Prognostic value of nonlinear heart rate dynamics in hemodialysis patients with coronary artery disease View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2003-08

AUTHORS

Hidekatsu Fukuta, Junichiro Hayano, Shinji Ishihara, Seiichiro Sakata, Nobuyuki Ohte, Hiroshi Takahashi, Masaki Yokoya, Takanobu Toriyama, Hirohisa Kawahara, Kazuhiro Yajima, Kenji Kobayashi, Genjiro Kimura

ABSTRACT

BACKGROUND: Although altered nonlinear heart rate dynamics predicts death in patients with coronary artery disease (CAD), its prognostic value in chronic hemodialysis patients with CAD is unknown. METHODS: We analyzed 24-hour electrocardiogram for nonlinear heart rate dynamics and heart rate variability in a retrospective cohort of 81 chronic hemodialysis patients with CAD. RESULTS: During a follow-up period of 31 +/- 20 months, 19 cardiac and 8 noncardiac deaths were observed. Cox hazards model, including diabetes, left ventricular ejection fraction, and the number of diseased coronary arteries, revealed that abnormal alpha2 (defined as both increase and decrease in alpha2 because of its J curve relationship with cardiac mortality), decreased approximate entropy and decreased heart rate variability (triangular index and ultra-low frequency power) were significant and independent predictors of cardiac death. No significant and independent predictive power for noncardiac death was observed in either the heart rate dynamics or the heart rate variability measures. The predictive power of alpha2 and approximate entropy was independent of that of triangular index and ultra-low frequency power. Combinations of two categories of measures improved the predictive accuracy; overall accuracy of approximate entropy + ultra-low frequency power for cardiac death was 87%. CONCLUSION: Altered nonlinear heart rate dynamics are independent predictors of cardiac death in chronic hemodialysis patients with CAD and their combinations with decreased heart rate variability provide clinically useful markers for risk stratification. More... »

PAGES

641-648

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1046/j.1523-1755.2003.00131.x

DOI

http://dx.doi.org/10.1046/j.1523-1755.2003.00131.x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031532222

