Comparison of automated vs. manual determination of the respiratory variations in the EKG R wave amplitude for the prediction of ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-01-10

AUTHORS

Christine K Lee, Joseph Rinehart, Cecilia Canales, Maxime Cannesson

ABSTRACT

Electrocardiogram (EKG) monitoring is a common standard of care across all operating rooms and intensive care units. Studies have suggested that respiratory variations in the EKG R wave amplitude (EKGv) can be used as an indicator of fluid responsiveness in mechanically ventilated patients under general anesthesia, but to date all calculations of variation have been done by hand. The aim of this study was to assess if a computer-automated algorithm could compute and monitor EKGv with the same precision as manual measurement. Batches of 30 s each of EKG lead II waveforms were recorded during surgical procedures with mechanical ventilation. R wave amplitude variability was assessed both manually and by automated algorithm. For both calculations, wave height was defined as R wave peak minus preceding Q wave trough, and the minimum and maximum amplitudes determined for each respiratory cycle. EKGv was calculated as 100 × [(RDIImax − RDIImin)/(RDIImax + RDIImin)/2]. Fifty-seven batches of waveforms were calculated. We found that our computer-automated algorithm calculation of EKGv was significantly correlated to manual measurements (r = 0.968, P < 0.001). Bland-Altman analysis also showed a strong agreement between automated and manual EKGv measurements (bias 0.13% ± 3.06%). The observed correlations between the manually and automatically calculated EKGv suggest that our current computer-automated algorithm is a reliable method for calculating EKGv. Validation in prospective volume expansion studies will be needed to assess the true clinical utility of this automated measurement. More... »

PAGES

5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/2194-3990-1-5

DOI

http://dx.doi.org/10.1186/2194-3990-1-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050160703


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266093.8", 
          "name": [
            "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Christine K", 
        "id": "sg:person.07577710026.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07577710026.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266093.8", 
          "name": [
            "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rinehart", 
        "givenName": "Joseph", 
        "id": "sg:person.01160710637.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160710637.64"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266093.8", 
          "name": [
            "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Canales", 
        "givenName": "Cecilia", 
        "id": "sg:person.01103725573.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103725573.66"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266093.8", 
          "name": [
            "Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cannesson", 
        "givenName": "Maxime", 
        "id": "sg:person.0755150717.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755150717.92"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/cc3902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037355654", 
          "https://doi.org/10.1186/cc3902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/cc10364", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052261679", 
          "https://doi.org/10.1186/cc10364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/cc6916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033411669", 
          "https://doi.org/10.1186/cc6916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/cc8179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043074984", 
          "https://doi.org/10.1186/cc8179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10877-012-9405-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010875619", 
          "https://doi.org/10.1007/s10877-012-9405-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10877-010-9235-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006631945", 
          "https://doi.org/10.1007/s10877-010-9235-3"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-01-10", 
    "datePublishedReg": "2014-01-10", 
    "description": "Electrocardiogram (EKG) monitoring is a common standard of care across all operating rooms and intensive care units. Studies have suggested that respiratory variations in the EKG R wave amplitude (EKGv) can be used as an indicator of fluid responsiveness in mechanically ventilated patients under general anesthesia, but to date all calculations of variation have been done by hand. The aim of this study was to assess if a computer-automated algorithm could compute and monitor EKGv with the same precision as manual measurement. Batches of 30\u00a0s each of EKG lead II waveforms were recorded during surgical procedures with mechanical ventilation. R wave amplitude variability was assessed both manually and by automated algorithm. For both calculations, wave height was defined as R wave peak minus preceding Q wave trough, and the minimum and maximum amplitudes determined for each respiratory cycle. EKGv was calculated as 100\u2009\u00d7\u2009[(RDIImax\u2009\u2212\u2009RDIImin)/(RDIImax\u2009+\u2009RDIImin)/2]. Fifty-seven batches of waveforms were calculated. We found that our computer-automated algorithm calculation of EKGv was significantly correlated to manual measurements (r\u2009=\u20090.968, P\u2009<\u20090.001). Bland-Altman analysis also showed a strong agreement between automated and manual EKGv measurements (bias 0.13%\u2009\u00b1\u20093.06%). The observed correlations between the manually and automatically calculated EKGv suggest that our current computer-automated algorithm is a reliable method for calculating EKGv. Validation in prospective volume expansion studies will be needed to assess the true clinical utility of this automated measurement.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/2194-3990-1-5", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1052069", 
        "issn": [
          "2194-3990"
        ], 
        "name": "Journal of Computational Surgery", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "1"
      }
    ], 
    "keywords": [
      "fluid responsiveness", 
      "respiratory variation", 
      "intensive care unit", 
      "true clinical utility", 
      "Bland-Altman analysis", 
      "wave amplitude variability", 
      "care unit", 
      "general anesthesia", 
      "mechanical ventilation", 
      "surgical procedures", 
      "clinical utility", 
      "computer-automated algorithm", 
      "respiratory cycle", 
      "operating room", 
      "electrocardiogram monitoring", 
      "responsiveness", 
      "wave amplitude", 
      "volume expansion study", 
      "reliable method", 
      "surgery", 
      "patients", 
      "anesthesia", 
      "EKG", 
      "amplitude variability", 
      "study", 
      "strong agreement", 
      "care", 
      "ventilation", 
      "manual measurements", 
      "aim", 
      "vs.", 
      "maximum amplitude", 
      "amplitude", 
      "date", 
      "correlation", 
      "room", 
      "procedure", 
      "utility", 
      "monitoring", 
      "indicators", 
      "measurements", 
      "units", 
      "hand", 
      "observed correlation", 
      "expansion studies", 
      "manual determination", 
      "standards", 
      "comparison", 
      "variability", 
      "analysis", 
      "validation", 
      "variation", 
      "cycle", 
      "common standards", 
      "waveforms", 
      "height", 
      "minus", 
      "algorithm calculation", 
      "method", 
      "determination", 
      "batch", 
      "trough", 
      "prediction", 
      "agreement", 
      "precision", 
      "calculation of variations", 
      "algorithm", 
      "same precision", 
      "calculations", 
      "wave trough", 
      "wave height", 
      "EKGv", 
      "R wave amplitude", 
      "wave peak minus", 
      "peak minus", 
      "batches of waveforms", 
      "computer-automated algorithm calculation", 
      "manual EKGv measurements", 
      "EKGv measurements", 
      "current computer-automated algorithm", 
      "prospective volume expansion studies"
    ], 
    "name": "Comparison of automated vs. manual determination of the respiratory variations in the EKG R wave amplitude for the prediction of fluid responsiveness during surgery", 
    "pagination": "5", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1050160703"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/2194-3990-1-5"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/2194-3990-1-5", 
      "https://app.dimensions.ai/details/publication/pub.1050160703"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:32", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_642.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/2194-3990-1-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/2194-3990-1-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/2194-3990-1-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/2194-3990-1-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/2194-3990-1-5'


