title: Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease.
abstract: OBJECTIVE
  Many 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  We 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).
  
  
  CONCLUSIONS
  There 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.
authors.affiliation: Centre for Health Policy, School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3051, Australia. chris.schilling@unimelb.edu.au.
authors.forename: Chris
authors.initials: C
authors.lastname: Schilling
authors.affiliation: Centre for Health Economics, Monash Business School, Monash University, Melbourne, VIC, 3800, Australia.
authors.forename: Duncan
authors.initials: D
authors.lastname: Mortimer
authors.affiliation: Centre for Health Policy, School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3051, Australia.
authors.forename: Kim
authors.initials: K
authors.lastname: Dalziel
authors.affiliation: The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, Sydney, NSW, 2050, Australia.
authors.forename: Emma
authors.initials: E
authors.lastname: Heeley
authors.affiliation: The George Institute for Global Health, The University of Sydney and the Royal Prince Alfred Hospital, Sydney, NSW, 2050, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Chalmers
authors.affiliation: Centre for Health Policy, School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3051, Australia.
authors.forename: Philip
authors.initials: P
authors.lastname: Clarke
mesh_terms: D000368:Aged; D001315:Australia; D002318:Cardiovascular Diseases / Q000209:etiology / Q000517:prevention & control*; D000066491:Clinical Decision-Making; D003663:Decision Trees; D005260:Female; D058005:General Practitioners / Q000706:statistics & numerical data; D006801:Humans; D000960:Hypolipidemic Agents / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D017410:Practice Guidelines as Topic; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011322:Primary Prevention; D012307:Risk Factors; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires; D014280:Triglycerides
chemical_list: D000960:Hypolipidemic Agents; D014280:Triglycerides