To develop, validate, and compare early warning models of the 30-day mortality risk for patients with malignant biliary obstruction (MBO) undergoing percutaneous transhepatic biliary stent placement (PTBS).
Materials and Methods
Between January 2013 and October 2018, this multicenter retrospective study included 299 patients with MBOs who underwent PTBS. The training set consisted of 166 patients from four cohorts, and another two independent cohorts were allocated as external validation sets A and B with 75 patients and 58 patients, respectively. A logistic model and an artificial neural network (ANN) model were developed to predict the risk of 30-day mortality after PTBS. The predictive performance of these two models was validated internally and externally.
The ANN model had higher values of area under the curve than the logistic model in the training set (0.819 vs 0.797), especially in the validation sets A (0.802 vs 0.714) and B (0.732 vs 0.568). Both models had high accuracy in the three sets (75.9–83.1%). Along with a high specificity, the ANN model improved the sensitivity. The net reclassification improvement and integrated discrimination improvement also demonstrated that the ANN model led to improvements in predictive ability compared with the logistic model.
Early warning models were proposed to predict the risk of 30-day mortality after PTBS in patients with MBO. The ANN model has higher accuracy and better generalizability than the logistic model.
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Artificial neural network
Malignant biliary obstruction
Percutaneous transhepatic biliary stent placement
Area under the curve
Net reclassification improvement
Integrated discrimination improvement
Eastern Cooperative Oncology Group
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The authors thank Dr. Qi Zhang, Dr. Yong Wang, and Dr. Jun-Ying Wang from Zhongda Hospital, Southeast University, Nanjing, China, for their work in the management of patients. The authors thank Dr. Bo Peng from Yunnan Tumor Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China, Dr. Jing-Jing Song from Lishui Central Hospital, Wenzhou Medical University, Lishui, China, Dr. Dong Lu from Anhui Provincial Hospital, the First Affiliated Hospital of University of Science and Technology of China (USTC), Hefei, China, Dr. Wu-Jie Wang from the Second Hospital of Shandong University, Jinan, China, and Dr. Ning Wei from Affiliated Hospital of Xuzhou Medical University, Xuzhou, China, for their efforts in the follow-up of patients.
This study was supported by the National Key Scientific Instrument and Equipment Development Projects of China (81827805), Innovation Platform of Jiangsu Provincial Medical Center (YXZXA2016005), and National Natural Science Foundation of China (81520108015, 81671796). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflict of interest
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
The requirement to obtain informed consent was waived due to the retrospective nature of this study.
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Zhou, HF., Lu, J., Zhu, HD. et al. Early Warning Models to Estimate the 30-Day Mortality Risk After Stent Placement for Patients with Malignant Biliary Obstruction. Cardiovasc Intervent Radiol 42, 1751–1759 (2019). https://doi.org/10.1007/s00270-019-02331-5
- Malignant biliary obstruction
- Biliary stent
- 30-Day mortality
- Artificial neural network