 

This table displays all metadata directly associated to this object as RDF triples.

183 TRIPLES      22 PREDICATES      112 URIs      98 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/2194-3990-1-5 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N0fe9d01594e84120a69ba975e824f3ec
4 schema:citation sg:pub.10.1007/s10877-010-9235-3
5 sg:pub.10.1007/s10877-012-9405-6
6 sg:pub.10.1186/cc10364
7 sg:pub.10.1186/cc3902
8 sg:pub.10.1186/cc6916
9 sg:pub.10.1186/cc8179
10 schema:datePublished 2014-01-10
11 schema:datePublishedReg 2014-01-10
12 schema:description Electrocardiogram (EKG) monitoring is a common standard of care across all operating rooms and intensive care units. Studies have suggested that respiratory variations in the EKG R wave amplitude (EKGv) can be used as an indicator of fluid responsiveness in mechanically ventilated patients under general anesthesia, but to date all calculations of variation have been done by hand. The aim of this study was to assess if a computer-automated algorithm could compute and monitor EKGv with the same precision as manual measurement. Batches of 30 s each of EKG lead II waveforms were recorded during surgical procedures with mechanical ventilation. R wave amplitude variability was assessed both manually and by automated algorithm. For both calculations, wave height was defined as R wave peak minus preceding Q wave trough, and the minimum and maximum amplitudes determined for each respiratory cycle. EKGv was calculated as 100 × [(RDIImax − RDIImin)/(RDIImax + RDIImin)/2]. Fifty-seven batches of waveforms were calculated. We found that our computer-automated algorithm calculation of EKGv was significantly correlated to manual measurements (r = 0.968, P < 0.001). Bland-Altman analysis also showed a strong agreement between automated and manual EKGv measurements (bias 0.13% ± 3.06%). The observed correlations between the manually and automatically calculated EKGv suggest that our current computer-automated algorithm is a reliable method for calculating EKGv. Validation in prospective volume expansion studies will be needed to assess the true clinical utility of this automated measurement.
13 schema:genre article
14 schema:inLanguage en
15 schema:isAccessibleForFree true
16 schema:isPartOf N37bee1692d0f41dc96a1485217e7749e
17 N6574adc2862a46e4bc4f5538ee981176
18 sg:journal.1052069
19 schema:keywords Bland-Altman analysis
20 EKG
21 EKGv
22 EKGv measurements
23 R wave amplitude
24 agreement
25 aim
26 algorithm
27 algorithm calculation
28 amplitude
29 amplitude variability
30 analysis
31 anesthesia
32 batch
33 batches of waveforms
34 calculation of variations
35 calculations
36 care
37 care unit
38 clinical utility
39 common standards
40 comparison
41 computer-automated algorithm
42 computer-automated algorithm calculation
43 correlation
44 current computer-automated algorithm
45 cycle
46 date
47 determination
48 electrocardiogram monitoring
49 expansion studies
50 fluid responsiveness
51 general anesthesia
52 hand
53 height
54 indicators
55 intensive care unit
56 manual EKGv measurements
57 manual determination
58 manual measurements
59 maximum amplitude
60 measurements
61 mechanical ventilation
62 method
63 minus
64 monitoring
65 observed correlation
66 operating room
67 patients
68 peak minus
69 precision
70 prediction
71 procedure
72 prospective volume expansion studies
73 reliable method
74 respiratory cycle
75 respiratory variation
76 responsiveness
77 room
78 same precision
79 standards
80 strong agreement
81 study
82 surgery
83 surgical procedures
84 trough
85 true clinical utility
86 units
87 utility
88 validation
89 variability
90 variation
91 ventilation
92 volume expansion study
93 vs.
94 wave amplitude
95 wave amplitude variability
96 wave height
97 wave peak minus
98 wave trough
99 waveforms
100 schema:name Comparison of automated vs. manual determination of the respiratory variations in the EKG R wave amplitude for the prediction of fluid responsiveness during surgery
101 schema:pagination 5
102 schema:productId Nad4f3ff526384c1c8d0a5a5628f79f15
