A Fast-And-Frugal Tree for Case Finding of Diagnosed Angina Among Adults Not Receiving Cardiovascular Treatment: A UK Biobank Analysis

Background: Adults with angina outside cardiovascular care may miss preventive treatment. We developed a transparent fast-and-frugal tree (FFT) for case finding in adults not receiving cardiovascular treatment. Methods: Cross-sectional analysis of UK Biobank data. The outcome was self-reported doctor-diagnosed angina. FFTs were developed from 39 candidate predictors in 294,238 participants with complete data, in all participants (primary FFT) and in those receiving neither cholesterol-lowering nor blood pressure medication (refined FFT), and compared with machine-learning models and the Pooled Cohort Equations. Results: Overall, 8970 participants (3.0%) had diagnosed angina. The primary FFT selected only these two medications. Among 63,975 untreated test participants, the refined FFT used chest pain, age, family history of heart disease and triglycerides (sensitivity 0.682; specificity 0.789; positive predictive value 0.011). Its first three cues were stable across sensitivity analyses. Logistic regression and classification and regression trees performed similarly to the refined FFT, whereas the Pooled Cohort Equations performed less well (area under the curve 0.655). Conclusions: A four-cue FFT identified two-thirds of untreated adults with diagnosed angina while flagging about one in five, and may serve as a first-stage filter for clinical assessment in primary and community care. The tool requires external and prospective validation before clinical application.