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
Department of Emergency and Critical Care Medicine, Jichi Medical University Saimata Medical Center, Saitama, Japan.
Department of Clinical Research Education and Training Unit, Keio University Hospital Clinical and Translational Research Center (CTR), Tokyo, Japan.
J Crit Care Med (Targu Mures). 2024 Jul 31;10(3):232-244. doi: 10.2478/jccm-2024-0028. eCollection 2024 Jul.
Early and accurate identification of high-risk patients with peripheral intravascular catheter (PIVC)-related phlebitis is vital to prevent medical device-related complications.
This study aimed to develop and validate a machine learning-based model for predicting the incidence of PIVC-related phlebitis in critically ill patients.
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.
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.
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.
早期准确识别外周血管内导管（PIVC）相关静脉炎的高危患者对于预防医疗器械相关并发症至关重要。
本研究旨在开发并验证一种基于机器学习的模型，用于预测重症患者中PIVC相关静脉炎的发生率。
使用重症监护病房入院期间新插入PIVC的18岁及以上患者的数据创建了四种机器学习模型。模型采用7:3分割进行开发和验证。随机生存森林（RSF）用于创建事件发生时间结局的预测模型。使用带有最小绝对收缩和选择算子（LASSO）的逻辑回归、随机森林（RF）和梯度提升决策树来开发将结局视为二元变量的预测模型。分别使用Cox比例风险（COX）和逻辑回归（LR）作为事件发生时间和二元结局的比较器。
最终队列有3429根PIVC，分为开发队列（2400根PIVC）和验证队列（1029根PIVC）。验证队列中模型用于区分的c统计量（95%置信区间）如下：RSF为0.689（0.627 - 0.750）；LASSO为0.664（0.610 - 0.717）；RF为0.699（0.645 - 0.753）；梯度提升树为0.699（0.647 - 0.750）；COX为0.516（0.454 - 0.578）；LR为0.633（0.575 - 0.691）。对于二元结局，四个模型的c统计量之间未观察到显著差异。然而，RSF的c统计量高于COX。RSF中的重要预测因素包括插入部位、导管材料、年龄和尼卡地平，而RF中的重要预测因素包括导管留置时间、尼卡地平年龄。
与COX模型相比，用于静脉炎发生生存时间分析的RSF模型显示出相对较高的预测性能。以静脉炎发生作为二元结局的模型之间在预测性能上未观察到显著差异。
