Accepted for/Published in: JMIR AI
Date Submitted: May 1, 2023
Open Peer Review Period: May 1, 2023 - May 16, 2023
Date Accepted: Jul 21, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Retrospective review of determinants of intravenous infusion longevity and infusion failure via a nonlinear model analysis of smart pump event logs.
ABSTRACT
Background:
Infusion failure may have severe consequences for patients receiving critical short half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy.
Objective:
To identify and rank determinants of longevity of continuous infusions delivered by syringe driver through nonlinear predictive models to evaluate key factors, and to create, and test a model for predicting the likelihood of successful infusion longevity.
Methods:
Data was taken from the event logs of smart pumps with information on care profile, medication type and concentration, occlusion alarm setting, and final infusion cessation cause. The data was fitted to five nonlinear models, and evaluated for the best explanatory model.
Results:
Random Forest was the best fit predictor model. Final medication concentration and medication type were of less significance to infusion longevity than rate and care unit. For low-rate infusions rates between 2-2.8 ml/hr performed best for a balance between infusion longevity and fluid load per infusion.
Conclusions:
The study can inform clinicians of which types of infusion warrant more intense observation or proactive intravenous access management, and what is the average length of uninterrupted infusion that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions through compounding to create customized concentrations with for patients may be possible in the light of the study’s outcomes. The study also highlights the potential of machine learning nonlinear models to predict outcomes and lifespans of specific therapies delivered via medical devices. Clinical Trial: Nil
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