assess the level of knowledge about dosing errors in multi-infusion, the panel was presented with four medication schedules in which a syringe exchange or change in flow rate took place. The panel was asked to predict the resulting dosing errors.
Results: A prediction tool was developed that describes a two pump multi-infusion system and predicts dosing errors resulting from changing the flow rate at one pump. 44% of the panel members wrongly predicted the impact of changing the set flow of liquid A on the flow of liquid B that reaches the patient. Nobody was able to correctly predict the dosing deviation if a very small catheter was used. After the prediction tool was shown, the clinicians indicated they had a improved understanding of what deviations to expect and that the tool would be useful in understanding multi-infusion dosing errors.
Conclusions: Using the predictive tool to visualise the deviations from the set flow rate is an effective method to allow clinicians to gain insight in dosing errors in multi-infusion therapy. This knowledge can be used to better anticipate future dosing errors in clinical situations.
Reference:Gevers RJ, Konings MK, van den Hoogen A, Timmerman AM. Bedside visualisation tool for prediction of deviation from intended dosage in multi-infusion therapy. J Vasc Access. 2023 Jan 27:11297298221146327. doi: 10.1177/11297298221146327. Epub ahead of print. PMID: 36705289.