Tag Archives: phlebitis

Influence of different short peripheral cannula materials on the incidence of phlebitis in intensive care units: A post-hoc analysis of the AMOR-VENUS study

Yutaro Shinzato1, Hideto Yasuda1-3, Haruka Taira1, Yuki Kishihara1, Masahiro Kashiura1, Takashi Moriya1, Yuki Kotani4, Natsuki Kondo5, Kosuke Sekine6, Nobuaki Shime6, Keita Morikane7

1 Jichi Medical University Saitama Medical Center, Saitama, Japan
2 Alliance for Vascular Access Teaching and Research, Griffith University, Nathan, Queensland, Australia.
3 Department of Clinical Research Education and Training Unit, Keio University Hospital Clinical and Translational Research Centre (CTR), Tokyo, Japan.
4 Kameda Medical Center, Kamogawa, Japan
5 Chiba Emergency and Psychiatric Medical Center, Chiba, Japan
6 Division of Clinical Laboratory and Infectious Diseases, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
7 Yamagata University Hospital, Yamagata, Japan

DOI: 10.2478/jccm-2026-0014

Aim of the study: Short peripheral cannula (SPC)-related phlebitis occurs in 7.5% of critically ill patients, and mechanical irritation from cannula materials is a risk factor. Softer polyurethane cannulas reportedly reduce phlebitis, but the incidence of phlebitis may vary depending on the type of polyurethane. Differences in cannula stiffness may also affect the incidence of phlebitis; however, this relationship is not well understood. This study analyzed intensive care unit (ICU) patient data to compare the incidence of phlebitis across different cannula products, focusing on polyurethane.
Material and Methods: This is a post-hoc analysis of the AMOR-VENUS study that involved 23 ICUs in Japan. We included patients aged ≥ 18 years, who were admitted to the ICU with SPCs. The primary outcome was phlebitis, evaluated using hazard ratios (HRs) and 95% confidence intervals (CIs). Based on the market share and differences in synthesis, polyurethanes were categorized into PEU-Vialon® (BD, USA), SuperCath® (Medikit, Japan), and other polyurethanes; non-polyurethane materials were also analyzed. Multivariable marginal Cox regression analysis was performed using other polyurethanes as a reference.
Results: In total, 1,355 patients and 3,429 SPCs were evaluated. Among polyurethane cannulas, 1,087 (33.5%) were PEU-Vialon®, 702 (21.6%) were SuperCath®, and 276 (8.5%) were other polyurethanes. Among non-polyurethane cannulas, 1,292 (39.8%) were ethylene tetrafluoroethylene (ETFE) cannulas, and 72 (2.2%) used other materials. The highest incidence of phlebitis was observed with SuperCath® (13.1%). Multivariate analysis revealed an HR of 1.45 (95% CI 0.75-2.8, p = 0.21) for PEU-Vialon®, 2.60 (95% CI 1.35-5.00, p < 0.01) for SuperCath®, 2.29 (95% CI 1.19-4.42, p = 0.01) for ETFE, and 2.2 (95% CI 0.46-10.59, p = 0.32) for others.
Conclusions: The incidence of phlebitis varied among polyurethane cannulas. Further research is warranted to determine the causes of these differences.

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

Hideto Yasuda1,2,3,4, Claire M. Rickard3, 4, 5, Olivier Mimoz6,7,8, Nicole Marsh3,4,5, Jessica A Schults3,4,5, Bertrand Drugeon6,7, Masahiro Kashiura1, Yuki Kishihara1, Yutaro Shinzato1, Midori Koike1, Takashi Moriya1, Yuki Kotani9, Natsuki Kondo10, Kosuke Sekine11, Nobuaki Shime12, Keita Morikane13, Takayuki Abe14,15

1 Department of Emergency and Critical Care Medicine, Jichi Medical University Saimata Medical Center, Saitama, Japan
2 Department of Clinical Research Education and Training Unit, Keio University Hospital Clinical and Translational Research Center (CTR), Tokyo, Japan
3 School of Nursing, Midwifery and Social Work, UQ Centre for Clinical Research, The University of Queensland, QLD, Australia
4 School of Nursing and Midwifery; Alliance for Vascular Access Teaching and Research, Griffith University, Nathan, QLD, Australia
5 Herston Infectious Diseases Institute; Nursing and Midwifery Research Centre, Royal Brisbane and Women’s Hospital, Metro North Health, Herston, QLD, Australia
6 CHU de Poitiers, Emergency Department and prehospital care, Poitiers, France
7 University of Poitiers, INSERM U1070, Pharmacology of antimicrobial agents and antibiotics resistance (PHAR2), Poitiers, France
8 University of Poitiers, Faculty of Medicine and Pharmacy, Poitiers, France
9 Department of Intensive Care Medicine, Kameda Medical Center, Chiba, Japan
10 Department of Emergency Medicine, Koga Community Hospital, Shizuoka, Japan
11 Department of Medical Engineer, Kameda Medical Center, Chiba, Japan
12 Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
13 Division of Clinical Laboratory and Infection Control, Yamagata University Hospital, Yamagata, Japan
14 Biostatistics, Clinical and Translational Research Center, Keio University School of Medicine, Tokyo, Japan
15 School of Data Science, Kyoto Women’s University, Kyoto, Japan

DOI: 10.2478/jccm-2024-0028

Introduction: Early and accurate identification of high-risk patients with peripheral intravascular catheter (PIVC)-related phlebitis is vital to prevent medical device-related complications.
Aim of the study: This study aimed to develop and validate a machine learning-based model for predicting the incidence of PIVC-related phlebitis in critically ill patients.
Materials and methods: Four machine learning models were created using data from patients ≥ 18 years with a newly inserted PIVC during intensive care unit admission. Models were developed and validated using a 7:3 split. Random survival forest (RSF) was used to create predictive models for time-to-event outcomes. Logistic regression with least absolute reduction and selection operator (LASSO), random forest (RF), and gradient boosting decision tree were used to develop predictive models that treat outcome as a binary variable. Cox proportional hazards (COX) and logistic regression (LR) were used as comparators for time-to-event and binary outcomes, respectively.
Results: The final cohort had 3429 PIVCs, which were divided into the development cohort (2400 PIVCs) and validation cohort (1029 PIVCs). The c-statistic (95% confidence interval) of the models in the validation cohort for discrimination were as follows: RSF, 0.689 (0.627–0.750); LASSO, 0.664 (0.610–0.717); RF, 0.699 (0.645–0.753); gradient boosting tree, 0.699 (0.647–0.750); COX, 0.516 (0.454–0.578); and LR, 0.633 (0.575–0.691). No significant difference was observed among the c-statistic of the four models for binary outcome. However, RSF had a higher c-statistic than COX. The important predictive factors in RSF included inserted site, catheter material, age, and nicardipine, whereas those in RF included catheter dwell duration, nicardipine, and age.
Conclusions: The RSF model for the survival time analysis of phlebitis occurrence showed relatively high prediction performance compared with the COX model. No significant differences in prediction performance were observed among the models with phlebitis occurrence as the binary outcome.

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