Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-11-17

AUTHORS

Chris Schilling, Duncan Mortimer, Kim Dalziel, Emma Heeley, John Chalmers, Philip Clarke

ABSTRACT

Background and ObjectiveMany guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications.MethodsWe employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning.ResultsWe found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95 % confidence interval [CI] 0.60–0.64).ConclusionsThere is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst’s toolkit when investigating healthcare decisions. More... »

PAGES

195-205

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40273-015-0342-3

DOI

http://dx.doi.org/10.1007/s40273-015-0342-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048051201

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26578402


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/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Australia", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cardiovascular Diseases", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Clinical Decision-Making", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Decision Trees", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "General Practitioners", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hypolipidemic Agents", 
        "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": "Practice Guidelines as Topic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Practice Patterns, Physicians'", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Primary Prevention", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Socioeconomic Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Surveys and Questionnaires", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Triglycerides", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1008.9", 
          "name": [
            "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schilling", 
        "givenName": "Chris", 
        "id": "sg:person.01306115473.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306115473.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Health Economics, Monash Business School, Monash University, 3800, Melbourne, VIC, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1002.3", 
          "name": [
            "Centre for Health Economics, Monash Business School, Monash University, 3800, Melbourne, VIC, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mortimer", 
        "givenName": "Duncan", 
        "id": "sg:person.01360061677.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360061677.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1008.9", 
          "name": [
            "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dalziel", 
        "givenName": "Kim", 
        "id": "sg:person.01356216720.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01356216720.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia", 
          "id": "http://www.grid.ac/institutes/grid.415508.d", 
          "name": [
            "The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heeley", 
        "givenName": "Emma", 
        "id": "sg:person.01247424450.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247424450.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia", 
          "id": "http://www.grid.ac/institutes/grid.415508.d", 
          "name": [
            "The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chalmers", 
        "givenName": "John", 
        "id": "sg:person.01275065504.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01275065504.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1008.9", 
          "name": [
            "Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Clarke", 
        "givenName": "Philip", 
        "id": "sg:person.0734456474.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734456474.38"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1748-5908-8-91", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014742380", 
          "https://doi.org/10.1186/1748-5908-8-91"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1748-5908-5-86", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003088454", 
          "https://doi.org/10.1186/1748-5908-5-86"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2261-11-46", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001962095", 
          "https://doi.org/10.1186/1471-2261-11-46"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1475-2840-8-25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027640629", 
          "https://doi.org/10.1186/1475-2840-8-25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00223-012-9616-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045625251", 
