Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis

Early and accurate identification of high-risk patients with peripheral intravascular catheter (PIVC)-related phlebitis is vital to prevent medical device-related complications.

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Bibliographic Details
Published in:Journal of critical care medicine (Universitatea de Medicină și Farmacie din Târgu-Mureș) 2024-07, Vol.10 (3), p.232-244
Main Authors: Yasuda, Hideto, Rickard, Claire M., Mimoz, Olivier, Marsh, Nicole, Schults, Jessica A, Drugeon, Bertrand, Kashiura, Masahiro, Kishihara, Yuki, Shinzato, Yutaro, Koike, Midori, Moriya, Takashi, Kotani, Yuki, Kondo, Natsuki, Sekine, Kosuke, Shime, Nobuaki, Morikane, Keita, Abe, Takayuki
Format: Article
Language:English
Subjects:
complication
machine learning
phlebitis
prediction model
risk factor
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Description
Summary:Early and accurate identification of high-risk patients with peripheral intravascular catheter (PIVC)-related phlebitis is vital to prevent medical device-related complications.
ISSN:2393-1817
2393-1809
2393-1817
DOI:10.2478/jccm-2024-0028