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{"title"=>"Monitoring and Identification of Sepsis Development through a Composite Measure of Heart Rate Variability", "type"=>"journal", "authors"=>[{"first_name"=>"Andrea", "last_name"=>"Bravi", "scopus_author_id"=>"36162922600"}, {"first_name"=>"Geoffrey", "last_name"=>"Green", "scopus_author_id"=>"55419348300"}, {"first_name"=>"André", "last_name"=>"Longtin", "scopus_author_id"=>"7003305679"}, {"first_name"=>"Andrew J.E.", "last_name"=>"Seely", "scopus_author_id"=>"6603125756"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365690633", "issn"=>"19326203", "isbn"=>"10.1371/journal.pone.0045666", "doi"=>"10.1371/journal.pone.0045666", "scopus"=>"2-s2.0-84866531477", "pmid"=>"23029171", "sgr"=>"84866531477"}, "id"=>"9cac980e-c3d8-35ea-b392-5462818fccfa", "abstract"=>"Tracking the physiological conditions of a patient developing infection is of utmost importance to provide optimal care at an early stage. This work presents a procedure to integrate multiple measures of heart rate variability into a unique measure for the tracking of sepsis development. An early warning system is used to illustrate its potential clinical value. The study involved 17 adults (age median 51 (interquartile range 46-62)) who experienced a period of neutropenia following chemoradiotherapy and bone marrow transplant; 14 developed sepsis, and 3 did not. A comprehensive panel (N = 92) of variability measures was calculated for 5 min-windows throughout the period of monitoring (12 ± 4 days). Variability measures underwent filtering and two steps of data reduction with the objective of enhancing the information related to the greatest degree of change. The proposed composite measure was capable of tracking the development of sepsis in 12 out of 14 patients. Simulating a real-time monitoring setting, the sum of the energy over the very low frequency range of the composite measure was used to classify the probability of developing sepsis. The composite revealed information about the onset of sepsis about 60 hours (median value) before of sepsis diagnosis. In a real monitoring setting this quicker detection time would be associated to increased efficacy in the treatment of sepsis, therefore highlighting the potential clinical utility of a composite measure of variability.", "link"=>"http://www.mendeley.com/research/monitoring-identification-sepsis-development-through-composite-measure-heart-rate-variability", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Agricultural and Biological Sciences"=>4, "Arts and Humanities"=>1, "Computer Science"=>4, "Engineering"=>10, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>25, "Neuroscience"=>1, "Sports and Recreations"=>2, "Physics and Astronomy"=>1, "Psychology"=>3, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>25}, "Sports and Recreations"=>{"Sports and Recreations"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>10}, "Neuroscience"=>{"Neuroscience"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"Belgium"=>1, "Norway"=>1, "United Kingdom"=>1, "Spain"=>1, "India"=>1}, "group_count"=>3}

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• http://doi.org/10.1016/j.earlhumdev.2018.01.008
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• http://doi.org/10.1088/0967-3334/36/5/L1
• http://doi.org/10.1164/rccm.201511-2288PP
• http://doi.org/10.1186/s40635-017-0146-1
• http://doi.org/10.1186/cc12894
• http://doi.org/10.1016/j.compbiomed.2013.09.012
• http://doi.org/10.1016/j.jpeds.2013.11.038
• http://doi.org/10.1109/RBME.2013.2243724
• http://doi.org/10.1088/0967-3334/35/12/L1
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• http://doi.org/10.1152/japplphysiol.00122.2017
• http://doi.org/10.1097/PCC.0000000000001590

