Novel Approaches to Risk Stratification of In-Hospital Cardiac Arrest View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-01-31

AUTHORS

Jason J. Yang, Xiao Hu, Noel G. Boyle, Duc H. Do

ABSTRACT

Purpose of ReviewThis review aims to discuss strategies for patient risk stratification of in-hospital cardiac arrest and their challenges and limitations.Recent FindingsIn-hospital cardiac arrest is a significant cause of inpatient mortality, but survival to discharge rates remain low and have not significantly improved in the last three decades. As most patients are in a monitored setting and commonly show clinical deterioration prior to cardiac arrest, early intervention is thought to be the best way to both prevent and improve survival from cardiac arrest. However, detection of an impending cardiac arrest has proven to be particularly challenging.SummaryContemporary methods often rely on score-based early warning systems based on intermittently collected vitals and laboratory values that have seen some success in preventing in-hospital cardiac arrest but are neither sensitive enough to detect impending arrest without significant false alarms nor have the ability to uncover the underlying cause of an imminent arrest. Machine learning–derived early warning systems that utilize temporal trends in vital signs as well as continuous telemetry data are currently being developed. These new approaches show promise in addressing the issue of sensitivity and positive predictive value but require further clinical research and technological advancements. More... »

PAGES

5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12170-021-00667-7

DOI

http://dx.doi.org/10.1007/s12170-021-00667-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1135022161


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Jason J.", 
        "id": "sg:person.07660711543.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07660711543.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Duke School of Nursing, Duke University, Durham, NC, USA", 
          "id": "http://www.grid.ac/institutes/grid.26009.3d", 
          "name": [
            "Duke School of Nursing, Duke University, Durham, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Xiao", 
        "id": "sg:person.01026220275.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01026220275.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boyle", 
        "givenName": "Noel G.", 
        "id": "sg:person.01324355355.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324355355.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Do", 
        "givenName": "Duc H.", 
        "id": "sg:person.010574404642.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010574404642.99"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/s13049-020-00791-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1131473237", 
          "https://doi.org/10.1186/s13049-020-00791-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/cc7998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024607698", 
          "https://doi.org/10.1186/cc7998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/cc10337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006357623", 
          "https://doi.org/10.1186/cc10337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00134-004-2268-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015206361", 
          "https://doi.org/10.1007/s00134-004-2268-7"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-01-31", 
    "datePublishedReg": "2021-01-31", 
    "description": "Purpose of ReviewThis review aims to discuss strategies for patient risk stratification of in-hospital cardiac arrest and their challenges and limitations.Recent FindingsIn-hospital cardiac arrest is a significant cause of inpatient mortality, but survival to discharge rates remain low and have not significantly improved in the last three decades. As most patients are in a monitored setting and commonly show clinical deterioration prior to cardiac arrest, early intervention is thought to be the best way to both prevent and improve survival from cardiac arrest. However, detection of an impending cardiac arrest has proven to be particularly challenging.SummaryContemporary methods often rely on score-based early warning systems based on intermittently collected vitals and laboratory values that have seen some success in preventing in-hospital cardiac arrest but are neither sensitive enough to detect impending arrest without significant false alarms nor have the ability to uncover the underlying cause of an imminent arrest. Machine learning\u2013derived early warning systems that utilize temporal trends in vital signs as well as continuous telemetry data are currently being developed. These new approaches show promise in addressing the issue of sensitivity and positive predictive value but require further clinical research and technological advancements.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s12170-021-00667-7", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1039476", 
        "issn": [
          "1932-9520", 
          "1932-9563"
        ], 
        "name": "Current Cardiovascular Risk Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "keywords": [
      "hospital cardiac arrest", 
      "cardiac arrest", 
      "In-Hospital Cardiac Arrest", 
      "patient risk stratification", 
      "further clinical research", 
      "positive predictive value", 
      "inpatient mortality", 
      "clinical deterioration", 
      "most patients", 
      "risk stratification", 
      "monitored setting", 
      "laboratory values", 
      "predictive value", 
      "early intervention", 
      "ReviewThis review", 
      "significant cause", 
      "vital signs", 
      "clinical research", 
      "underlying cause", 
      "arrest", 
      "survival", 
      "cause", 
      "temporal trends", 
      "patients", 
      "stratification", 
      "mortality", 
      "discharge rate", 
      "intervention", 
      "significant false alarms", 
      "signs", 
      "review", 
      "vitals", 
      "setting", 
      "deterioration", 
      "sensitivity", 
      "rate", 
      "best way", 
      "issues of sensitivity", 
      "promise", 
      "early warning system", 
      "purpose", 
      "novel approach", 
      "ability", 
      "values", 
      "data", 
      "strategies", 
      "detection", 
      "success", 
      "decades", 
      "technological advancements", 
      "advancement", 
      "trends", 
      "challenges", 
      "limitations", 
      "approach", 
      "warning system", 
      "system", 
      "research", 
      "new approach", 
      "method", 
      "issues", 
      "alarms", 
      "way", 
      "false alarms", 
      "telemetry data"
    ], 
    "name": "Novel Approaches to Risk Stratification of In-Hospital Cardiac Arrest", 
    "pagination": "5", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1135022161"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12170-021-00667-7"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12170-021-00667-7", 
      "https://app.dimensions.ai/details/publication/pub.1135022161"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_895.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s12170-021-00667-7"
  }
]
 

