Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease
- 276 Downloads
Background and 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.
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.
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).
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.
KeywordsAbsolute Risk Individual Risk Factor Pharmaceutical Benefit Scheme High Total Cholesterol National Heart Foundation
This work was supported by Monash University, the George Institute for Global Health, and the University of Melbourne.
Chris Schilling, Duncan Mortimer, Kim Dalziel, Emma Heeley, John Chalmers and Philip Clarke declare that they have no conflicts of interest.
CS, DM and KD conceptualized this report. All authors had input in developing the approach. CS produced multiple drafts. All authors provided input on the draft report and all read and approved the final report.
- 3.Greenland P, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines developed in collaboration with the American Society of Echocardiography, American Society of Nuclear Cardiology, Society of Atherosclerosis Imaging and Prevention, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. J Am Coll Cardiol. 2010;56(25):e50–103.CrossRefPubMedGoogle Scholar
- 4.Stone NJ, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Pt B):2889–934.Google Scholar
- 5.Lalor E, et al. National Vascular Disease Prevention Alliance. Guidelines for the management of absolute cardiovascular disease risk. 2012. ISBN 978–0–9872830–1–6. https://strokefoundation.com.au/~/media/strokewebsite/resources/treatment/absolutecvd_gl_webready.ashx?la=en.
- 6.National Heart Foundation of Australia. Guide to management of hypertension 2008. 2010. Available at: http://www.heartfoundation.org.au/SiteCollectionDocuments/HypertensionGuidelines2008to2010Update.pdf.
- 13.Hothorn T, et al. Party: a laboratory for recursive partytioning. 2010. Available at: https://cran.r-project.org/web/packages/party/vignettes/party.pdf.
- 16.Pharmaceutical Benefits Scheme. General statement for lipid-lowering drugs prescribed as pharmaceutical benefits. Pharmaceutical Benefits Scheme; 2014.Google Scholar
- 20.Timofeev R. Classification and regression trees (CART) theory and applications. Humboldt-Universitat zu Berlin, Wirtschaftswissenschaftliche Fakultat. 2004. http://edoc.hu-berlin.de/docviews/abstract.php?id=26951.
- 21.Tomcikova D, et al. Epidemiology, quality improvement and outcome: risk of in-hospital mortality identified according to the typology of patients with acute heart failure: classification tree analysis on data from the Acute Heart Failure Database–main registry. J Crit Care. 2013;28:250–8.CrossRefPubMedGoogle Scholar
- 24.Breiman L, et al. Classification and regression trees. Boca Raton: CRC Press; 1984.Google Scholar
- 25.Torgo L. Inductive learning of tree-based regression models. Universidada do Porto. Reitoria. 1999. http://repositorio-aberto.up.pt/handle/10216/10018.
- 28.The Mathworks Inc. Matlab and statistics toolbox release 2015a. Natick: The Mathworks; 2015.Google Scholar
- 29.Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI. 1995;2:1137–43.Google Scholar
- 31.Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123–40.Google Scholar
- 34.Lopert R, Henry D. The Pharmaceutical Benefits Scheme: economic evaluation works… but is not a panacea. Aust Prescr. 2002;25(6):126.Google Scholar