Literature DB >> 26342251

Heart rate dynamics preceding hemorrhage in the intensive care unit.

Travis J Moss1, Matthew T Clark1, Douglas E Lake2, J Randall Moorman1, J Forrest Calland3.   

Abstract

Occult hemorrhage in surgical/trauma intensive care unit (STICU) patients is common and may lead to circulatory collapse. Continuous electrocardiography (ECG) monitoring may allow for early identification and treatment, and could improve outcomes. We studied 4,259 consecutive admissions to the STICU at the University of Virginia Health System. We collected ECG waveform data captured by bedside monitors and calculated linear and non-linear measures of the RR interbeat intervals. We tested the hypothesis that a transfusion requirement of 3 or more PRBC transfusions in a 24 hour period is preceded by dynamical changes in these heart rate measures and performed logistic regression modeling. We identified 308 hemorrhage events. A multivariate model including heart rate, standard deviation of the RR intervals, detrended fluctuation analysis, and local dynamics density had a C-statistic of 0.62. Earlier detection of hemorrhage might improve outcomes by allowing earlier resuscitation in STICU patients.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Blood transfusion; Critical care; Heart rate dynamics; Hemorrhage; Physiologic monitoring; Statistical risk model

Mesh:

Year:  2015        PMID: 26342251     DOI: 10.1016/j.jelectrocard.2015.08.007

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  10 in total

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2.  Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

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Review 3.  Data Science for Child Health.

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4.  Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.

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Journal:  J Clin Monit Comput       Date:  2018-08-18       Impact factor: 1.977

5.  Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.

Authors:  Travis J Moss; Matthew T Clark; James Forrest Calland; Kyle B Enfield; John D Voss; Douglas E Lake; J Randall Moorman
Journal:  PLoS One       Date:  2017-08-03       Impact factor: 3.240

6.  New-Onset Atrial Fibrillation in the Critically Ill.

Authors:  Travis J Moss; James Forrest Calland; Kyle B Enfield; Diana C Gomez-Manjarres; Caroline Ruminski; John P DiMarco; Douglas E Lake; J Randall Moorman
Journal:  Crit Care Med       Date:  2017-05       Impact factor: 7.598

7.  Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.

Authors:  Jessica Keim-Malpass; Liza P Moorman
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8.  Heart rate fluctuation predicts mortality in critically ill patients in the intensive care unit: a retrospective cohort study.

Authors:  Qi Guo; Zhanchao Xiao; Maohuan Lin; Guiyi Yuan; Qiong Qiu; Ying Yang; Huiying Zhao; Yuling Zhang; Shuxian Zhou; Jingfeng Wang
Journal:  Ann Transl Med       Date:  2021-02

Review 9.  The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.

Authors:  J Randall Moorman
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Authors:  Jessica Keim-Malpass; Sarah J Ratcliffe; Liza P Moorman; Matthew T Clark; Katy N Krahn; Oliver J Monfredi; Susan Hamil; Gholamreza Yousefvand; J Randall Moorman; Jamieson M Bourque
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  10 in total

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