Predicting severe injury using vehicle telemetry data.
Identifieur interne : 000258 ( PubMed/Curation ); précédent : 000257; suivant : 000259Predicting severe injury using vehicle telemetry data.
Auteurs : Patricia Ayoung-Chee [États-Unis] ; Christopher D. Mack ; Robert Kaufman ; Eileen BulgerSource :
- The journal of trauma and acute care surgery [ 2163-0763 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- instrumentation : Telemetry.
- statistics & numerical data : Accidents, Traffic.
- Automobiles, Biomechanical Phenomena, Humans, Injury Severity Score, Models, Statistical, Sensitivity and Specificity.
Abstract
In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data.
DOI: 10.1097/TA.0b013e31827a0bb6
PubMed: 23271095
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pubmed:23271095Le document en format XML
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Mack</name></author><author><name sortKey="Kaufman, Robert" sort="Kaufman, Robert" uniqKey="Kaufman R" first="Robert" last="Kaufman">Robert Kaufman</name></author><author><name sortKey="Bulger, Eileen" sort="Bulger, Eileen" uniqKey="Bulger E" first="Eileen" last="Bulger">Eileen Bulger</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="doi">10.1097/TA.0b013e31827a0bb6</idno><idno type="RBID">pubmed:23271095</idno><idno type="pmid">23271095</idno><idno type="wicri:Area/PubMed/Corpus">000258</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000258</idno><idno type="wicri:Area/PubMed/Curation">000258</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000258</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting severe injury using vehicle telemetry data.</title><author><name sortKey="Ayoung Chee, Patricia" sort="Ayoung Chee, Patricia" uniqKey="Ayoung Chee P" first="Patricia" last="Ayoung-Chee">Patricia Ayoung-Chee</name><affiliation wicri:level="1"><nlm:affiliation>Harborview Injury Prevention and Research Center, and Department of Surgery, University of Washington, Seattle, Washington 98104, USA. ayoungp@uw.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Harborview Injury Prevention and Research Center, and Department of Surgery, University of Washington, Seattle, Washington 98104</wicri:regionArea></affiliation></author><author><name sortKey="Mack, Christopher D" sort="Mack, Christopher D" uniqKey="Mack C" first="Christopher D" last="Mack">Christopher D. Mack</name></author><author><name sortKey="Kaufman, Robert" sort="Kaufman, Robert" uniqKey="Kaufman R" first="Robert" last="Kaufman">Robert Kaufman</name></author><author><name sortKey="Bulger, Eileen" sort="Bulger, Eileen" uniqKey="Bulger E" first="Eileen" last="Bulger">Eileen Bulger</name></author></analytic><series><title level="j">The journal of trauma and acute care surgery</title><idno type="eISSN">2163-0763</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accidents, Traffic (statistics & numerical data)</term><term>Automobiles</term><term>Biomechanical Phenomena</term><term>Humans</term><term>Injury Severity Score</term><term>Models, Statistical</term><term>Sensitivity and Specificity</term><term>Telemetry (instrumentation)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Accidents de la circulation ()</term><term>Automobiles</term><term>Humains</term><term>Modèles statistiques</term><term>Phénomènes biomécaniques</term><term>Score de gravité des lésions traumatiques</term><term>Sensibilité et spécificité</term><term>Télémesure (instrumentation)</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Telemetry</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Accidents, Traffic</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Automobiles</term><term>Biomechanical Phenomena</term><term>Humans</term><term>Injury Severity Score</term><term>Models, Statistical</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="fr"><term>Accidents de la circulation</term><term>Automobiles</term><term>Humains</term><term>Modèles statistiques</term><term>Phénomènes biomécaniques</term><term>Score de gravité des lésions traumatiques</term><term>Sensibilité et spécificité</term><term>Télémesure</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">23271095</PMID><DateCreated><Year>2012</Year><Month>12</Month><Day>28</Day></DateCreated><DateCompleted><Year>2013</Year><Month>03</Month><Day>21</Day></DateCompleted><DateRevised><Year>2013</Year><Month>11</Month><Day>21</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">2163-0763</ISSN><JournalIssue CitedMedium="Internet"><Volume>74</Volume><Issue>1</Issue><PubDate><Year>2013</Year><Month>Jan</Month></PubDate></JournalIssue><Title>The journal of trauma and acute care surgery</Title><ISOAbbreviation>J Trauma Acute Care Surg</ISOAbbreviation></Journal><ArticleTitle>Predicting severe injury using vehicle telemetry data.</ArticleTitle><Pagination><MedlinePgn>190-4; discussion 194-5</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1097/TA.0b013e31827a0bb6</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Using the National Automotive Sampling System Crashworthiness Data System data set, we included front-seat occupants in late-model vehicles (2000 and later) in nonrollover and rollover crashes in years 2000 to 2010. Telematic (change in velocity, direction of force, seat belt use, vehicle type and curb weight, as well as multiple impact) and nontelematic variables (maximum intrusion, narrow impact, and passenger ejection) were included. Missing data were multiply imputed. The University of Washington model was tested to predict severe injury before application of guidelines (Step 0) and for occupants who did not meet Steps 1 and 2 criteria (Step 3) of the Centers for Disease Control and Prevention Field Triage Guidelines. A probability threshold of 20% was chosen in accordance with Centers for Disease Control and Prevention recommendations.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">There were 28,633 crashes, involving 33,956 vehicles and 52,033 occupants, of whom 9.9% had severe injury. At Step 0, the University of Washington model sensitivity was 40.0% and positive predictive value (PPV) was 20.7%. At Step 3, the sensitivity was 32.3 % and PPV was 10.1%. Model analysis excluding nontelematic variables decreased sensitivity and PPV. The sensitivity of the re-created General Motors model was 38.5% at Step 0 and 28.1% at Step 3.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We designed a model using only vehicle collision data that was predictive of severe injury at collision notification and in the field and was comparable with an existing model. These models demonstrate the potential use of advanced automatic collision notification in planning EMS response.</AbstractText><AbstractText Label="LEVEL OF EVIDENCE" NlmCategory="METHODS">Prognostic study, level II.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ayoung-Chee</LastName><ForeName>Patricia</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Harborview Injury Prevention and Research Center, and Department of Surgery, University of Washington, Seattle, Washington 98104, USA. ayoungp@uw.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mack</LastName><ForeName>Christopher D</ForeName><Initials>CD</Initials></Author><Author ValidYN="Y"><LastName>Kaufman</LastName><ForeName>Robert</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Bulger</LastName><ForeName>Eileen</ForeName><Initials>E</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Trauma Acute Care Surg</MedlineTA><NlmUniqueID>101570622</NlmUniqueID><ISSNLinking>2163-0755</ISSNLinking></MedlineJournalInfo><CitationSubset>AIM</CitationSubset><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D000063">Accidents, Traffic</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000706">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001336">Automobiles</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001696">Biomechanical Phenomena</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D015601">Injury Severity Score</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015233">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D013686">Telemetry</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>12</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>12</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>3</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1097/TA.0b013e31827a0bb6</ArticleId><ArticleId IdType="pii">01586154-201301000-00026</ArticleId><ArticleId IdType="pubmed">23271095</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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