103 Ndb2cf41784a04bc78a5e4d1f908f9dab
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050160703
105 https://doi.org/10.1186/2194-3990-1-5
106 schema:sdDatePublished 2021-12-01T19:32
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher Nc7af88d63846473091005c2b87f4d3ae
109 schema:url https://doi.org/10.1186/2194-3990-1-5
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N06ca08ef195a45e38071a1bd45d16b51 rdf:first sg:person.01160710637.64
114 rdf:rest Nf3847efadbf1434b9b496360b01de721
115 N0fe9d01594e84120a69ba975e824f3ec rdf:first sg:person.07577710026.86
116 rdf:rest N06ca08ef195a45e38071a1bd45d16b51
117 N37bee1692d0f41dc96a1485217e7749e schema:issueNumber 1
118 rdf:type schema:PublicationIssue
119 N6574adc2862a46e4bc4f5538ee981176 schema:volumeNumber 1
120 rdf:type schema:PublicationVolume
121 Nad4f3ff526384c1c8d0a5a5628f79f15 schema:name dimensions_id
122 schema:value pub.1050160703
123 rdf:type schema:PropertyValue
124 Nc7af88d63846473091005c2b87f4d3ae schema:name Springer Nature - SN SciGraph project
125 rdf:type schema:Organization
126 Nd85289e1884246699b801543a97bc4f3 rdf:first sg:person.0755150717.92
127 rdf:rest rdf:nil
128 Ndb2cf41784a04bc78a5e4d1f908f9dab schema:name doi
129 schema:value 10.1186/2194-3990-1-5
130 rdf:type schema:PropertyValue
131 Nf3847efadbf1434b9b496360b01de721 rdf:first sg:person.01103725573.66
132 rdf:rest Nd85289e1884246699b801543a97bc4f3
133 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
134 schema:name Medical and Health Sciences
135 rdf:type schema:DefinedTerm
136 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
137 schema:name Clinical Sciences
138 rdf:type schema:DefinedTerm
139 sg:journal.1052069 schema:issn 2194-3990
140 schema:name Journal of Computational Surgery
141 schema:publisher Springer Nature
142 rdf:type schema:Periodical
143 sg:person.01103725573.66 schema:affiliation grid-institutes:grid.266093.8
144 schema:familyName Canales
145 schema:givenName Cecilia
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103725573.66
147 rdf:type schema:Person
148 sg:person.01160710637.64 schema:affiliation grid-institutes:grid.266093.8
149 schema:familyName Rinehart
150 schema:givenName Joseph
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160710637.64
152 rdf:type schema:Person
153 sg:person.0755150717.92 schema:affiliation grid-institutes:grid.266093.8
154 schema:familyName Cannesson
155 schema:givenName Maxime
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755150717.92
157 rdf:type schema:Person
158 sg:person.07577710026.86 schema:affiliation grid-institutes:grid.266093.8
159 schema:familyName Lee
160 schema:givenName Christine K
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07577710026.86
162 rdf:type schema:Person
163 sg:pub.10.1007/s10877-010-9235-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006631945
164 https://doi.org/10.1007/s10877-010-9235-3
165 rdf:type schema:CreativeWork
166 sg:pub.10.1007/s10877-012-9405-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010875619
167 https://doi.org/10.1007/s10877-012-9405-6
168 rdf:type schema:CreativeWork
169 sg:pub.10.1186/cc10364 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052261679
170 https://doi.org/10.1186/cc10364
171 rdf:type schema:CreativeWork
172 sg:pub.10.1186/cc3902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037355654
173 https://doi.org/10.1186/cc3902
174 rdf:type schema:CreativeWork
175 sg:pub.10.1186/cc6916 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033411669
176 https://doi.org/10.1186/cc6916
177 rdf:type schema:CreativeWork
178 sg:pub.10.1186/cc8179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043074984
179 https://doi.org/10.1186/cc8179
180 rdf:type schema:CreativeWork
181 grid-institutes:grid.266093.8 schema:alternateName Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA
182 schema:name Department of Anesthesiology and Perioperative Care, School of Medicine, University of California, Irvine, 101 City Drive South, 92868, Orange, CA, USA
183 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...