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12846761


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Coronary Artery Disease", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Electrocardiography, Ambulatory", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Follow-Up Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart Rate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Incidence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Kidney Failure, Chronic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nonlinear Dynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prognosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Renal Dialysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Survival Analysis", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Nagoya City University", 
          "id": "https://www.grid.ac/institutes/grid.260433.0", 
          "name": [
            "Department of Internal Medicine and Pathophysiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fukuta", 
        "givenName": "Hidekatsu", 
        "id": "sg:person.0774421303.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774421303.15"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Hayano", 
        "givenName": "Junichiro", 
        "id": "sg:person.01312407755.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312407755.81"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Ishihara", 
        "givenName": "Shinji", 
        "id": "sg:person.01130015667.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01130015667.17"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Sakata", 
        "givenName": "Seiichiro", 
        "id": "sg:person.01244244267.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01244244267.35"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Ohte", 
        "givenName": "Nobuyuki", 
        "id": "sg:person.0610702054.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610702054.26"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Takahashi", 
        "givenName": "Hiroshi", 
        "id": "sg:person.0735501673.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0735501673.71"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Yokoya", 
        "givenName": "Masaki", 
        "id": "sg:person.0610022227.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610022227.46"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Toriyama", 
        "givenName": "Takanobu", 
        "id": "sg:person.01117743421.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117743421.03"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Kawahara", 
        "givenName": "Hirohisa", 
        "id": "sg:person.01234172021.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234172021.61"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Yajima", 
        "givenName": "Kazuhiro", 
        "id": "sg:person.01247037400.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247037400.65"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Kobayashi", 
        "givenName": "Kenji", 
        "id": "sg:person.0604372740.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604372740.04"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Kimura", 
        "givenName": "Genjiro", 
        "id": "sg:person.01241043201.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01241043201.53"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1136/hrt.65.1.14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002104244"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0002-9149(00)01312-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011954925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0895-7061(96)00152-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020743953"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ki.1995.202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023397795", 
          "https://doi.org/10.1038/ki.1995.202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0002-9149(98)01076-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023817675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ndt/12.5.884", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026166663"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1542-474x.2002.tb00154.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026388466"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.102.3.300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027971373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ndt/14.6.1480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031560282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.101.1.47", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033299517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(97)11144-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037817965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0002-9149(87)90795-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039930968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1053/ajkd.1998.v31.pm9531175", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043187409"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/00365518609083729", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043741864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0002-9149(01)01851-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051990876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0002-9149(01)01578-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052912728"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000189469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053622550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.166141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057739643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.93.5.1043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063337007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpheart.1994.266.4.h1643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082691656"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpregu.1996.271.4.r1078", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082991281"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2003-08", 
    "datePublishedReg": "2003-08-01", 
    "description": "BACKGROUND: Although altered nonlinear heart rate dynamics predicts death in patients with coronary artery disease (CAD), its prognostic value in chronic hemodialysis patients with CAD is unknown.\nMETHODS: We analyzed 24-hour electrocardiogram for nonlinear heart rate dynamics and heart rate variability in a retrospective cohort of 81 chronic hemodialysis patients with CAD.\nRESULTS: During a follow-up period of 31 +/- 20 months, 19 cardiac and 8 noncardiac deaths were observed. Cox hazards model, including diabetes, left ventricular ejection fraction, and the number of diseased coronary arteries, revealed that abnormal alpha2 (defined as both increase and decrease in alpha2 because of its J curve relationship with cardiac mortality), decreased approximate entropy and decreased heart rate variability (triangular index and ultra-low frequency power) were significant and independent predictors of cardiac death. No significant and independent predictive power for noncardiac death was observed in either the heart rate dynamics or the heart rate variability measures. The predictive power of alpha2 and approximate entropy was independent of that of triangular index and ultra-low frequency power. Combinations of two categories of measures improved the predictive accuracy; overall accuracy of approximate entropy + ultra-low frequency power for cardiac death was 87%.\nCONCLUSION: Altered nonlinear heart rate dynamics are independent predictors of cardiac death in chronic hemodialysis patients with CAD and their combinations with decreased heart rate variability provide clinically useful markers for risk stratification.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1046/j.1523-1755.2003.00131.x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1015319", 
        "issn": [
          "0085-2538", 
          "1523-1755"
        ], 
        "name": "Kidney International", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "64"
      }
    ], 
    "name": "Prognostic value of nonlinear heart rate dynamics in hemodialysis patients with coronary artery disease", 
    "pagination": "641-648", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "9636b52d984b3706fac01fc1e35a324a01e21818b4a5e18215fe6706e74cb695"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "12846761"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0323470"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1046/j.1523-1755.2003.00131.x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1031532222"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1046/j.1523-1755.2003.00131.x", 
      "https://app.dimensions.ai/details/publication/pub.1031532222"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000425.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://www.nature.com/ki/journal/v64/n2/full/4493925a.html"
  }
]
 

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.10.1046/j.1523-1755.2003.00131.x'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1046/j.1523-1755.2003.00131.x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1046/j.1523-1755.2003.00131.x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1046/j.1523-1755.2003.00131.x'


 

