Abstract
AIMS: Elevated lipoprotein(a) [Lp(a)] is a common risk factor for cardiovascular disease (CVD) affecting ∼1.4 billion people globally, with novel treatments under development. Guidelines recommend one-lifetime measurement, yet <1% are tested. Population-wide screening faces cost and implementation challenges. We developed a machine learning (ML) model to help prioritise patients for Lp(a) testing.
METHODS: Ethnicity-calibrated ML models were developed to identify individuals with elevated Lp(a) in UK Biobank. Participants ≥37 years old (N=438,579) were split into feature importance/selection(20%), derivation(60%), and validation(20%) datasets. Performances across risk-enhancing Lp(a) thresholds recommended by clinical guidelines (90, 125, 430 nmol/L) or entry criteria for ongoing Lp(a)-lowering trials (150, 175, 200 nmol/L) were evaluated. External validation was conducted in NHANES III.
RESULTS: Screening one million people using a universal approach would identify 222,717 cases above 90 nmol/L and 1950 above 430 nmol/L. In contrast, applying ML-targeted testing using the same number of tests would identify 280,899 (+26%; 95%CI:20-28%) and 6881 (+253%; 95%CI:192-310%) cases, respectively. At the thresholds of 125, 150, 175, and 200 nmol/L, yield increases were 38% (95%CI:35-40%), 51% (95%CI:47-54%), 59% (95%CI:55-63%), and 66% (95%CI:61-71%). Across thresholds 90-430 nmol/L, ML-targeted testing (Number Needed to Screen [NNS] 3.6-145, AUC 0.61-0.84) required 21%-72% fewer tests to identify one million cases. NHANES III validation demonstrated similar performance. Top 4 predictors included age, height (proxy for sex), total cholesterol and statin use.
CONCLUSION: A ML-guided approach to prioritise testing for elevated Lp(a) would require fewer tests to identify those above risk-enhancing thresholds or potentially eligible for emerging therapies, offering a scalable interim compromise between the low current testing rates and universal screening aspirations.