          "https://doi.org/10.1007/s00223-012-9616-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00058655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002929950", 
          "https://doi.org/10.1007/bf00058655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11033-011-1432-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052582965", 
          "https://doi.org/10.1007/s11033-011-1432-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/jhh.2008.83", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036188675", 
          "https://doi.org/10.1038/jhh.2008.83"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-11-17", 
    "datePublishedReg": "2015-11-17", 
    "description": "Background and ObjectiveMany guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications.MethodsWe employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n\u00a0=\u00a01903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning.ResultsWe found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95\u00a0% confidence interval [CI] 0.60\u20130.64).ConclusionsThere is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst\u2019s toolkit when investigating healthcare decisions.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s40273-015-0342-3", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7876485", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1102812", 
        "issn": [
          "1170-7690", 
          "1179-2027"
        ], 
        "name": "PharmacoEconomics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "34"
      }
    ], 
    "keywords": [
      "individual risk factors", 
      "lipid-lowering medications", 
      "risk factors", 
      "general practitioners", 
      "cardiovascular disease", 
      "prescribing decisions", 
      "sociodemographic factors", 
      "patient-level data", 
      "National Heart Foundation", 
      "Australian general practitioners", 
      "prescribing thresholds", 
      "prescribing patterns", 
      "Pharmaceutical Benefits Scheme", 
      "primary prevention", 
      "such medications", 
      "prescribing practices", 
      "prescribing information", 
      "patient survey", 
      "clinical assessment", 
      "GP prescribing", 
      "Heart Foundation", 
      "eligibility guidelines", 
      "clinical decision", 
      "AR guidelines", 
      "medications", 
      "ROC area", 
      "socioeconomic factors", 
      "patients", 
      "prescribing", 
      "characteristic curve", 
      "healthcare decisions", 
      "disease", 
      "useful addition", 
      "guidelines", 
      "Benefits Scheme", 
      "risk guidelines", 
      "little evidence", 
      "factors", 
      "MethodsWe", 
      "ConclusionsThere", 
      "prevention", 
      "ResultsWe", 
      "practice", 
      "cart", 
      "particular profile", 
      "aim", 
      "threshold", 
      "new insights", 
      "recruitment", 
      "assessment", 
      "evidence", 
      "period", 
      "practitioners", 
      "classification", 
      "background", 
      "CART model", 
      "curves", 
      "survey", 
      "decisions", 
      "profile", 
      "predominant driver", 
      "patterns", 
      "addition", 
      "data", 
      "favor", 
      "relationship", 
      "area", 
      "information", 
      "receiver", 
      "insights", 
      "regression trees", 
      "method", 
      "model", 
      "Ar", 
      "alliance", 
      "drivers", 
      "conditional relationship", 
      "foundation", 
      "toolkit", 
      "performance", 
      "trees", 
      "structure", 
      "hierarchy", 
      "sample performance", 
      "such structures", 
      "scheme", 
      "ObjectiveMany guidelines", 
      "absolute risk (AR) guidelines", 
      "administrative prescribing information", 
      "Framingham AR", 
      "National Vascular Disease Prevention Alliance", 
      "Vascular Disease Prevention Alliance", 
      "Disease Prevention Alliance", 
      "Prevention Alliance", 
      "conditional individual risk eligibility guidelines", 
      "individual risk eligibility guidelines", 
      "risk eligibility guidelines", 
      "analyst\u2019s toolkit"
    ], 
    "name": "Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease", 
    "pagination": "195-205", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1048051201"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s40273-015-0342-3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "26578402"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s40273-015-0342-3", 
      "https://app.dimensions.ai/details/publication/pub.1048051201"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_680.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s40273-015-0342-3"
  }
]
 

