Literature DB >> 25229393

Local dynamics of heart rate: detection and prognostic implications.

Travis J Moss1, Douglas E Lake, J Randall Moorman.   

Abstract

The original observation that reduced heart rate variability (HRV) confers poor prognosis after myocardial infarction has been followed by many studies of heart rate dynamics. We tested the hypothesis that an entropy-based local dynamics measure gave prognostic information in ambulatory patients undergoing 24-h electrocardiography. In this context, entropy is the probability that short templates will find matches in the time series. We studied RR interval time series from 24-h Holter monitors of 1564 consecutive patients over age 39. We generated histograms of the count of templates as a function of the number of templates matches in short RR interval time series, and found characteristic appearance of histograms for atrial fibrillation, sinus rhythm with normal HRV, and sinus rhythm with reduced HRV and premature ventricular contractions (PVCs). We developed statistical models to detect the abnormal dynamic phenotype of reduced HRV with PVCs and fashioned a local dynamics score (LDs) that, after controlling for age, added more prognostic information than other standard risk factors and common HRV metrics, including, to our surprise, the PVC count and the HRV of normal-to-normal intervals. Addition of the LDs to a predictive model using standard risk factors significantly increased the ROC area and the net reclassification improvement was 27%. We conclude that abnormal local dynamics of heart rate confer adverse prognosis in patients undergoing 24-h ambulatory electrocardiography.

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Year:  2014        PMID: 25229393     DOI: 10.1088/0967-3334/35/10/1929

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  7 in total

1.  Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All.

Authors:  Jacob N Blackwell; Jessica Keim-Malpass; Matthew T Clark; Rebecca L Kowalski; Salim N Najjar; Jamieson M Bourque; Douglas E Lake; J Randall Moorman
Journal:  Crit Care Explor       Date:  2020-05-11

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

Authors:  Travis J Moss; Douglas E Lake; J Forrest Calland; Kyle B Enfield; John B Delos; Karen D Fairchild; J Randall Moorman
Journal:  Crit Care Med       Date:  2016-09       Impact factor: 7.598

3.  Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks.

Authors:  Teeranan Pokaprakarn; Rebecca R Kitzmiller; J Randall Moorman; Doug E Lake; Ashok K Krishnamurthy; Michael R Kosorok
Journal:  IEEE J Biomed Health Inform       Date:  2022-02-04       Impact factor: 7.021

4.  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

5.  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

6.  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
Journal:  Int J Nurs Stud Adv       Date:  2021-01-05

7.  Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.

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
Journal:  JMIR Res Protoc       Date:  2021-07-02
  7 in total

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