Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis

Autor: Sihai Dave Zhao, Gregory L. Damhorst, Muhammad S. Khan, Manish Patel, Samuel Wachspress, Benjamin Davis, Rashid Bashir, Syed Anwaruddin, Michael Rappleye, Bobby Reddy, Sumeet Soni, Harsh Rawal, Ishan Taneja, Gillian Smith, Jackson Winter, Muhammad Ajmal, Zachary Price, Karen White, James Kumar, Tor W. Jensen, Umer Hassan, Jay Patel, Tanmay Ghonge, Ryan Healey, Raiya Sarwar, Ruoqing Zhu
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Scientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/s41598-017-09766-1
Popis: 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.
Databáze: OpenAIRE
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