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/s40273-015-0342-3'

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/s40273-015-0342-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40273-015-0342-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40273-015-0342-3'


 

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

307 TRIPLES      22 PREDICATES      150 URIs      134 LITERALS      25 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s40273-015-0342-3 schema:about N1af4e20f2cb74c84b2de2c6ec79bd5a6
2 N2221aec4927c485fa056465f3a266efd
3 N23ed14b224d34992b8257e8f3c38d613
4 N41d96eb247dc4f70b28cf38232878e45
5 N4c2093ef47eb408d970b10fdf496bcbe
6 N661e895c2e274ea8ace099c14e129393
7 N67aed22b4bd74cd5a96e3808e750387b
8 N7c27a75aa6c246f48e12868632b3b736
9 N900348c6a8874f96b93d57853e3c8fac
10 N938c4a5d552d40918dbb078751174426
11 Nbba3173260624eeca9eafd8f408fd562
12 Nc06c594fd81a4da89d82356a1d799276
13 Nc138fcfea6d846ffb8d6a5f0012a78b2
14 Nc5f47ae5cd86471eb4d407e2f2e5b611
15 Nd4332960bf984130a6c1f090dcd37ae7
16 Ndd68736bcc2840968cf0089370db97f4
17 Nf97212b410404f6fb3f46a97a05ab7dd
18 Nfd335e9179b14f15bdeb96017bb82fee
19 anzsrc-for:11
20 anzsrc-for:1117
21 schema:author N9230f27bb65c40f98bd3445dcd178f93
22 schema:citation sg:pub.10.1007/bf00058655
23 sg:pub.10.1007/s00223-012-9616-3
24 sg:pub.10.1007/s11033-011-1432-8
25 sg:pub.10.1038/jhh.2008.83
26 sg:pub.10.1186/1471-2261-11-46
27 sg:pub.10.1186/1475-2840-8-25
28 sg:pub.10.1186/1748-5908-5-86
29 sg:pub.10.1186/1748-5908-8-91
30 schema:datePublished 2015-11-17
31 schema:datePublishedReg 2015-11-17
32 schema:description Background and ObjectiveMany guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications.MethodsWe employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning.ResultsWe found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95 % confidence interval [CI] 0.60–0.64).ConclusionsThere is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst’s toolkit when investigating healthcare decisions.
33 schema:genre article
34 schema:inLanguage en
35 schema:isAccessibleForFree true
36 schema:isPartOf N4d5075811fa542ffa6685cd6fa238e10
37 N7c01ef50ce734aa6a2e45beef0206bc9
38 sg:journal.1102812
39 schema:keywords AR guidelines
40 Ar
41 Australian general practitioners
42 Benefits Scheme
43 CART model
44 ConclusionsThere
45 Disease Prevention Alliance
46 Framingham AR
47 GP prescribing
48 Heart Foundation
49 MethodsWe
50 National Heart Foundation
51 National Vascular Disease Prevention Alliance
52 ObjectiveMany guidelines
53 Pharmaceutical Benefits Scheme
54 Prevention Alliance
55 ROC area
56 ResultsWe
57 Vascular Disease Prevention Alliance
58 absolute risk (AR) guidelines
59 addition
60 administrative prescribing information
61 aim
62 alliance
63 analyst’s toolkit
64 area
65 assessment
66 background
67 cardiovascular disease
68 cart
69 characteristic curve
70 classification
71 clinical assessment
72 clinical decision
73 conditional individual risk eligibility guidelines
74 conditional relationship
75 curves
76 data
77 decisions
78 disease
79 drivers
80 eligibility guidelines
81 evidence
82 factors
83 favor
84 foundation
85 general practitioners
86 guidelines
87 healthcare decisions
88 hierarchy
89 individual risk eligibility guidelines
90 individual risk factors
91 information
92 insights
93 lipid-lowering medications
94 little evidence
95 medications
96 method
97 model
98 new insights
99 particular profile
100 patient survey
101 patient-level data
102 patients
103 patterns
104 performance
105 period
106 practice
107 practitioners
108 predominant driver
109 prescribing
110 prescribing decisions
111 prescribing information
112 prescribing patterns
113 prescribing practices
114 prescribing thresholds
115 prevention
116 primary prevention
117 profile
118 receiver
119 recruitment
120 regression trees
121 relationship
122 risk eligibility guidelines
123 risk factors
124 risk guidelines
125 sample performance
126 scheme
127 sociodemographic factors
128 socioeconomic factors
129 structure
130 such medications
131 such structures
132 survey
133 threshold
134 toolkit
135 trees
136 useful addition
137 schema:name Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease
138 schema:pagination 195-205
139 schema:productId N15c4d91cde3f45b8a7e4d83d00c4bcea
140 Na6038ac5bb1f441ea870e102f6b78134
141 Nc07ab8ad38af4a8392aa9c684a850b05
