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

Hideto Yasuda; Claire M Rickard; Olivier Mimoz; Nicole Marsh; Jessica A Schults; Bertrand Drugeon; Masahiro Kashiura; Yuki Kishihara; Yutaro Shinzato; Midori Koike; Takashi Moriya; Yuki Kotani; Natsuki Kondo; Kosuke Sekine; Nobuaki Shime; Keita Morikane; Takayuki Abe

doi:10.2478/jccm-2024-0028

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

J Crit Care Med (Targu Mures). 2024 Jul 31;10(3):232-244. doi: 10.2478/jccm-2024-0028. eCollection 2024 Jul.

Authors

Hideto Yasuda^{1

2

3

4}, Claire M Rickard^{3

4

5}, Olivier Mimoz^{6

7

8}, Nicole Marsh^{3

4

5}, Jessica A Schults^{3

4

5}, Bertrand Drugeon^{6

7}, Masahiro Kashiura¹, Yuki Kishihara¹, Yutaro Shinzato¹, Midori Koike¹, Takashi Moriya¹, Yuki Kotani⁹, Natsuki Kondo¹⁰, Kosuke Sekine¹¹, Nobuaki Shime¹², Keita Morikane¹³, Takayuki Abe^{14

15}

Affiliations

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.

Abstract

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.

Keywords: complication; machine learning; phlebitis; prediction model; risk factor.