Using Classification and Regression Trees ( @entity18527 ) to Identify Prescribing Thresholds for XXXX .

@entity1 @entity455 @entity6 @entity1674 @entity18527 @entity65
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 ( @entity18527 ) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of @entity65 ( @entity65 ). 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 @entity1 -level data from a clinical assessment and @entity1 survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for @entity1 (n  =  1903) who were not using such medications prior to recruitment. @entity18527 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, @entity6 , @entity1674 and a history of @entity65 ), 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 ( @entity1674 and @entity65 history) only become relevant for @entity1 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 @entity455 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 @entity18527 provides new insights into prescribing decisions. Overall, @entity18527 is a useful addition to the analyst's toolkit when investigating healthcare decisions.
