Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring

Identifying the low risk patient in surgical intensive and intermediate care units using... BackgroundContinuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Surgery Elsevier

Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring

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Publisher
Elsevier
Copyright
Copyright © 2017 The Authors
ISSN
0039-6060
D.O.I.
10.1016/j.surg.2017.08.022
Publisher site
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Abstract

BackgroundContinuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit.

Journal

SurgeryElsevier

Published: Apr 1, 2018

References

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