Continuous Multi-Parameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults
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{"title"=>"Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults", "type"=>"journal", "authors"=>[{"first_name"=>"Saif", "last_name"=>"Ahmad", "scopus_author_id"=>"35117513900"}, {"first_name"=>"Tim", "last_name"=>"Ramsay", "scopus_author_id"=>"8278133500"}, {"first_name"=>"Lothar", "last_name"=>"Huebsch", "scopus_author_id"=>"6701703664"}, {"first_name"=>"Sarah", "last_name"=>"Flanagan", "scopus_author_id"=>"35117805500"}, {"first_name"=>"Sheryl", "last_name"=>"McDiarmid", "scopus_author_id"=>"7007041192"}, {"first_name"=>"Izmail", "last_name"=>"Batkin", "scopus_author_id"=>"35117427600"}, {"first_name"=>"Lauralyn", "last_name"=>"McIntyre", "scopus_author_id"=>"7101992439"}, {"first_name"=>"Sudhir R.", "last_name"=>"Sundaresan", "scopus_author_id"=>"7102152361"}, {"first_name"=>"Donna E.", "last_name"=>"Maziak", "scopus_author_id"=>"7004351178"}, {"first_name"=>"Farid M.", "last_name"=>"Shamji", "scopus_author_id"=>"7004262859"}, {"first_name"=>"Paul", "last_name"=>"Hebert", "scopus_author_id"=>"26643333400"}, {"first_name"=>"Dean", "last_name"=>"Fergusson", "scopus_author_id"=>"7102364909"}, {"first_name"=>"Alan", "last_name"=>"Tinmouth", "scopus_author_id"=>"35581620600"}, {"first_name"=>"Andrew J.E.", "last_name"=>"Seely", "scopus_author_id"=>"6603125756"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"355218950", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0006642", "scopus"=>"2-s2.0-69949162508", "pmid"=>"19680545", "sgr"=>"69949162508"}, "id"=>"65c8d281-fa6d-35aa-96dd-dc1c6ff3de3c", "abstract"=>"BACKGROUND: Early diagnosis of sepsis enables timely resuscitation and antibiotics and prevents subsequent morbidity and mortality. Clinical approaches relying on point-in-time analysis of vital signs or lab values are often insensitive, non-specific and late diagnostic markers of sepsis. Exploring otherwise hidden information within intervals-in-time, heart rate variability (HRV) has been documented to be both altered in the presence of sepsis, and correlated with its severity. We hypothesized that by continuously tracking individual patient HRV over time in patients as they develop sepsis, we would demonstrate reduced HRV in association with the onset of sepsis. METHODOLOGY/PRINCIPAL FINDINGS: We monitored heart rate continuously in adult bone marrow transplant (BMT) patients (n = 21) beginning a day before their BMT and continuing until recovery or withdrawal (12+/-4 days). We characterized HRV continuously over time with a panel of time, frequency, complexity, and scale-invariant domain techniques. We defined baseline HRV as mean variability for the first 24 h of monitoring and studied individual and population average percentage change (from baseline) over time in diverse HRV metrics, in comparison with the time of clinical diagnosis and treatment of sepsis (defined as systemic inflammatory response syndrome along with clinically suspected infection requiring treatment). Of the 21 patients enrolled, 4 patients withdrew, leaving 17 patients who completed the study. Fourteen patients developed sepsis requiring antibiotic therapy, whereas 3 did not. On average, for 12 out of 14 infected patients, a significant (25%) reduction prior to the clinical diagnosis and treatment of sepsis was observed in standard deviation, root mean square successive difference, sample and multiscale entropy, fast Fourier transform, detrended fluctuation analysis, and wavelet variability metrics. For infected patients (n = 14), wavelet HRV demonstrated a 25% drop from baseline 35 h prior to sepsis on average. For 3 out of 3 non-infected patients, all measures, except root mean square successive difference and entropy, showed no significant reduction. Significant correlation was present amongst these HRV metrics for the entire population. CONCLUSIONS/SIGNIFICANCE: Continuous HRV monitoring is feasible in ambulatory patients, demonstrates significant HRV alteration in individual patients in association with, and prior to clinical diagnosis and treatment of sepsis, and merits further investigation as a means of providing early warning of sepsis.", "link"=>"http://www.mendeley.com/research/continuous-multiparameter-heart-rate-variability-analysis-heralds-onset-sepsis-adults", "reader_count"=>122, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>28, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Student > Master"=>19, "Other"=>12, "Student > Bachelor"=>3, "Professor"=>15}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>28, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Student > Master"=>19, "Other"=>12, "Student > Bachelor"=>3, "Professor"=>15}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>7, "Business, Management and Accounting"=>1, "Computer Science"=>9, "Engineering"=>19, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>61, "Neuroscience"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Sports and Recreations"=>1, "Physics and Astronomy"=>5, "Psychology"=>2, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>61}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>19}, "Neuroscience"=>{"Neuroscience"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>9}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "United States"=>1, "Japan"=>1, "Ukraine"=>1, "Mexico"=>1, "Italy"=>1, "Germany"=>2, "Spain"=>1}, "group_count"=>16}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/887548"], "description"=>"<p>Data are number (%) unless otherwise stated.