Yang Erming, Wang Qiaohong, Guo Jing, Wei Jilin, Zhang Chiyu, Zhao Wenfang, He Xingyue, Bo Enhui, Mao Ya, Yang Hui
School of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.
Department of Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Intensive Crit Care Nurs. 2024 Oct;84:103703. doi: 10.1016/j.iccn.2024.103703. Epub 2024 May 4.
This study aimed to develop and validate a prediction model for premature circuit clotting of continuous renal replacement therapy (CRRT) in critically ill patients.
A retrospective cohort study was conducted on ICU patients undergoing CRRT. The Medical Information Mart for Intensive Care-III Clinical Database CareVue subset and Medical Information Mart for Intensive Care-IV were utilized for model development, while the eICU Collaborative Research Database was employed for external validation. Predictive factors were selected through Least Absolute Shrinkage and Selection Operator Regression and univariate logistic regression. A prediction model was then developed using binary logistic regression. Internal and external validations assessed the model's discrimination, calibration, and clinical net benefit.
This study encompassed 2531 patients overall, with a premature circuit clotting rate of 31.88 %. The prediction model comprises five variables: body temperature, anticoagulation, mean arterial pressure, maximum transmembrane pressure change within two hours, and vasopressor. The model demonstrated robust predictive performance, achieving an area under the receiver operating characteristic curve of 0.897 (95 % CI: 0.879-0.915) in the training set and 0.877 (95 % CI: 0.852-0.902) in the external validation set. Internal validation yielded a Brier score of 0.087, while external validation showed a Brier score of 0.120. Calibration curves indicated good model calibration for both validations. The decision curve analysis indicates that the model yields a clinical net benefit across a wide range of decision thresholds.
The model demonstrates robust discrimination, calibration, and clinical net benefit, with readily available variables indicating substantial potential for valuable clinical applications.
Healthcare providers in the ICU can leverage the model to evaluate the risk of premature circuit clotting in critically ill patients undergoing continuous renal replacement therapy, facilitating timely intervention to mitigate its incidence.
本研究旨在开发并验证一种针对危重症患者连续性肾脏替代治疗(CRRT)时体外循环管路过早凝血的预测模型。
对接受CRRT的ICU患者进行一项回顾性队列研究。重症监护医学信息集市-III临床数据库CareVue子集和重症监护医学信息集市-IV用于模型开发,而eICU协作研究数据库用于外部验证。通过最小绝对收缩和选择算子回归以及单变量逻辑回归选择预测因素。然后使用二元逻辑回归开发预测模型。内部和外部验证评估了模型的辨别力、校准度和临床净效益。
本研究共纳入2531例患者,体外循环管路过早凝血发生率为31.88%。预测模型包含五个变量:体温、抗凝情况、平均动脉压、两小时内最大跨膜压变化以及血管活性药物使用情况。该模型表现出强大的预测性能,在训练集中受试者工作特征曲线下面积为0.897(95%CI:0.879 - 0.915),在外部验证集中为0.877(95%CI:0.852 - 0.902)。内部验证的Brier评分为0.087,外部验证的Brier评分为0.120。校准曲线表明两次验证中模型校准良好。决策曲线分析表明,该模型在广泛的决策阈值范围内均产生临床净效益。
该模型具有强大的辨别力、校准度和临床净效益,所使用的变量易于获取,表明其在临床应用中具有很大潜力。
ICU医护人员可利用该模型评估接受连续性肾脏替代治疗的危重症患者体外循环管路过早凝血的风险,以便及时进行干预,降低其发生率。