Determinants of Intravenous Infusion Longevity and Infusion Failure via a Nonlinear Model Analysis of Smart Pump Event Logs: Retrospective Study.

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

This study aims to identify and rank determinants of the longevity of continuous infusions administered through syringe drivers, using nonlinear predictive models. Additionally, this study aims to evaluate key factors influencing infusion longevity and develop and test a model for predicting the likelihood of achieving successful infusion longevity.

METHODS

Data were extracted from the event logs of smart pumps containing information on care profiles, medication types and concentrations, occlusion alarm settings, and the final infusion cessation cause. These data were then used to fit 5 nonlinear models and evaluate the best explanatory model.

RESULTS

Random forest was the best-fit predictor, with an F

CONCLUSIONS

This study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study's outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.

Informations de copyright

©Arash Kia, James Waterson, Norma Bargary, Stuart Rolt, Kevin Burke, Jeremy Robertson, Samuel Garcia, Alessio Benavoli, David Bergström. Originally published in JMIR AI (https://ai.jmir.org), 13.09.2023.