Introduction: Peripherally inserted central catheters (PICC) are commonly used in cancer patients, but occlusion is a frequent complication. Early prediction of the occlusion risk can guide timely interventions and improve patient outcomes. Objective: This study develops and validates a machine-learning model to predict the PICC occlusion risk in cancer patients using clinical data from electronic medical records. Methodology: In this retrospective, single-center study, data from cancer patients with PICC lines were analyzed. Three machine learning algorithms-logistic regression, random forest, and XGBoost-were used to predict the occlusion risk. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). Key risk factors, including patient demographics, clinical conditions, and catheter maintenance practices, were incorporated. Results: XGBoost outperformed the other models, achieving AUC values of 0.909 in the training cohort and 0.759 in the validation cohort. Key predictors of PICC occlusion included catheter duration, electrolyte disturbances, the chemotherapy drug type, and the insertion length. SHAP analysis provided transparent model interpretation. Conclusion: The XGBoost model effectively predicts the PICC occlusion risk and identifies key predictors. While limited by its retrospective design, the study suggests the potential for clinical integration to improve patient outcomes. Further prospective studies are needed.
基金:
National Natural Science Foundation of China Young Scientists Fund Project [72304060]; Special Research Project on Venous Therapy by China Nursing Magazine [ZLHLZZS-202303]
第一作者机构:[1]Univ Elect Sci & Technol China, Sichuan Canc Hosp & Inst, Sichuan Clin Res Ctr Canc, Sichuan Canc Ctr,Dept Nursing, Chengdu, Peoples R China
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
Gao Li,Feng Jingjing,Gao Yu,et al.XGBoost-based model for predicting PICC occlusion risk in cancer patients: Insights from SHAP analysis[J].ALEXANDRIA ENGINEERING JOURNAL.2025,123:436-447.doi:10.1016/j.aej.2025.03.089.
APA:
Gao, Li,Feng, Jingjing,Gao, Yu,Luo, Lei,Jiang, Hongxiu...&Guo, Ling.(2025).XGBoost-based model for predicting PICC occlusion risk in cancer patients: Insights from SHAP analysis.ALEXANDRIA ENGINEERING JOURNAL,123,
MLA:
Gao, Li,et al."XGBoost-based model for predicting PICC occlusion risk in cancer patients: Insights from SHAP analysis".ALEXANDRIA ENGINEERING JOURNAL 123.(2025):436-447