Wang Fan, Zhu Yanyi, Wang Lijuan, Huang Caiying, Mei Ranran, Deng Li-E, Yang Xiulan, Xu Yan, Zhang Lingling, Xu Min
Breast Surgery, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China.
Radiotherapy Department, Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China.
Asia Pac J Oncol Nurs. 2024 Jun 22;11(8):100546. doi: 10.1016/j.apjon.2024.100546. eCollection 2024 Aug.
This study aimed to develop and validate a machine learning-based risk prediction model for catheter-related bloodstream infection (CRBSI) following implantation of totally implantable venous access ports (TIVAPs) in patients.
A retrospective cohort study design was employed, utilizing the R software package mlr3. Various algorithms including logistic regression, naive Bayes, K nearest neighbor, classification tree, and random forest were applied. Addressing class imbalance, benchmarks were used, and model performance was assessed using the area under the curve (AUC). The final model, chosen for its superior performance, was interpreted using variable importance scores. Additionally, a nomogram was developed to calculate individualized risk probabilities, enhancing clinical utility.
The study involved 755 patients across both development and validation cohorts, with a TIVAP-CRBSI rate of 14.17%. The random forest model demonstrated the highest discrimination ability, achieving a validated AUC of 0.94, which was consistent in the validation cohort.
This study successfully developed a robust predictive model for TIVAP-CRBSI risk post-implantation. Implementation of this model may aid healthcare providers in making informed decisions, thereby potentially improving patient outcomes.
本研究旨在开发并验证一种基于机器学习的风险预测模型，用于预测患者植入全植入式静脉通路端口（TIVAP）后发生导管相关血流感染（CRBSI）的风险。
采用回顾性队列研究设计，使用R软件包mlr3。应用了包括逻辑回归、朴素贝叶斯、K近邻、分类树和随机森林在内的各种算法。针对类别不平衡问题，使用了基准，并使用曲线下面积（AUC）评估模型性能。选择性能优越的最终模型，使用变量重要性得分进行解释。此外，还开发了一种列线图来计算个体化风险概率，以提高临床实用性。
该研究涉及开发队列和验证队列中的755例患者，TIVAP-CRBSI发生率为14.17%。随机森林模型表现出最高的区分能力，验证后的AUC为0.94，在验证队列中结果一致。
本研究成功开发了一种用于预测植入后TIVAP-CRBSI风险的强大预测模型。实施该模型可能有助于医疗保健提供者做出明智的决策，从而有可能改善患者的治疗结果。