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.10.1007/s12170-021-00667-7'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12170-021-00667-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12170-021-00667-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12170-021-00667-7'


 

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

163 TRIPLES      22 PREDICATES      94 URIs      82 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12170-021-00667-7 schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author N9690914d476943e3b8ba5e7ea7dd29f0
4 schema:citation sg:pub.10.1007/s00134-004-2268-7
5 sg:pub.10.1186/cc10337
6 sg:pub.10.1186/cc7998
7 sg:pub.10.1186/s13049-020-00791-0
8 schema:datePublished 2021-01-31
9 schema:datePublishedReg 2021-01-31
10 schema:description Purpose of ReviewThis review aims to discuss strategies for patient risk stratification of in-hospital cardiac arrest and their challenges and limitations.Recent FindingsIn-hospital cardiac arrest is a significant cause of inpatient mortality, but survival to discharge rates remain low and have not significantly improved in the last three decades. As most patients are in a monitored setting and commonly show clinical deterioration prior to cardiac arrest, early intervention is thought to be the best way to both prevent and improve survival from cardiac arrest. However, detection of an impending cardiac arrest has proven to be particularly challenging.SummaryContemporary methods often rely on score-based early warning systems based on intermittently collected vitals and laboratory values that have seen some success in preventing in-hospital cardiac arrest but are neither sensitive enough to detect impending arrest without significant false alarms nor have the ability to uncover the underlying cause of an imminent arrest. Machine learning–derived early warning systems that utilize temporal trends in vital signs as well as continuous telemetry data are currently being developed. These new approaches show promise in addressing the issue of sensitivity and positive predictive value but require further clinical research and technological advancements.
11 schema:genre article
12 schema:inLanguage en
13 schema:isAccessibleForFree false
14 schema:isPartOf N86dfc7bdf7904fddbcefe6b739d5b2a7
15 Na126e151116542a1bbf3e71bd03eed8d
16 sg:journal.1039476
17 schema:keywords In-Hospital Cardiac Arrest
18 ReviewThis review
19 ability
20 advancement
21 alarms
22 approach
23 arrest
24 best way
25 cardiac arrest
26 cause
27 challenges
28 clinical deterioration
29 clinical research
30 data
31 decades
32 detection
33 deterioration
34 discharge rate
35 early intervention
36 early warning system
37 false alarms
38 further clinical research
39 hospital cardiac arrest
40 inpatient mortality
41 intervention
42 issues
43 issues of sensitivity
44 laboratory values
45 limitations
46 method
47 monitored setting
48 mortality
49 most patients
50 new approach
51 novel approach
52 patient risk stratification
53 patients
54 positive predictive value
55 predictive value
56 promise
57 purpose
58 rate
59 research
60 review
61 risk stratification
62 sensitivity
63 setting
64 significant cause
65 significant false alarms
66 signs
67 strategies
68 stratification
69 success
70 survival
71 system
72 technological advancements
73 telemetry data
74 temporal trends
75 trends
76 underlying cause
77 values
78 vital signs
79 vitals
80 warning system
81 way
82 schema:name Novel Approaches to Risk Stratification of In-Hospital Cardiac Arrest
83 schema:pagination 5
84 schema:productId N71bdc2c245d748519b8db9ac3a97bdc6
85 N9af3912b316c4169af9133634817fbac
86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1135022161
87 https://doi.org/10.1007/s12170-021-00667-7
88 schema:sdDatePublished 2022-05-20T07:38
89 schema:sdLicense https://scigraph.springernature.com/explorer/license/