142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048051201
143 https://doi.org/10.1007/s40273-015-0342-3
144 schema:sdDatePublished 2022-01-01T18:38
145 schema:sdLicense https://scigraph.springernature.com/explorer/license/
146 schema:sdPublisher N341025cb249b4bab822846d5ac6f95b3
147 schema:url https://doi.org/10.1007/s40273-015-0342-3
148 sgo:license sg:explorer/license/
149 sgo:sdDataset articles
150 rdf:type schema:ScholarlyArticle
151 N114d221e97254cd0a64ed5b4c6db5bad rdf:first sg:person.01356216720.78
152 rdf:rest N6f507f2f40c147b2aa170103c81bff5b
153 N15c4d91cde3f45b8a7e4d83d00c4bcea schema:name pubmed_id
154 schema:value 26578402
155 rdf:type schema:PropertyValue
156 N1af4e20f2cb74c84b2de2c6ec79bd5a6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Australia
158 rdf:type schema:DefinedTerm
159 N2221aec4927c485fa056465f3a266efd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Practice Patterns, Physicians'
161 rdf:type schema:DefinedTerm
162 N23ed14b224d34992b8257e8f3c38d613 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Male
164 rdf:type schema:DefinedTerm
165 N341025cb249b4bab822846d5ac6f95b3 schema:name Springer Nature - SN SciGraph project
166 rdf:type schema:Organization
167 N41d96eb247dc4f70b28cf38232878e45 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Primary Prevention
169 rdf:type schema:DefinedTerm
170 N4c2093ef47eb408d970b10fdf496bcbe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Humans
172 rdf:type schema:DefinedTerm
173 N4d5075811fa542ffa6685cd6fa238e10 schema:issueNumber 2
174 rdf:type schema:PublicationIssue
175 N50d4a13a5ac9406cb244987d86a615c4 rdf:first sg:person.01360061677.00
176 rdf:rest N114d221e97254cd0a64ed5b4c6db5bad
177 N661e895c2e274ea8ace099c14e129393 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Practice Guidelines as Topic
179 rdf:type schema:DefinedTerm
180 N67aed22b4bd74cd5a96e3808e750387b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
181 schema:name Surveys and Questionnaires
182 rdf:type schema:DefinedTerm
183 N6f507f2f40c147b2aa170103c81bff5b rdf:first sg:person.01247424450.53
184 rdf:rest Ne16c85d4f5fb44988a000f5996b67227
185 N7c01ef50ce734aa6a2e45beef0206bc9 schema:volumeNumber 34
186 rdf:type schema:PublicationVolume
187 N7c27a75aa6c246f48e12868632b3b736 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
188 schema:name Cardiovascular Diseases
189 rdf:type schema:DefinedTerm
190 N900348c6a8874f96b93d57853e3c8fac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Risk Factors
192 rdf:type schema:DefinedTerm
193 N9230f27bb65c40f98bd3445dcd178f93 rdf:first sg:person.01306115473.29
194 rdf:rest N50d4a13a5ac9406cb244987d86a615c4
195 N938c4a5d552d40918dbb078751174426 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Clinical Decision-Making
197 rdf:type schema:DefinedTerm
198 Na6038ac5bb1f441ea870e102f6b78134 schema:name doi
199 schema:value 10.1007/s40273-015-0342-3
200 rdf:type schema:PropertyValue
201 Nbba3173260624eeca9eafd8f408fd562 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
202 schema:name Decision Trees
203 rdf:type schema:DefinedTerm
204 Nc06c594fd81a4da89d82356a1d799276 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
205 schema:name General Practitioners
206 rdf:type schema:DefinedTerm
207 Nc07ab8ad38af4a8392aa9c684a850b05 schema:name dimensions_id
208 schema:value pub.1048051201
209 rdf:type schema:PropertyValue
210 Nc138fcfea6d846ffb8d6a5f0012a78b2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
211 schema:name Triglycerides
212 rdf:type schema:DefinedTerm
213 Nc5f47ae5cd86471eb4d407e2f2e5b611 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
214 schema:name Female
215 rdf:type schema:DefinedTerm
216 Nd4332960bf984130a6c1f090dcd37ae7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
217 schema:name Aged
218 rdf:type schema:DefinedTerm
219 Nd7f4900793564629914bcdfe79c30f50 rdf:first sg:person.0734456474.38
220 rdf:rest rdf:nil
221 Ndd68736bcc2840968cf0089370db97f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
222 schema:name Hypolipidemic Agents
223 rdf:type schema:DefinedTerm
224 Ne16c85d4f5fb44988a000f5996b67227 rdf:first sg:person.01275065504.75
225 rdf:rest Nd7f4900793564629914bcdfe79c30f50
226 Nf97212b410404f6fb3f46a97a05ab7dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
227 schema:name Middle Aged
228 rdf:type schema:DefinedTerm
