Heart rate variability as a marker of recovery from critical illness in children
The purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV de...
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| creator | Marsillio, Lauren E Manghi, Tomas Carroll, Michael S Balmert, Lauren C Wainwright, Mark S |
| description | The purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV derived from electrocardiogram (ECG) data from bedside monitors can be calculated in critically-ill children using a real-time, streaming analytics platform.
This was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children's hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).
HRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (p |
| doi_str_mv | 10.1371/journal.pone.0215930 |
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This was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children's hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).
HRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (p<0.001 for all three measures). In models exploring the relationship between time from admission and log HRV values, the predicted average HRV remained consistently higher in the last 24 hours of PICU stay compared to the first 24 hours.
HRV was significantly lower in the first 24 hours compared to the 24 hours preceding PICU discharge, after resolution of critical illness. This demonstrates that it is feasible to detect changes in HRV using an automated, streaming analytics platform. Continuous tracking of HRV may serve as a marker of recovery in critically ill children.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0215930</identifier><identifier>PMID: 31100075</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Biomarkers ; Change detection ; Child ; Child, Preschool ; Children ; Comparative analysis ; Critical care ; Critical Illness ; Critically ill children ; Demographic aspects ; Discharge ; EKG ; Electrocardiography ; Feasibility studies ; Female ; Health aspects ; Heart Rate ; Homeostasis ; Hospitals ; Humans ; Hypertension ; Illnesses ; Infant ; Infant, Newborn ; Intensive care ; Intensive Care Units ; Intervals ; Male ; Medicine and Health Sciences ; Methods ; Mortality ; Patients ; Pediatric diseases ; Pediatric intensive care ; Pediatrics ; People and Places ; Physiology ; Preventive medicine ; Prognosis ; Real time ; Recovery ; Recovery (Medical) ; Recovery of Function ; Research and Analysis Methods ; Retrospective Studies ; Streaming ; Trauma ; Variability ; Ventilators</subject><ispartof>PloS one, 2019-05, Vol.14 (5), p.e0215930-e0215930</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Marsillio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Marsillio et al 2019 Marsillio et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-32e2eb1708a77e6532ae6ead4504dacbee537a6882caff43194a0cf94d8061333</citedby><cites>FETCH-LOGICAL-c692t-32e2eb1708a77e6532ae6ead4504dacbee537a6882caff43194a0cf94d8061333</cites><orcidid>0000-0003-3166-6318</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524820/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524820/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2104,2930,23873,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31100075$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Patman, Shane</contributor><creatorcontrib>Marsillio, Lauren E</creatorcontrib><creatorcontrib>Manghi, Tomas</creatorcontrib><creatorcontrib>Carroll, Michael S</creatorcontrib><creatorcontrib>Balmert, Lauren C</creatorcontrib><creatorcontrib>Wainwright, Mark S</creatorcontrib><title>Heart rate variability as a marker of recovery from critical illness in children</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV derived from electrocardiogram (ECG) data from bedside monitors can be calculated in critically-ill children using a real-time, streaming analytics platform.
This was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children's hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).
HRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (p<0.001 for all three measures). In models exploring the relationship between time from admission and log HRV values, the predicted average HRV remained consistently higher in the last 24 hours of PICU stay compared to the first 24 hours.
HRV was significantly lower in the first 24 hours compared to the 24 hours preceding PICU discharge, after resolution of critical illness. This demonstrates that it is feasible to detect changes in HRV using an automated, streaming analytics platform. Continuous tracking of HRV may serve as a marker of recovery in critically ill children.</description><subject>Adolescent</subject><subject>Biomarkers</subject><subject>Change detection</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Comparative analysis</subject><subject>Critical care</subject><subject>Critical Illness</subject><subject>Critically ill children</subject><subject>Demographic aspects</subject><subject>Discharge</subject><subject>EKG</subject><subject>Electrocardiography</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Health aspects</subject><subject>Heart Rate</subject><subject>Homeostasis</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Illnesses</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Intensive care</subject><subject>Intensive Care Units</subject><subject>Intervals</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Mortality</subject><subject>Patients</subject><subject>Pediatric