Literature DB >> 31208946

Risk Prediction for Early Biliary Infection after Percutaneous Transhepatic Biliary Stent Placement in Malignant Biliary Obstruction.

Hai-Feng Zhou1, Ming Huang2, Jian-Song Ji3, Hai-Dong Zhu1, Jian Lu1, Jin-He Guo1, Li Chen1, Bin-Yan Zhong1, Guang-Yu Zhu1, Gao-Jun Teng4.   

Abstract

PURPOSE: To establish a nomogram for predicting the occurrence of early biliary infection (EBI) after percutaneous transhepatic biliary stent (PTBS) placement in malignant biliary obstruction (MBO).
MATERIALS AND METHODS: In this multicenter study, patients treated with PTBS for MBO were allocated to a training cohort or a validation cohort. The independent risk factors for EBI selected by multivariate analyses in the training cohort were used to develop a predictive nomogram. An artificial neural network was applied to assess the importance of these factors in predicting EBI. The predictive accuracy of this nomogram was determined by concordance index (c-index) and a calibration plot, both internally and externally.
RESULTS: A total of 243 patients (training cohort: n = 182; validation cohort: n = 61) were included in this study. The independent risk factors were length of obstruction (odds ratio [OR], 1.061; 95% confidence interval [CI], 1.013-1.111; P = .012), diabetes (OR, 5.070; 95% CI, 1.917-13.412; P = .001), location of obstruction (OR, 2.283; 95% CI, 1.012-5.149; P = .047), and previous surgical or endoscopic intervention (OR, 3.968; 95% CI, 1.709-9.217; P = .001), which were selected into the nomogram. The c-index values showed good predictive performance in the training and validation cohorts (0.792 and 0.802, respectively). The optimum cutoff value of risk was 0.25.
CONCLUSIONS: The nomogram can facilitate the early and accurate prediction of EBI in patients with MBO who underwent PTBS. Patients with high risk (> 0.25) should be administered more effective prophylactic antibiotics and undergo closer monitoring.
Copyright © 2019 SIR. Published by Elsevier Inc. All rights reserved.

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Year:  2019        PMID: 31208946     DOI: 10.1016/j.jvir.2019.03.001

Source DB:  PubMed          Journal:  J Vasc Interv Radiol        ISSN: 1051-0443            Impact factor:   3.464


  3 in total

Review 1.  Application of artificial intelligence in pancreaticobiliary diseases.

Authors:  Hemant Goyal; Rupinder Mann; Zainab Gandhi; Abhilash Perisetti; Zhongheng Zhang; Neil Sharma; Shreyas Saligram; Sumant Inamdar; Benjamin Tharian
Journal:  Ther Adv Gastrointest Endosc       Date:  2021-02-15

2.  Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.

Authors:  Alan Brnabic; Lisa M Hess
Journal:  BMC Med Inform Decis Mak       Date:  2021-02-15       Impact factor: 2.796

3.  Analysis of the Bacterial Spectrum and Key Clinical Factors of Biliary Tract Infection in Patients with Malignant Obstructive Jaundice after PTCD.

Authors:  Dongjuan Xing; Weihua Song; Shaojuan Gong; Aimin Xu; Bo Zhai
Journal:  Dis Markers       Date:  2022-07-30       Impact factor: 3.464

  3 in total

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