ArticlePharmacoEconomics2016
Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease.
Article in PharmacoEconomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 27 citations in OpenAlex.
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values.Health economics · 2022Article
- Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.Therapeutic advances in musculoskeletal disease · 2022Article
- Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study.Implementation science : IS · 2021Article
- Pharmacological treatment of increased vascular risk and cognitive performance in middle-aged and old persons: six-year observational longitudinal study.BMC neurology · 2020Observational
- Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑αWellcome open research · 2020Article
- An exploratory model for the non-fatal drowning risks in children in Guangdong, China.BMC public health · 2019Article
- Article
- Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors.Oncology letters · 2016Article
- Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis.PharmacoEconomics · 2016Article
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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