Fu Jianqin, Cai Weifeng, Zeng Bangwei, He Lijuan, Bao Liqun, Lin Zhaodi, Lin Fang, Hu Wenjuan, Lin Linying, Huang Hanying, Zheng Suhui, Chen Liyuan, Zhou Wei, Lin Yanjuan, Fu Fangmeng
Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China.
Int J Nurs Stud. 2022 Nov;135:104341. doi: 10.1016/j.ijnurstu.2022.104341. Epub 2022 Aug 8.
Peripherally inserted central catheters have been extensively applied in clinical practices. However, they are associated with an increased risk of thrombosis. To improve patient care, it is critical to timely identify patients at risk of developing peripherally inserted central catheter-related thrombosis. Artificial neural networks have been successfully used in many areas of clinical events prediction and affected clinical decisions and practice.
To develop and validate a novel clinical model based on artificial neural network for predicting peripherally inserted central catheter-related thrombosis in breast cancer patients who underwent chemotherapy and determine whether it may improve the prediction performance compared with the logistic regression model.
A prospective cohort study.
A large general hospital in Fujian Province, China.
One thousand eight hundred and forty-four breast cancer patients with peripherally inserted central catheters placement for chemotherapy were eligible for the study.
The dataset was divided into a training set (N = 1497) and an independent validation set (N = 347). The synthetic minority oversampling technique (SMOTE) was used to handle the effect of imbalance class. Both the artificial neural network and logistic regression models were then developed on the training set with and without SMOTE, respectively. The performance of each model was evaluated on the validation set using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
Of the 1844 enrolled patients, 256 (13.9%) were diagnosed with peripherally inserted central catheter-related thrombosis. Predictive models were constructed in the training set and assessed in the validation set. Eight factors were selected as input variables to develop the artificial neural network model. Without SMOTE, the artificial neural network model (AUC = 0.725) outperformed the logistic regression model (AUC = 0.670, p = 0.039). SMOTE improved the performance of both two models based on AUC. With the SMOTE sampling, the artificial neural network model performed the best across all evaluated models, the AUC value remained statistically better than that of the logistic regression model (0.742 vs. 0.675, p = 0.004).
Artificial neural network model can effectively predict peripherally inserted central catheter-related thrombosis in breast cancer patients receiving chemotherapy. Identifying high-risk groups with peripherally inserted central catheter-related thrombosis can provide close monitoring and an opportune time for intervention.
外周静脉穿刺中心静脉导管已在临床实践中广泛应用。然而，它们与血栓形成风险增加相关。为改善患者护理，及时识别有发生外周静脉穿刺中心静脉导管相关血栓形成风险的患者至关重要。人工神经网络已成功用于临床事件预测的许多领域，并影响临床决策和实践。
开发并验证一种基于人工神经网络的新型临床模型，用于预测接受化疗的乳腺癌患者外周静脉穿刺中心静脉导管相关血栓形成，并确定与逻辑回归模型相比它是否能提高预测性能。
一项前瞻性队列研究。
中国福建省的一家大型综合医院。
1844例接受外周静脉穿刺中心静脉导管置入化疗的乳腺癌患者符合研究条件。
数据集分为训练集（N = 1497）和独立验证集（N = 347）。采用合成少数过采样技术（SMOTE）处理类别不平衡的影响。然后分别在有和没有SMOTE的训练集上开发人工神经网络和逻辑回归模型。使用准确性、敏感性、特异性和受试者工作特征曲线下面积（AUC）在验证集上评估每个模型的性能。
在1844例入组患者中，256例（13.9%）被诊断为外周静脉穿刺中心静脉导管相关血栓形成。在训练集中构建预测模型并在验证集中进行评估。选择8个因素作为输入变量来开发人工神经网络模型。在没有SMOTE的情况下，人工神经网络模型（AUC = 0.725）优于逻辑回归模型（AUC = 0.670，p = 0.039）。SMOTE基于AUC改善了两个模型的性能。通过SMOTE采样，人工神经网络模型在所有评估模型中表现最佳，AUC值在统计学上仍优于逻辑回归模型（0.742对0.675，p = 0.004）。
人工神经网络模型可有效预测接受化疗的乳腺癌患者外周静脉穿刺中心静脉导管相关血栓形成。识别有外周静脉穿刺中心静脉导管相关血栓形成的高危人群可为密切监测和适时干预提供时机。