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

271 TRIPLES      21 PREDICATES      68 URIs      39 LITERALS      27 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1046/j.1523-1755.2003.00131.x schema:about N21b43d4926c84d5f83c4420395e15e14
2 N2a6e4635737f432ebc1ceb3005ea7928
3 N42ca385b68744fce93d9c8e191b4ab49
4 N47afc7909c1d4d91823288f83b1cb883
5 N482415e4a3b344c7accb7062ce21d0ed
6 N4dc39a8dd98748a6bdf9cec4ca224e2f
7 N52495afa8316417abff6bbe587d9d6e9
8 N699b23d90821429f9cf8a296f386f60b
9 N84c8866aee324c74bf0773ebe13eae7f
10 N8c3921cd0e4143d3839b1755839570a6
11 N8e61650ce9984e9e9132fc4312269599
12 N9b01716089114b25bb92b78b3cbba5d2
13 N9b599e9ab47f4c45b1b82f13a0e30db6
14 N9cb9a3c1dfcc4991a8f733a0b46b1ac2
15 Nb01690bb8d5747f1b4adb951b491c5aa
16 Nb5a1f89f154d4bf69b486ff273fcc0e6
17 Ndd2de4ba0e3e4c6b8b731f1c5c2be4ab
18 Ne8a6c5c7359b4329a795563527ac455d
19 anzsrc-for:11
20 anzsrc-for:1102
21 schema:author N9f3281939a1846dd86af4663ff22beb4
22 schema:citation sg:pub.10.1038/ki.1995.202
23 https://doi.org/10.1016/0002-9149(87)90795-8
24 https://doi.org/10.1016/0895-7061(96)00152-5
25 https://doi.org/10.1016/s0002-9149(00)01312-6
26 https://doi.org/10.1016/s0002-9149(01)01578-8
27 https://doi.org/10.1016/s0002-9149(01)01851-3
28 https://doi.org/10.1016/s0002-9149(98)01076-5
29 https://doi.org/10.1016/s0140-6736(97)11144-8
30 https://doi.org/10.1053/ajkd.1998.v31.pm9531175
31 https://doi.org/10.1063/1.166141
32 https://doi.org/10.1093/ndt/12.5.884
33 https://doi.org/10.1093/ndt/14.6.1480
34 https://doi.org/10.1111/j.1542-474x.2002.tb00154.x
35 https://doi.org/10.1136/hrt.65.1.14
36 https://doi.org/10.1152/ajpheart.1994.266.4.h1643
37 https://doi.org/10.1152/ajpregu.1996.271.4.r1078
38 https://doi.org/10.1159/000189469
39 https://doi.org/10.1161/01.cir.101.1.47
40 https://doi.org/10.1161/01.cir.102.3.300
41 https://doi.org/10.1161/01.cir.93.5.1043
42 https://doi.org/10.3109/00365518609083729
43 schema:datePublished 2003-08
44 schema:datePublishedReg 2003-08-01
45 schema:description BACKGROUND: Although altered nonlinear heart rate dynamics predicts death in patients with coronary artery disease (CAD), its prognostic value in chronic hemodialysis patients with CAD is unknown. METHODS: We analyzed 24-hour electrocardiogram for nonlinear heart rate dynamics and heart rate variability in a retrospective cohort of 81 chronic hemodialysis patients with CAD. RESULTS: During a follow-up period of 31 +/- 20 months, 19 cardiac and 8 noncardiac deaths were observed. Cox hazards model, including diabetes, left ventricular ejection fraction, and the number of diseased coronary arteries, revealed that abnormal alpha2 (defined as both increase and decrease in alpha2 because of its J curve relationship with cardiac mortality), decreased approximate entropy and decreased heart rate variability (triangular index and ultra-low frequency power) were significant and independent predictors of cardiac death. No significant and independent predictive power for noncardiac death was observed in either the heart rate dynamics or the heart rate variability measures. The predictive power of alpha2 and approximate entropy was independent of that of triangular index and ultra-low frequency power. Combinations of two categories of measures improved the predictive accuracy; overall accuracy of approximate entropy + ultra-low frequency power for cardiac death was 87%. CONCLUSION: Altered nonlinear heart rate dynamics are independent predictors of cardiac death in chronic hemodialysis patients with CAD and their combinations with decreased heart rate variability provide clinically useful markers for risk stratification.
46 schema:genre research_article
47 schema:inLanguage en
48 schema:isAccessibleForFree true
49 schema:isPartOf N3b2b455442a74b3a91ddc8b1a61850bc
50 Nf7a3b4789dcd4027bdfe1e0b96c278c7
51 sg:journal.1015319
52 schema:name Prognostic value of nonlinear heart rate dynamics in hemodialysis patients with coronary artery disease
53 schema:pagination 641-648
54 schema:productId N40e7775d6f6c4032bb51db9451952914
55 N5d3a10cecebe4c50940cee6f35a4ea3c
56 N8665455ff0574918962e28ec663a6d09
57 Nd96ccaf87b6640ebbdde36a05dbc6256
58 Ne3d65105aff346e9971ca340f89b20de
59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031532222
60 https://doi.org/10.1046/j.1523-1755.2003.00131.x
61 schema:sdDatePublished 2019-04-10T15:38
62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
63 schema:sdPublisher Nbc62f659fca64499ad3ffe28684d8b5a
64 schema:url http://www.nature.com/ki/journal/v64/n2/full/4493925a.html
65 sgo:license sg:explorer/license/
66 sgo:sdDataset articles
67 rdf:type schema:ScholarlyArticle
68 N21b43d4926c84d5f83c4420395e15e14 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Prognosis
70 rdf:type schema:DefinedTerm
71 N2a6e4635737f432ebc1ceb3005ea7928 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name Coronary Artery Disease
73 rdf:type schema:DefinedTerm
74 N3b2b455442a74b3a91ddc8b1a61850bc schema:issueNumber 2
75 rdf:type schema:PublicationIssue
76 N40e7775d6f6c4032bb51db9451952914 schema:name nlm_unique_id
77 schema:value 0323470
78 rdf:type schema:PropertyValue