90 schema:sdPublisher N6db057e26ba3412f8e4d709adfe26592
91 schema:url https://doi.org/10.1007/s12170-021-00667-7
92 sgo:license sg:explorer/license/
93 sgo:sdDataset articles
94 rdf:type schema:ScholarlyArticle
95 N51cf58b264bd4cb4ae025b0823f8a7fb rdf:first sg:person.01026220275.59
96 rdf:rest Nc4550887be6e4b789f58ddaefb8dcbc6
97 N5cca02c12ddf435b8ba4c95a39e69525 rdf:first sg:person.010574404642.99
98 rdf:rest rdf:nil
99 N6db057e26ba3412f8e4d709adfe26592 schema:name Springer Nature - SN SciGraph project
100 rdf:type schema:Organization
101 N71bdc2c245d748519b8db9ac3a97bdc6 schema:name doi
102 schema:value 10.1007/s12170-021-00667-7
103 rdf:type schema:PropertyValue
104 N86dfc7bdf7904fddbcefe6b739d5b2a7 schema:issueNumber 3
105 rdf:type schema:PublicationIssue
106 N9690914d476943e3b8ba5e7ea7dd29f0 rdf:first sg:person.07660711543.67
107 rdf:rest N51cf58b264bd4cb4ae025b0823f8a7fb
108 N9af3912b316c4169af9133634817fbac schema:name dimensions_id
109 schema:value pub.1135022161
110 rdf:type schema:PropertyValue
111 Na126e151116542a1bbf3e71bd03eed8d schema:volumeNumber 15
112 rdf:type schema:PublicationVolume
113 Nc4550887be6e4b789f58ddaefb8dcbc6 rdf:first sg:person.01324355355.05
114 rdf:rest N5cca02c12ddf435b8ba4c95a39e69525
115 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
116 schema:name Medical and Health Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
119 schema:name Cardiorespiratory Medicine and Haematology
120 rdf:type schema:DefinedTerm
121 sg:journal.1039476 schema:issn 1932-9520
122 1932-9563
123 schema:name Current Cardiovascular Risk Reports
124 schema:publisher Springer Nature
125 rdf:type schema:Periodical
126 sg:person.01026220275.59 schema:affiliation grid-institutes:grid.26009.3d
127 schema:familyName Hu
128 schema:givenName Xiao
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01026220275.59
130 rdf:type schema:Person
131 sg:person.010574404642.99 schema:affiliation grid-institutes:grid.19006.3e
132 schema:familyName Do
133 schema:givenName Duc H.
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010574404642.99
135 rdf:type schema:Person
136 sg:person.01324355355.05 schema:affiliation grid-institutes:grid.19006.3e
137 schema:familyName Boyle
138 schema:givenName Noel G.
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324355355.05
140 rdf:type schema:Person
141 sg:person.07660711543.67 schema:affiliation grid-institutes:grid.19006.3e
142 schema:familyName Yang
143 schema:givenName Jason J.
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07660711543.67
145 rdf:type schema:Person
146 sg:pub.10.1007/s00134-004-2268-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015206361
147 https://doi.org/10.1007/s00134-004-2268-7
148 rdf:type schema:CreativeWork
149 sg:pub.10.1186/cc10337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006357623
150 https://doi.org/10.1186/cc10337
151 rdf:type schema:CreativeWork
152 sg:pub.10.1186/cc7998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024607698
153 https://doi.org/10.1186/cc7998
154 rdf:type schema:CreativeWork
155 sg:pub.10.1186/s13049-020-00791-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131473237
156 https://doi.org/10.1186/s13049-020-00791-0
157 rdf:type schema:CreativeWork
158 grid-institutes:grid.19006.3e schema:alternateName UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA
159 schema:name UCLA Cardiac Arrhythmia Center, UCLA Health System, David Geffen School of Medicine at UCLA, 100 UCLA Medical Plaza, Suite 660, 90095-7392, Los Angeles, CA, USA
160 rdf:type schema:Organization
161 grid-institutes:grid.26009.3d schema:alternateName Duke School of Nursing, Duke University, Durham, NC, USA
162 schema:name Duke School of Nursing, Duke University, Durham, NC, USA
163 rdf:type schema:Organization
 




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


...