Literature DB >> 26246167

A targeted real-time early warning score (TREWScore) for septic shock.

Katharine E Henry1, David N Hager2, Peter J Pronovost3, Suchi Saria4.   

Abstract

Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed "TREWScore," a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality.
Copyright © 2015, American Association for the Advancement of Science.

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Year:  2015        PMID: 26246167     DOI: 10.1126/scitranslmed.aab3719

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   17.956


  143 in total

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4.  Improving Acute GI Bleeding Management Through Artificial Intelligence: Unnatural Selection?

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5.  Automated early warning system for septic shock: the new way to achieve intensive care unit quality improvement?

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Journal:  Ann Transl Med       Date:  2017-01

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7.  Technology and Innovation: The Game Changer of the Next Decade.

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8.  Evaluating performance of early warning indices to predict physiological instabilities.

Authors:  Christopher G Scully; Chathuri Daluwatte
Journal:  J Biomed Inform       Date:  2017-09-20       Impact factor: 6.317

9.  A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.

Authors:  Nathan C Hurley; Erica S Spatz; Harlan M Krumholz; Roozbeh Jafari; Bobak J Mortazavi
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Review 10.  A Review of Predictive Analytics Solutions for Sepsis Patients.

Authors:  Andrew K Teng; Adam B Wilcox
Journal:  Appl Clin Inform       Date:  2020-05-27       Impact factor: 2.342

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