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Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis

BACKGROUND: Acute cholangitis is a life-threatening bacterial infection of the biliary tract. Main focus of this study was to create a useful risk prediction model that helps physicians to assign patients with acute cholangitis into different management groups. METHODS: 981 cholangitis episodes from...

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Autores principales: Schneider, Jochen, Hapfelmeier, Alexander, Thöres, Sieglinde, Obermeier, Andreas, Schulz, Christoph, Pförringer, Dominik, Nennstiel, Simon, Spinner, Christoph, Schmid, Roland M., Algül, Hana, Huber, Wolfgang, Weber, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746925/
https://www.ncbi.nlm.nih.gov/pubmed/26860903
http://dx.doi.org/10.1186/s12876-016-0428-1
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author Schneider, Jochen
Hapfelmeier, Alexander
Thöres, Sieglinde
Obermeier, Andreas
Schulz, Christoph
Pförringer, Dominik
Nennstiel, Simon
Spinner, Christoph
Schmid, Roland M.
Algül, Hana
Huber, Wolfgang
Weber, Andreas
author_facet Schneider, Jochen
Hapfelmeier, Alexander
Thöres, Sieglinde
Obermeier, Andreas
Schulz, Christoph
Pförringer, Dominik
Nennstiel, Simon
Spinner, Christoph
Schmid, Roland M.
Algül, Hana
Huber, Wolfgang
Weber, Andreas
author_sort Schneider, Jochen
collection PubMed
description BACKGROUND: Acute cholangitis is a life-threatening bacterial infection of the biliary tract. Main focus of this study was to create a useful risk prediction model that helps physicians to assign patients with acute cholangitis into different management groups. METHODS: 981 cholangitis episodes from 810 patients were analysed retrospectively at a German tertiary center. RESULTS: Out of eleven investigated statistical models fit to 22 predictors, the Random Forest model achieved the best (cross-)validated performance to predict mortality. The receiver operating characteristics (ROC) curve revealed a mean area under the curve (AUC) of 91.5 %. Dependent on the calculated mortality risk, we propose to stratify patients with acute cholangitis into a high and low risk group. The mean sensitivity, specificity, positive and negative predictive value of the corresponding optimal cutpoint were 82.9 %, 85.1 %, 19.0 % and 99.3 %, respectively. All of these results emerge from nested (cross-)validation and are supposed to reflect the model’s performance expected for external data. An implementation of our risk prediction model including the specific treatment recommendations adopted from the Tokyo guidelines is available on http://www2.imse.med.tum.de:3838/. CONCLUSION: Our risk prediction model for mortality appears promising to stratify patients with acute cholangitis into different management groups. Additional validation of its performance should be provided by further prospective trails.
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spelling pubmed-47469252016-02-10 Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis Schneider, Jochen Hapfelmeier, Alexander Thöres, Sieglinde Obermeier, Andreas Schulz, Christoph Pförringer, Dominik Nennstiel, Simon Spinner, Christoph Schmid, Roland M. Algül, Hana Huber, Wolfgang Weber, Andreas BMC Gastroenterol Research Article BACKGROUND: Acute cholangitis is a life-threatening bacterial infection of the biliary tract. Main focus of this study was to create a useful risk prediction model that helps physicians to assign patients with acute cholangitis into different management groups. METHODS: 981 cholangitis episodes from 810 patients were analysed retrospectively at a German tertiary center. RESULTS: Out of eleven investigated statistical models fit to 22 predictors, the Random Forest model achieved the best (cross-)validated performance to predict mortality. The receiver operating characteristics (ROC) curve revealed a mean area under the curve (AUC) of 91.5 %. Dependent on the calculated mortality risk, we propose to stratify patients with acute cholangitis into a high and low risk group. The mean sensitivity, specificity, positive and negative predictive value of the corresponding optimal cutpoint were 82.9 %, 85.1 %, 19.0 % and 99.3 %, respectively. All of these results emerge from nested (cross-)validation and are supposed to reflect the model’s performance expected for external data. An implementation of our risk prediction model including the specific treatment recommendations adopted from the Tokyo guidelines is available on http://www2.imse.med.tum.de:3838/. CONCLUSION: Our risk prediction model for mortality appears promising to stratify patients with acute cholangitis into different management groups. Additional validation of its performance should be provided by further prospective trails. BioMed Central 2016-02-09 /pmc/articles/PMC4746925/ /pubmed/26860903 http://dx.doi.org/10.1186/s12876-016-0428-1 Text en © Schneider et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Schneider, Jochen
Hapfelmeier, Alexander
Thöres, Sieglinde
Obermeier, Andreas
Schulz, Christoph
Pförringer, Dominik
Nennstiel, Simon
Spinner, Christoph
Schmid, Roland M.
Algül, Hana
Huber, Wolfgang
Weber, Andreas
Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title_full Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title_fullStr Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title_full_unstemmed Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title_short Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
title_sort mortality risk for acute cholangitis (mac): a risk prediction model for in-hospital mortality in patients with acute cholangitis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746925/
https://www.ncbi.nlm.nih.gov/pubmed/26860903
http://dx.doi.org/10.1186/s12876-016-0428-1
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