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CardioVascular and Interventional Radiology

, Volume 42, Issue 12, pp 1751–1759 | Cite as

Early Warning Models to Estimate the 30-Day Mortality Risk After Stent Placement for Patients with Malignant Biliary Obstruction

  • Hai-Feng Zhou
  • Jian Lu
  • Hai-Dong Zhu
  • Jin-He Guo
  • Ming Huang
  • Jian-Song Ji
  • Wei-Fu Lv
  • Yu-Liang Li
  • Hao Xu
  • Li Chen
  • Guang-Yu Zhu
  • Gao-Jun TengEmail author
Clinical Investigation Biliary
  • 97 Downloads
Part of the following topical collections:
  1. Biliary

Abstract

Purpose

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.

Results

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.

Conclusions

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.

Keywords

Malignant biliary obstruction Biliary stent 30-Day mortality Prediction Artificial neural network 

Abbreviations

ANN

Artificial neural network

MBO

Malignant biliary obstruction

PTBS

Percutaneous transhepatic biliary stent placement

AUC

Area under the curve

NRI

Net reclassification improvement

IDI

Integrated discrimination improvement

ECOG

Eastern Cooperative Oncology Group

NLR

Neutrophil-to-lymphocyte ratio

CA

Cancer antigen

CEA

Carcinoembryonic antigen

OR

Odds ratio

CI

Confidence interval

Notes

Acknowledgments

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.

Author Contributions

All authors contributed to review and critical revision of the manuscript and approved the final version of the manuscript. GJT, HFZ, JL, HDZ, and JHG contributed to study concept and design. JHG, MH, JSJ, WFL, YLL, HX, LC, and GYZ contributed to acquisition of data. HFZ contributed to drafting of the manuscript. GJT, HDZ, and JHG contributed to analysis and interpretation of data. JL contributed to statistical analysis. GJT, HDZ, and JHG supervised and oversaw the study.

Funding

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.

Compliance with Ethical Standards

Conflict of interest

None.

Ethical Approval

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.

Informed Consent

The requirement to obtain informed consent was waived due to the retrospective nature of this study.

Supplementary material

270_2019_2331_MOESM1_ESM.docx (358 kb)
Supplementary file1 (DOCX 357 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) 2019

Authors and Affiliations

  1. 1.Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical SchoolSoutheast UniversityNanjingChina
  2. 2.Department of Minimally Invasive Interventional Radiology, Yunnan Tumor HospitalThe Third Affiliated Hospital of Kunming Medical UniversityKunmingChina
  3. 3.Department of Radiology, Lishui Central HospitalWenzhou Medical UniversityLishuiChina
  4. 4.Department of Interventional Radiology, Anhui Provincial HospitalThe First Affiliated Hospital of University of Science and Technology of China (USTC)HefeiChina
  5. 5.Department of Interventional MedicineThe Second Hospital of Shandong UniversityJinanChina
  6. 6.Department of Interventional RadiologyAffiliated Hospital of Xuzhou Medical UniversityXuzhouChina

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