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Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis
Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply mac...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589821/ https://www.ncbi.nlm.nih.gov/pubmed/28883645 http://dx.doi.org/10.1038/s41598-017-09766-1 |
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author | Taneja, Ishan Reddy, Bobby Damhorst, Gregory Dave Zhao, Sihai Hassan, Umer Price, Zachary Jensen, Tor Ghonge, Tanmay Patel, Manish Wachspress, Samuel Winter, Jackson Rappleye, Michael Smith, Gillian Healey, Ryan Ajmal, Muhammad Khan, Muhammad Patel, Jay Rawal, Harsh Sarwar, Raiya Soni, Sumeet Anwaruddin, Syed Davis, Benjamin Kumar, James White, Karen Bashir, Rashid Zhu, Ruoqing |
author_facet | Taneja, Ishan Reddy, Bobby Damhorst, Gregory Dave Zhao, Sihai Hassan, Umer Price, Zachary Jensen, Tor Ghonge, Tanmay Patel, Manish Wachspress, Samuel Winter, Jackson Rappleye, Michael Smith, Gillian Healey, Ryan Ajmal, Muhammad Khan, Muhammad Patel, Jay Rawal, Harsh Sarwar, Raiya Soni, Sumeet Anwaruddin, Syed Davis, Benjamin Kumar, James White, Karen Bashir, Rashid Zhu, Ruoqing |
author_sort | Taneja, Ishan |
collection | PubMed |
description | Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention. |
format | Online Article Text |
id | pubmed-5589821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55898212017-09-13 Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis Taneja, Ishan Reddy, Bobby Damhorst, Gregory Dave Zhao, Sihai Hassan, Umer Price, Zachary Jensen, Tor Ghonge, Tanmay Patel, Manish Wachspress, Samuel Winter, Jackson Rappleye, Michael Smith, Gillian Healey, Ryan Ajmal, Muhammad Khan, Muhammad Patel, Jay Rawal, Harsh Sarwar, Raiya Soni, Sumeet Anwaruddin, Syed Davis, Benjamin Kumar, James White, Karen Bashir, Rashid Zhu, Ruoqing Sci Rep Article Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention. Nature Publishing Group UK 2017-09-07 /pmc/articles/PMC5589821/ /pubmed/28883645 http://dx.doi.org/10.1038/s41598-017-09766-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Taneja, Ishan Reddy, Bobby Damhorst, Gregory Dave Zhao, Sihai Hassan, Umer Price, Zachary Jensen, Tor Ghonge, Tanmay Patel, Manish Wachspress, Samuel Winter, Jackson Rappleye, Michael Smith, Gillian Healey, Ryan Ajmal, Muhammad Khan, Muhammad Patel, Jay Rawal, Harsh Sarwar, Raiya Soni, Sumeet Anwaruddin, Syed Davis, Benjamin Kumar, James White, Karen Bashir, Rashid Zhu, Ruoqing Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title | Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title_full | Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title_fullStr | Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title_full_unstemmed | Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title_short | Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis |
title_sort | combining biomarkers with emr data to identify patients in different phases of sepsis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589821/ https://www.ncbi.nlm.nih.gov/pubmed/28883645 http://dx.doi.org/10.1038/s41598-017-09766-1 |
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