79 N42ca385b68744fce93d9c8e191b4ab49 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
80 schema:name Aged
81 rdf:type schema:DefinedTerm
82 N460dc093a91b42a69faab522159af4b5 rdf:first sg:person.01117743421.03
83 rdf:rest N5249698ead8943099bc0684feda877f6
84 N47afc7909c1d4d91823288f83b1cb883 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Female
86 rdf:type schema:DefinedTerm
87 N482415e4a3b344c7accb7062ce21d0ed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Middle Aged
89 rdf:type schema:DefinedTerm
90 N4dc39a8dd98748a6bdf9cec4ca224e2f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Renal Dialysis
92 rdf:type schema:DefinedTerm
93 N5048f74c73ee404692a1fc4f43f849f9 rdf:first sg:person.01241043201.53
94 rdf:rest rdf:nil
95 N52495afa8316417abff6bbe587d9d6e9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Risk Factors
97 rdf:type schema:DefinedTerm
98 N5249698ead8943099bc0684feda877f6 rdf:first sg:person.01234172021.61
99 rdf:rest N97699c2353f34eb5a4b9938559be4632
100 N5d3a10cecebe4c50940cee6f35a4ea3c schema:name pubmed_id
101 schema:value 12846761
102 rdf:type schema:PropertyValue
103 N65b6fac6ed31456586172930f75aa9ed rdf:first sg:person.0610022227.46
104 rdf:rest N460dc093a91b42a69faab522159af4b5
105 N699b23d90821429f9cf8a296f386f60b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Humans
107 rdf:type schema:DefinedTerm
108 N75de873434c046b0a48a305f7a786cac rdf:first sg:person.01244244267.35
109 rdf:rest Nbec0a9f6388a4e279123effea355d7ea
110 N84c8866aee324c74bf0773ebe13eae7f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Male
112 rdf:type schema:DefinedTerm
113 N8665455ff0574918962e28ec663a6d09 schema:name doi
114 schema:value 10.1046/j.1523-1755.2003.00131.x
115 rdf:type schema:PropertyValue
116 N8c3921cd0e4143d3839b1755839570a6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Electrocardiography, Ambulatory
118 rdf:type schema:DefinedTerm
119 N8e61650ce9984e9e9132fc4312269599 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Retrospective Studies
121 rdf:type schema:DefinedTerm
122 N97699c2353f34eb5a4b9938559be4632 rdf:first sg:person.01247037400.65
123 rdf:rest Nc87ce60e459c4fee8706fc9dece8b93b
124 N9b01716089114b25bb92b78b3cbba5d2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Kidney Failure, Chronic
126 rdf:type schema:DefinedTerm
127 N9b599e9ab47f4c45b1b82f13a0e30db6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Predictive Value of Tests
129 rdf:type schema:DefinedTerm
130 N9cb9a3c1dfcc4991a8f733a0b46b1ac2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Nonlinear Dynamics
132 rdf:type schema:DefinedTerm
133 N9f3281939a1846dd86af4663ff22beb4 rdf:first sg:person.0774421303.15
134 rdf:rest Nc3ee303a67da435b9c3936217b6341de
135 Naa35a4e45e0e425a9f23703083659132 rdf:first sg:person.01130015667.17
136 rdf:rest N75de873434c046b0a48a305f7a786cac
137 Nb01690bb8d5747f1b4adb951b491c5aa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Follow-Up Studies
139 rdf:type schema:DefinedTerm
140 Nb5a1f89f154d4bf69b486ff273fcc0e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Heart Rate
142 rdf:type schema:DefinedTerm
143 Nbc62f659fca64499ad3ffe28684d8b5a schema:name Springer Nature - SN SciGraph project
144 rdf:type schema:Organization
145 Nbec0a9f6388a4e279123effea355d7ea rdf:first sg:person.0610702054.26
146 rdf:rest Nd284ebf0e841439cbfcd8a5adc39eb08
147 Nc3ee303a67da435b9c3936217b6341de rdf:first sg:person.01312407755.81
148 rdf:rest Naa35a4e45e0e425a9f23703083659132
149 Nc87ce60e459c4fee8706fc9dece8b93b rdf:first sg:person.0604372740.04
150 rdf:rest N5048f74c73ee404692a1fc4f43f849f9
151 Nd284ebf0e841439cbfcd8a5adc39eb08 rdf:first sg:person.0735501673.71
152 rdf:rest N65b6fac6ed31456586172930f75aa9ed
153 Nd96ccaf87b6640ebbdde36a05dbc6256 schema:name readcube_id
154 schema:value 9636b52d984b3706fac01fc1e35a324a01e21818b4a5e18215fe6706e74cb695
155 rdf:type schema:PropertyValue
156 Ndd2de4ba0e3e4c6b8b731f1c5c2be4ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Incidence
158 rdf:type schema:DefinedTerm
159 Ne3d65105aff346e9971ca340f89b20de schema:name dimensions_id
160 schema:value pub.1031532222
161 rdf:type schema:PropertyValue
162 Ne8a6c5c7359b4329a795563527ac455d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Survival Analysis
164 rdf:type schema:DefinedTerm
165 Nf7a3b4789dcd4027bdfe1e0b96c278c7 schema:volumeNumber 64
166 rdf:type schema:PublicationVolume
167 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
168 schema:name Medical and Health Sciences
169 rdf:type schema:DefinedTerm
170 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
171 schema:name Cardiorespiratory Medicine and Haematology
172 rdf:type schema:DefinedTerm
173 sg:journal.1015319 schema:issn 0085-2538
174 1523-1755
175 schema:name Kidney International
176 rdf:type schema:Periodical
177 sg:person.01117743421.03 schema:familyName Toriyama
178 schema:givenName Takanobu
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117743421.03
180 rdf:type schema:Person
181 sg:person.01130015667.17 schema:familyName Ishihara
182 schema:givenName Shinji
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01130015667.17
