Machine learning-driven prioritization of lipoprotein(a) testing: model development and validation

European Journal of Preventive Cardiology

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.

Authors 11

1. Christophe A. T. Stevens corresponding

   Imperial College London

   Affiliation as printed

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

2. Fotios Barkas

   University of Ioannina · Imperial College London

   Affiliation as printed

   Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina , Ioannina ,

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

3. Julia Brandts Aachen Department of Medicine I

   Universitätsklinikum Aachen · Imperial College London

   Affiliation as printed

   Department of Medicine I, University Hospital Aachen , Aachen ,

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

4. Sunganani Kwilasi

   Imperial College London

   Affiliation as printed

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

5. Kanika Dharmayat

   Imperial College London

   Affiliation as printed

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

6. Amany K. Elshorbagy

   Imperial College London · Alexandria University

   Affiliation as printed

   Department of Physiology, Faculty of Medicine, University of Alexandria , Alexandria ,

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

7. Irene Karungi

   Imperial College London

   Affiliation as printed

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

8. Alireza S. Mahani
   Affiliation as printed

   Quantitative Research, Davidson Kempner Capital Management , NewYork ,

9. Mansour Taghavi Azar Sharabiani

   Imperial College London

   Affiliation as printed

   Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

10. Antonio J. Vallejo‐Vaz

    Consejo Superior de Investigaciones Científicas · Instituto de Salud Carlos III · Centro de Investigación Biomédica en Red · Instituto de Biomedicina de Sevilla · Hospital Universitario Virgen del Rocío · Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública · Imperial College London · Universidad de Sevilla

    Affiliation as printed

    Centro de Investigación Biomédica en Red (CIBER) de Epidemiología y Salud Pública, Instituto de Salud Carlos III , Madrid ,

    Clinical Epidemiology and Vascular Risk, Instituto de Biomedicina de Sevilla (IBiS), IBiS/Hospital Universitario Virgen del Rocío/Universidad de Sevilla/CSIC , Sevilla ,

    Department of Medicine, Faculty of Medicine, Universidad de Sevilla , Sevilla ,

    Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,

11. Kausik K Ray

    Imperial College London

    Affiliation as printed

    Department of Primary Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,