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Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection fr...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301583/ https://www.ncbi.nlm.nih.gov/pubmed/33786999 http://dx.doi.org/10.1111/cts.13030 |
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author | Taneja, Ishan Damhorst, Gregory L. Lopez‐Espina, Carlos Zhao, Sihai Dave Zhu, Ruoqing Khan, Shah White, Karen Kumar, James Vincent, Andrew Yeh, Leon Majdizadeh, Shirin Weir, William Isbell, Scott Skinner, James Devanand, Manubolo Azharuddin, Syed Meenakshisundaram, Rajamurugan Upadhyay, Riddhi Syed, Anwaruddin Bauman, Thomas Devito, Joseph Heinzmann, Charles Podolej, Gregory Shen, Lanxin Timilsina, Sanjay Sharma Quinlan, Lucas Manafirasi, Setareh Valera, Enrique Reddy, Bobby Bashir, Rashid |
author_facet | Taneja, Ishan Damhorst, Gregory L. Lopez‐Espina, Carlos Zhao, Sihai Dave Zhu, Ruoqing Khan, Shah White, Karen Kumar, James Vincent, Andrew Yeh, Leon Majdizadeh, Shirin Weir, William Isbell, Scott Skinner, James Devanand, Manubolo Azharuddin, Syed Meenakshisundaram, Rajamurugan Upadhyay, Riddhi Syed, Anwaruddin Bauman, Thomas Devito, Joseph Heinzmann, Charles Podolej, Gregory Shen, Lanxin Timilsina, Sanjay Sharma Quinlan, Lucas Manafirasi, Setareh Valera, Enrique Reddy, Bobby Bashir, Rashid |
author_sort | Taneja, Ishan |
collection | PubMed |
description | Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine‐learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine‐learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30‐day mortality, and 3‐day inpatient re‐admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high‐risk groups showed significant differences in LOS (p < 0.0001), 30‐day mortality (p < 0.0001), and 30‐day inpatient readmission (p < 0.0001). In conclusion, a machine‐learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture. |
format | Online Article Text |
id | pubmed-8301583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83015832021-07-27 Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis Taneja, Ishan Damhorst, Gregory L. Lopez‐Espina, Carlos Zhao, Sihai Dave Zhu, Ruoqing Khan, Shah White, Karen Kumar, James Vincent, Andrew Yeh, Leon Majdizadeh, Shirin Weir, William Isbell, Scott Skinner, James Devanand, Manubolo Azharuddin, Syed Meenakshisundaram, Rajamurugan Upadhyay, Riddhi Syed, Anwaruddin Bauman, Thomas Devito, Joseph Heinzmann, Charles Podolej, Gregory Shen, Lanxin Timilsina, Sanjay Sharma Quinlan, Lucas Manafirasi, Setareh Valera, Enrique Reddy, Bobby Bashir, Rashid Clin Transl Sci Research Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine‐learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine‐learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30‐day mortality, and 3‐day inpatient re‐admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high‐risk groups showed significant differences in LOS (p < 0.0001), 30‐day mortality (p < 0.0001), and 30‐day inpatient readmission (p < 0.0001). In conclusion, a machine‐learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture. John Wiley and Sons Inc. 2021-05-02 2021-07 /pmc/articles/PMC8301583/ /pubmed/33786999 http://dx.doi.org/10.1111/cts.13030 Text en © 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Taneja, Ishan Damhorst, Gregory L. Lopez‐Espina, Carlos Zhao, Sihai Dave Zhu, Ruoqing Khan, Shah White, Karen Kumar, James Vincent, Andrew Yeh, Leon Majdizadeh, Shirin Weir, William Isbell, Scott Skinner, James Devanand, Manubolo Azharuddin, Syed Meenakshisundaram, Rajamurugan Upadhyay, Riddhi Syed, Anwaruddin Bauman, Thomas Devito, Joseph Heinzmann, Charles Podolej, Gregory Shen, Lanxin Timilsina, Sanjay Sharma Quinlan, Lucas Manafirasi, Setareh Valera, Enrique Reddy, Bobby Bashir, Rashid Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title | Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title_full | Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title_fullStr | Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title_full_unstemmed | Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title_short | Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis |
title_sort | diagnostic and prognostic capabilities of a biomarker and emr‐based machine learning algorithm for sepsis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301583/ https://www.ncbi.nlm.nih.gov/pubmed/33786999 http://dx.doi.org/10.1111/cts.13030 |
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