184 rdf:type schema:Person
185 sg:person.01234172021.61 schema:familyName Kawahara
186 schema:givenName Hirohisa
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234172021.61
188 rdf:type schema:Person
189 sg:person.01241043201.53 schema:familyName Kimura
190 schema:givenName Genjiro
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01241043201.53
192 rdf:type schema:Person
193 sg:person.01244244267.35 schema:familyName Sakata
194 schema:givenName Seiichiro
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01244244267.35
196 rdf:type schema:Person
197 sg:person.01247037400.65 schema:familyName Yajima
198 schema:givenName Kazuhiro
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247037400.65
200 rdf:type schema:Person
201 sg:person.01312407755.81 schema:familyName Hayano
202 schema:givenName Junichiro
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312407755.81
204 rdf:type schema:Person
205 sg:person.0604372740.04 schema:familyName Kobayashi
206 schema:givenName Kenji
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604372740.04
208 rdf:type schema:Person
209 sg:person.0610022227.46 schema:familyName Yokoya
210 schema:givenName Masaki
211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610022227.46
212 rdf:type schema:Person
213 sg:person.0610702054.26 schema:familyName Ohte
214 schema:givenName Nobuyuki
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610702054.26
216 rdf:type schema:Person
217 sg:person.0735501673.71 schema:familyName Takahashi
218 schema:givenName Hiroshi
219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0735501673.71
220 rdf:type schema:Person
221 sg:person.0774421303.15 schema:affiliation https://www.grid.ac/institutes/grid.260433.0
222 schema:familyName Fukuta
223 schema:givenName Hidekatsu
224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774421303.15
225 rdf:type schema:Person
226 sg:pub.10.1038/ki.1995.202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023397795
227 https://doi.org/10.1038/ki.1995.202
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1016/0002-9149(87)90795-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039930968
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1016/0895-7061(96)00152-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020743953
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1016/s0002-9149(00)01312-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011954925
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1016/s0002-9149(01)01578-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052912728
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1016/s0002-9149(01)01851-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051990876
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1016/s0002-9149(98)01076-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023817675
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1016/s0140-6736(97)11144-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037817965
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1053/ajkd.1998.v31.pm9531175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043187409
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1063/1.166141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057739643
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1093/ndt/12.5.884 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026166663
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1093/ndt/14.6.1480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031560282
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1111/j.1542-474x.2002.tb00154.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026388466
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1136/hrt.65.1.14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002104244
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1152/ajpheart.1994.266.4.h1643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082691656
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1152/ajpregu.1996.271.4.r1078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082991281
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1159/000189469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053622550
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1161/01.cir.101.1.47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033299517
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1161/01.cir.102.3.300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027971373
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1161/01.cir.93.5.1043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063337007
266 rdf:type schema:CreativeWork
267 https://doi.org/10.3109/00365518609083729 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043741864
268 rdf:type schema:CreativeWork
269 https://www.grid.ac/institutes/grid.260433.0 schema:alternateName Nagoya City University
270 schema:name Department of Internal Medicine and Pathophysiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
271 rdf:type schema:Organization
 




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


...