229 Nfd335e9179b14f15bdeb96017bb82fee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
230 schema:name Socioeconomic Factors
231 rdf:type schema:DefinedTerm
232 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
233 schema:name Medical and Health Sciences
234 rdf:type schema:DefinedTerm
235 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
236 schema:name Public Health and Health Services
237 rdf:type schema:DefinedTerm
238 sg:grant.7876485 http://pending.schema.org/fundedItem sg:pub.10.1007/s40273-015-0342-3
239 rdf:type schema:MonetaryGrant
240 sg:journal.1102812 schema:issn 1170-7690
241 1179-2027
242 schema:name PharmacoEconomics
243 schema:publisher Springer Nature
244 rdf:type schema:Periodical
245 sg:person.01247424450.53 schema:affiliation grid-institutes:grid.415508.d
246 schema:familyName Heeley
247 schema:givenName Emma
248 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247424450.53
249 rdf:type schema:Person
250 sg:person.01275065504.75 schema:affiliation grid-institutes:grid.415508.d
251 schema:familyName Chalmers
252 schema:givenName John
253 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01275065504.75
254 rdf:type schema:Person
255 sg:person.01306115473.29 schema:affiliation grid-institutes:grid.1008.9
256 schema:familyName Schilling
257 schema:givenName Chris
258 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306115473.29
259 rdf:type schema:Person
260 sg:person.01356216720.78 schema:affiliation grid-institutes:grid.1008.9
261 schema:familyName Dalziel
262 schema:givenName Kim
263 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01356216720.78
264 rdf:type schema:Person
265 sg:person.01360061677.00 schema:affiliation grid-institutes:grid.1002.3
266 schema:familyName Mortimer
267 schema:givenName Duncan
268 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360061677.00
269 rdf:type schema:Person
270 sg:person.0734456474.38 schema:affiliation grid-institutes:grid.1008.9
271 schema:familyName Clarke
272 schema:givenName Philip
273 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734456474.38
274 rdf:type schema:Person
275 sg:pub.10.1007/bf00058655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
276 https://doi.org/10.1007/bf00058655
277 rdf:type schema:CreativeWork
278 sg:pub.10.1007/s00223-012-9616-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045625251
279 https://doi.org/10.1007/s00223-012-9616-3
280 rdf:type schema:CreativeWork
281 sg:pub.10.1007/s11033-011-1432-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052582965
282 https://doi.org/10.1007/s11033-011-1432-8
283 rdf:type schema:CreativeWork
284 sg:pub.10.1038/jhh.2008.83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036188675
285 https://doi.org/10.1038/jhh.2008.83
286 rdf:type schema:CreativeWork
287 sg:pub.10.1186/1471-2261-11-46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001962095
288 https://doi.org/10.1186/1471-2261-11-46
289 rdf:type schema:CreativeWork
290 sg:pub.10.1186/1475-2840-8-25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027640629
291 https://doi.org/10.1186/1475-2840-8-25
292 rdf:type schema:CreativeWork
293 sg:pub.10.1186/1748-5908-5-86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003088454
294 https://doi.org/10.1186/1748-5908-5-86
295 rdf:type schema:CreativeWork
296 sg:pub.10.1186/1748-5908-8-91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014742380
297 https://doi.org/10.1186/1748-5908-8-91
298 rdf:type schema:CreativeWork
299 grid-institutes:grid.1002.3 schema:alternateName Centre for Health Economics, Monash Business School, Monash University, 3800, Melbourne, VIC, Australia
300 schema:name Centre for Health Economics, Monash Business School, Monash University, 3800, Melbourne, VIC, Australia
301 rdf:type schema:Organization
302 grid-institutes:grid.1008.9 schema:alternateName Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia
303 schema:name Centre for Health Policy, School of Population and Global Health, University of Melbourne, 3051, Melbourne, VIC, Australia
304 rdf:type schema:Organization
305 grid-institutes:grid.415508.d schema:alternateName The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia
306 schema:name The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, 2050, Sydney, NSW, Australia
307 rdf:type schema:Organization
 




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


...