diseases</subject><subject>Pediatric intensive care</subject><subject>Pediatrics</subject><subject>People and Places</subject><subject>Physiology</subject><subject>Preventive medicine</subject><subject>Prognosis</subject><subject>Real time</subject><subject>Recovery</subject><subject>Recovery (Medical)</subject><subject>Recovery of Function</subject><subject>Research and Analysis Methods</subject><subject>Retrospective Studies</subject><subject>Streaming</subject><subject>Trauma</subject><subject>Variability</subject><subject>Ventilators</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2LEzEUhgdR3LX6D0QHBNGL1nzMJDM3wrKoW1hY8es2nGZO2tR0UpOZYv-9qZ1dOrIXkouE5DlvzsebZc8pmVEu6bu170MLbrb1Lc4Io2XNyYPsnNacTQUj_OHJ-Sx7EuOakJJXQjzOzjilhBBZnmefrxBClwfoMN9BsLCwznb7HGIO-QbCTwy5N3lA7XcY9rkJfpPrYDurweXWuRZjzG2b65V1TcD2afbIgIv4bNgn2fePH75dXk2vbz7NLy-up1rUrJtyhgwXVJIKpERRcgYoEJqiJEUDeoFYcgmiqpgGYwpO6wKINnXRVERQzvkke3nU3Tof1dCMqBhjQoqaVlUi5kei8bBW22BTOXvlwaq_Fz4sVSrdaodKmsaAwYJzKgptSljolBU0DBuzoNQkrffDb_1ig43GtgvgRqLjl9au1NLvlChZUaUJTLI3g0Dwv3qMndrYqNE5aNH3h7w5I4WsRZ3QV_-g91c3UEtIBdjW-PSvPoiqi7IqpWSVLBM1u4dKq8GN1ck5xqb7UcDbUUBiOvzdLaGPUc2_fvl_9ubHmH19wq4QXLeK3vWd9W0cg8UR1MHHGNDcNZkSdTD-bTfUwfhqMH4Ke3E6oLugW6fzP7Wg_mc</recordid><startdate>20190517</startdate><enddate>20190517</enddate><creator>Marsillio, 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rate variability as a marker of recovery from critical illness in children</title><author>Marsillio, Lauren E ; Manghi, Tomas ; Carroll, Michael S ; Balmert, Lauren C ; Wainwright, Mark S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-32e2eb1708a77e6532ae6ead4504dacbee537a6882caff43194a0cf94d8061333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Biomarkers</topic><topic>Change detection</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Children</topic><topic>Comparative analysis</topic><topic>Critical care</topic><topic>Critical Illness</topic><topic>Critically ill children</topic><topic>Demographic aspects</topic><topic>Discharge</topic><topic>EKG</topic><topic>Electrocardiography</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Health aspects</topic><topic>Heart Rate</topic><topic>Homeostasis</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Illnesses</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Intensive care</topic><topic>Intensive Care Units</topic><topic>Intervals</topic><topic>Male</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Mortality</topic><topic>Patients</topic><topic>Pediatric diseases</topic><topic>Pediatric intensive care</topic><topic>Pediatrics</topic><topic>People and Places</topic><topic>Physiology</topic><topic>Preventive medicine</topic><topic>Prognosis</topic><topic>Real time</topic><topic>Recovery</topic><topic>Recovery (Medical)</topic><topic>Recovery of Function</topic><topic>Research and Analysis Methods</topic><topic>Retrospective 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Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Marsillio, Lauren E</au><au>Manghi, Tomas</au><au>Carroll, Michael S</au><au>Balmert, Lauren C</au><au>Wainwright, Mark S</au><au>Patman, Shane</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Heart rate variability as a marker of recovery from critical illness in children</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-05-17</date><risdate>2019</risdate><volume>14</volume><issue>5</issue><spage>e0215930</spage><epage>e0215930</epage><pages>e0215930-e0215930</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV derived from electrocardiogram (ECG) data from bedside monitors can be calculated in critically-ill children using a real-time, streaming analytics platform.
This was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children's hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).
HRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (p<0.001 for all three measures). In models exploring the relationship between time from admission and log HRV values, the predicted average HRV remained consistently higher in the last 24 hours of PICU stay compared to the first 24 hours.
HRV was significantly lower in the first 24 hours compared to the 24 hours preceding PICU discharge, after resolution of critical illness. This demonstrates that it is feasible to detect changes in HRV using an automated, streaming analytics platform. Continuous tracking of HRV may serve as a marker of recovery in critically ill children.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31100075</pmid><doi>10.1371/journal.pone.0215930</doi><tpages>e0215930</tpages><orcidid>https://orcid.org/0000-0003-3166-6318</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Biomarkers Change detection Child Child, Preschool Children Comparative analysis Critical care Critical Illness Critically ill children Demographic aspects Discharge EKG Electrocardiography Feasibility studies Female Health aspects Heart Rate Homeostasis Hospitals Humans Hypertension Illnesses Infant Infant, Newborn Intensive care Intensive Care Units Intervals Male Medicine and Health Sciences Methods Mortality Patients Pediatric diseases Pediatric intensive care Pediatrics People and Places Physiology Preventive medicine Prognosis Real time Recovery Recovery (Medical) Recovery of Function Research and Analysis Methods Retrospective Studies Streaming Trauma Variability Ventilators |
| title | Heart rate variability as a marker of recovery from critical illness in children |
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