{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T01:40:56Z","timestamp":1785289256082,"version":"3.55.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004336","name":"Novartis","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004336","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000761","name":"Imperial College London","doi-asserted-by":"publisher","award":["CTQJ230A1GB04T"],"award-info":[{"award-number":["CTQJ230A1GB04T"]}],"id":[{"id":"10.13039\/501100000761","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Elevated lipoprotein(a) [Lp(a)] is a common risk factor for cardiovascular disease affecting \u223c1.4 billion people globally, with novel treatments under development. Guidelines recommend one-lifetime measurement, yet &amp;lt;1% are tested. Population-wide screening faces cost and implementation challenges. We developed a machine learning (ML) model to help prioritize patients for Lp(a) testing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Ethnicity-calibrated ML models were developed to identify individuals with elevated Lp(a) in the UK Biobank. Participants \u226537 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\u2005nmol\/L) or entry criteria for ongoing Lp(a)-lowering trials (150, 175, 200\u2005nmol\/L) were evaluated. External validation was conducted in NHANES III. Screening 1 million people using a universal approach would identify 222 717 cases above 90\u2005nmol\/L and 1950 above 430\u2005nmol\/L. In contrast, applying ML-targeted testing using the same number of tests would identify 280 899 (+26%; 95% CI: 20\u201328%) and 6881 (+253%; 95% CI: 192\u2013310%) cases, respectively. At the thresholds of 125, 150, 175, and 200\u2005nmol\/L, yield increases were 38% (95% CI: 35\u201340%), 51% (95% CI: 47\u201354%), 59% (95% CI: 55\u201363%), and 66% (95% CI: 61\u201371%). Across thresholds 90\u2013430\u2005nmol\/L, ML-targeted testing (number needed to screen 3.6\u2013145, AUC 0.61\u20130.84) required 21\u201372% fewer tests to identify 1 million cases. NHANES III validation demonstrated similar performance. Top 4 predictors included age, height (proxy for sex), total cholesterol, and statin use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An ML-guided approach to prioritize 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurjpc\/zwag185","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:48:40Z","timestamp":1774615720000},"source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-driven prioritization of lipoprotein(a) testing: model development and validation"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7129-8524","authenticated-orcid":false,"given":"Christophe A T","family":"Stevens","sequence":"first","affiliation":[{"name":"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 ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5940-6895","authenticated-orcid":false,"given":"Fotios","family":"Barkas","sequence":"additional","affiliation":[{"name":"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 ,","place":["UK"]},{"name":"Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina , Ioannina 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Care and Public Health, School of Public Health, Imperial College London , School of Public Health Building, 90 Wood Lane, London W12 0BZ ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8624-860X","authenticated-orcid":false,"given":"Amany","family":"Elshorbagy","sequence":"additional","affiliation":[{"name":"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 ,","place":["UK"]},{"name":"Department of Physiology, Faculty of Medicine, University of Alexandria , Alexandria ,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene","family":"Karungi","sequence":"additional","affiliation":[{"name":"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 ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Mahani","sequence":"additional","affiliation":[{"name":"Quantitative Research, Davidson Kempner Capital Management , NewYork ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mansour T A","family":"Sharabiani","sequence":"additional","affiliation":[{"name":"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 ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio J","family":"Vallejo-Vaz","sequence":"additional","affiliation":[{"name":"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 ,","place":["UK"]},{"name":"Department of Medicine, Faculty of Medicine, Universidad de Sevilla , Sevilla ,","place":["Spain"]},{"name":"Clinical Epidemiology and Vascular Risk, Instituto de Biomedicina de Sevilla (IBiS), IBiS\/Hospital Universitario Virgen del Roc\u00edo\/Universidad de Sevilla\/CSIC , Sevilla ,","place":["Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red (CIBER) de Epidemiolog\u00eda y Salud P\u00fablica, Instituto de Salud Carlos III , Madrid ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kausik K","family":"Ray","sequence":"additional","affiliation":[{"name":"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 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