</p>", "links"=>[], "tags"=>["antibiotics", "bacteriological"], "article_id"=>557991, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Indication_for_antibiotics_and_bacteriological_diagnosis_/557991", "title"=>"Indication for antibiotics and bacteriological diagnosis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 04:59:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/887168"], "description"=>"<p>Average multi-parameter (HRV is studied every 12 h in infected (n = 14) and non-infected (n = 3) patients from 0 h (time after baseline variability) up to the end of the study. Green plots are non-infected patients whereas red plots are infected patients. The error bars represent the standard error mean (SEM). The grey horizontal bar at the bottom represents the range of hours where sepsis occurred for all infected patients. The blue horizontal line at the bottom represents the mean time ±standard deviation of sepsis for all infected patients.</p>", "links"=>[], "tags"=>["multi-parameter"], "article_id"=>557608, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_percentage_change_916_HRV_in_multi_parameter_variability_/557608", "title"=>"Average percentage change (ΔHRV) in multi-parameter variability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 04:57:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/887057"], "description"=>"<p>((HRV) in wavelet variability. Individual wavelet (HRV is studied every 1 h in infected (n = 14) and non-infected (n = 3) patients from 0 h (time after baseline variability) up to the end of the study. Green plots are non-infected patients whereas red plots are infected patients. Solid vertical line denotes the time of sepsis. Lead time (TP is studied for 25% drop (dashed vertical line and dot) from baseline (first 24 h) HRV.</p>", "links"=>[], "tags"=>["smoothed"], "article_id"=>557489, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.g001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individual_smoothed_percentage_change_/557489", "title"=>"Individual smoothed percentage change.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 04:56:36"}
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  • {"files"=>["https://ndownloader.figshare.com/files/887261"], "description"=>"<p>Average multi-parameter ΔHRV is studied every 6 h in infected (n = 14) patients±72 h around sepsis (0 h). The error bars represent the standard error mean (SEM).</p>", "links"=>[], "tags"=>["multi-parameter", "variability"], "article_id"=>557707, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.g003", "stats"=>{"downloads"=>2, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_percentage_change_916_HRV_in_multi_parameter_variability_around_sepsis_/557707", "title"=>"Average percentage change (ΔHRV) in multi-parameter variability around sepsis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 04:57:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/887590"], "description"=>"<p>Summary of signal analysis capabilities of CIMVA.</p>", "links"=>[], "tags"=>["capabilities"], "article_id"=>558039, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.t003", "stats"=>{"downloads"=>17, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_signal_analysis_capabilities_of_CIMVA_/558039", "title"=>"Summary of signal analysis capabilities of CIMVA.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 04:59:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/887449"], "description"=>"<p>Example of first five window instances of a CIMVA generated change in HRV over time matrix for diverse measures for a moving window <i>width</i> of 1200 samples and <i>step</i> of 200 samples.</p>", "links"=>[], "tags"=>["instances", "cimva", "generated", "hrv", "matrix", "measures", "moving", "1200", "samples", "200"], "article_id"=>557880, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_first_five_window_instances_of_a_CIMVA_generated_change_in_HRV_over_time_matrix_for_diverse_measures_for_a_moving_window_width_of_1200_samples_and_step_of_200_samples_/557880", "title"=>"Example of first five window instances of a CIMVA generated change in HRV over time matrix for diverse measures for a moving window <i>width</i> of 1200 samples and <i>step</i> of 200 samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 04:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/887652"], "description"=>"<p>Data are number (%) unless otherwise stated.</p>", "links"=>[], "tags"=>["Critical care and emergency medicine", "physiology", "mathematics/fourier analysis", "mathematics/fractals", "mathematics/mathematical computing", "mathematics/nonlinear dynamics", "mathematics/statistics", "critical care and emergency medicine/sepsis and multiple organ failure", "infectious diseases/bacterial infections"], "article_id"=>558085, "categories"=>["Mathematics", "Medicine", "Infectious Diseases", "Physiology"], "users"=>["Saif Ahmad", "Tim Ramsay", "Lothar Huebsch", "Sarah Flanagan", "Sheryl McDiarmid", "Izmail Batkin", "Lauralyn McIntyre", "Sudhir R. Sundaresan", "Donna E. Maziak", "Farid M. Shamji", "Paul Hebert", "Dean Fergusson", "Alan Tinmouth", "Andrew J. E. Seely"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006642.t001", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_and_patient_characteristics_along_with_reason_and_type_of_BMT_/558085", "title"=>"Study and patient characteristics, along with reason and type of BMT.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 04:59:51"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
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  • {"unique-ip"=>"14", "full-text"=>"16", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"16", "full-text"=>"17", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
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Relative Metric

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