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/303561", "https://ndownloader.figshare.com/files/303593", "https://ndownloader.figshare.com/files/303624", "https://ndownloader.figshare.com/files/303654"], "description"=>"<div><p>Tracking the physiological conditions of a patient developing infection is of utmost importance to provide optimal care at an early stage. This work presents a procedure to integrate multiple measures of heart rate variability into a unique measure for the tracking of sepsis development. An early warning system is used to illustrate its potential clinical value. The study involved 17 adults (age median 51 (interquartile range 46–62)) who experienced a period of neutropenia following chemoradiotherapy and bone marrow transplant; 14 developed sepsis, and 3 did not. A comprehensive panel (N = 92) of variability measures was calculated for 5 min-windows throughout the period of monitoring (12±4 days). Variability measures underwent filtering and two steps of data reduction with the objective of enhancing the information related to the greatest degree of change. The proposed composite measure was capable of tracking the development of sepsis in 12 out of 14 patients. Simulating a real-time monitoring setting, the sum of the energy over the very low frequency range of the composite measure was used to classify the probability of developing sepsis. The composite revealed information about the onset of sepsis about 60 hours (median value) before of sepsis diagnosis. In a real monitoring setting this quicker detection time would be associated to increased efficacy in the treatment of sepsis, therefore highlighting the potential clinical utility of a composite measure of variability.</p> </div>", "links"=>[], "tags"=>["monitoring", "sepsis", "composite", "variability"], "article_id"=>119868, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.s001", "https://dx.doi.org/10.1371/journal.pone.0045666.s002", "https://dx.doi.org/10.1371/journal.pone.0045666.s003", "https://dx.doi.org/10.1371/journal.pone.0045666.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Monitoring_and_Identification_of_Sepsis_Development_through_a_Composite_Measure_of_Heart_Rate_Variability/119868", "title"=>"Monitoring and Identification of Sepsis Development through a Composite Measure of Heart Rate Variability", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-09-19 02:44:28"}
• {"files"=>["https://ndownloader.figshare.com/files/574681"], "description"=>"<p>Block diagram showing how to create the composite measure of variability and the likelihood of developing sepsis. The time window [<b>0</b>,<b><i>t</i></b>] is increased at every iteration of 2.5 minutes. This allows to reproduce a monitoring situation where new R-R intervals are continuously analyzed. Having the variability up to at a certain time <b><i>t</i></b>, we can compute the composite, and from the composite the probability of developing sepsis.</p>", "links"=>[], "tags"=>["immunology", "biotechnology", "Critical care and emergency medicine", "oncology"], "article_id"=>245174, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Signal_processing_diagram_/245174", "title"=>"Signal processing diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-19 01:26:14"}
• {"files"=>["https://ndownloader.figshare.com/files/574776"], "description"=>"<p>In red are displayed the results of the composite; for comparison, in black are displayed the results of the detrended fluctuation analysis area under the curve, after admission condition normalization. The continuous lines represent the average value of the time series across the population, and the dashed lines represent plus or minus the standard error of the mean. The two vertical dotted lines highlight when, on average, the composite variability started to drop. Before averaging, for each of the 14 subjects developing sepsis the time series of either the composite or the detrended fluctuation analysis were aligned to the time of administration of antibiotics (t = 0). The picture shows the higher sensitivity of the composite to sepsis development, respect to the sensitivity of a single HRV measure.</p>", "links"=>[], "tags"=>["composite"], "article_id"=>245272, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_composite_measure_of_variability_/245272", "title"=>"Average composite measure of variability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-19 01:27:52"}
• {"files"=>["https://ndownloader.figshare.com/files/574866"], "description"=>"<p>This set of double graphs show the composite measure of variability (blue solid line) and the probability of developing sepsis (green dotted line) at a given time, for each subject. As reported for the plot of subject 1, the x-axis is the time with respect to the administration of antibiotics (t = 0).</p>", "links"=>[], "tags"=>["immunology", "biotechnology", "Critical care and emergency medicine", "oncology"], "article_id"=>245358, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_development_of_sepsis_/245358", "title"=>"Probability of development of sepsis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-19 01:29:18"}
• {"files"=>["https://ndownloader.figshare.com/files/574986"], "description"=>"<p>Median value of detection 64.7 hours in advance, with a transition time from probability zero to probability higher than 0.99 of 3.3 hours (median value).</p>", "links"=>[], "tags"=>["sepsis", "composite"], "article_id"=>245488, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Detection_of_sepsis_development_through_composite_variability_/245488", "title"=>"Detection of sepsis development through composite variability.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-19 01:31:28"}
• {"files"=>["https://ndownloader.figshare.com/files/575011"], "description"=>"<p>For further details about these measures refer to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045666#pone.0045666-Bravi1\" target=\"_blank\">[10]</a>. The specific parameters used to compute the measures are available upon request.</p>", "links"=>[], "tags"=>["measures"], "article_id"=>245511, "categories"=>["Cancer", "Biotechnology", "Medicine", "Immunology"], "users"=>["Andrea Bravi", "Geoffrey Green", "André Longtin", "Andrew J. E. Seely"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045666.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_measures_of_variability_/245511", "title"=>"Selected measures of variability.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-19 01:31:51"}

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[322, 550, 671, 773, 864, 955, 1048, 1135, 1223, 1308, 1387, 1465, 1534, 1602, 1673, 1744, 1813, 1885, 1955, 2026, 2093, 2160, 2228, 2290, 2349]}, {"subject_area"=>"/Biology and life sciences/Microbiology", "average_usage"=>[339, 580, 709, 814, 907, 1003, 1095, 1190, 1286, 1375, 1466, 1546, 1622, 1694, 1765, 1830, 1902, 1978, 2054, 2122, 2194, 2269, 2330, 2388, 2460]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[352, 587, 696, 809, 901, 989, 1072, 1156, 1257, 1334, 1422, 1486, 1555, 1647, 1714, 1780, 1844, 1919, 1997, 2051, 2138, 2198, 2267, 2324, 2391]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[355, 520, 650, 728, 791, 856, 901, 981, 1041, 1117, 1199, 1275, 1333, 1378, 1462, 1519, 1562, 1627, 1747, 1820, 1896, 1973, 2045, 2100, 2145]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[308, 500, 607, 700, 782, 876, 957, 1039, 1128, 1214, 1290, 1365, 1431, 1496, 1560, 1634, 1695, 1772, 1839, 1908, 1972, 2043, 2106, 2179, 2246]}, {"subject_area"=>"/Medicine and health sciences/Pathology and laboratory medicine", "average_usage"=>[321, 553, 683, 785, 880, 973, 1072, 1162, 1249, 1336, 1425, 1504, 1574, 1642, 1711, 1785, 1852, 1923, 1987, 2050, 2129, 2192, 2259, 2325, 2393]}, {"subject_area"=>"/Medicine and health sciences/Pulmonology", "average_usage"=>[311, 554, 688, 795, 893, 992, 1073, 1157, 1250, 1327, 1407, 1478, 1557, 1610, 1684, 1749, 1812, 1890, 1978, 2054, 2134, 2210, 2280, 2346, 2425]}]}

F1000Prime