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Development and multinational validation of an algorithmic strategy for high Lp(a) screening

Nature Cardiovascular Research volume 3, pages 558–566 (2024)Cite this article

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Abstract

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l−1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

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Fig. 1: Study design.
Fig. 2: Receiver operating characteristic curves for ARISE across the study cohorts and for the PCEs in the UKB held-out test set.
Fig. 3: OR of elevated Lp(a) across quartiles of ARISE score (predicted probability of elevated Lp(a)) for the study cohorts.
Fig. 4: Performance measures of ARISE across thresholds in the UKB held-out test set.
Fig. 5: The performance and NNT relative reduction of ARISE across demographic and clinical subgroups in the UKB held-out test set.
Fig. 6: Survival curves across those with high and low ARISE scores based on Lp(a) assessment.

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Data availability

The analyzed de-identified data are available for the UKB cohort from the Access Management System of the UKB and for the ARIC, CARDIA and MESA cohorts from the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) of the National Heart, Lung, and Blood Institute. The source data for Fig. 6 are available from the corresponding author upon request.

Code availability

Custom code used in this study is available at https://github.com/CarDS-Yale/ARISE.

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Acknowledgements

The study was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award R01HL167858 and K23HL153775 to R.K.) and the Doris Duke Charitable Foundation (under award 2022060 to R.K.). The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the paper; and decision to submit the paper for publication.

Author information

Author notes

  1. Twitter: @rohan_khera

Authors and Affiliations

  1. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Arya Aminorroaya, Lovedeep S. Dhingra, Evangelos K. Oikonomou, Phyllis Thangaraj, Sumukh Vasisht Shankar, Erica S. Spatz & Rohan Khera

  2. Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Seyedmohammad Saadatagah

  3. Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA

    Erica S. Spatz & Rohan Khera

  4. Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Rohan Khera

  5. Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA

    Rohan Khera

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Contributions

A.A. and R.K. conceived and designed the study. A.A. conducted statistical analyses. L.S.D., E.K.O., S.S., P.T., S.V., E.S., and R.K. interpreted the data. A.A. and L.S.D. wrote the first draft of the paper and prepared figures. E.K.O., S.S., P.T., S.V.S., E.S. and R.K. critically revised the paper. A.A. and S.V.S. made ARISE available online. R.K. acquired funding and supervised the study. All authors had full access to all data used in this study. All authors approved the final version for submission.

Corresponding author

Correspondence to Rohan Khera.

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Competing interests

R.K. is an associate editor at JAMA and receives research grant support, through Yale, from Bristol‐Myers Squibb, Novo Nordisk and BridgeBio. R.K. also receives support from the Blavatnik Foundation through the Blavatnik Fund for Innovation at Yale. R.K. is a cofounder of Ensight-AI, and R.K. and E.K.O. are cofounders of Evidence2Health, both representing precision health platforms to improve evidence-based cardiovascular care. E.K.O. has served as a consultant to Caristo Diagnostics Ltd. (Oxford, U.K.), unrelated to the current work. The other authors have no disclosures to report.

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Nature Cardiovascular Research thanks Dimitrios Doudesis, Erik Stroes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Lp(a) Assessment among the UK Biobank Participants.

Abbreviations: UKB, UK Biobank. The UK Biobank represents the largest cohort of individuals with protocolized Lp(a) assessment.

Extended Data Fig. 2 SHAP Values of ARISE’s Features Across UK Biobank Held-out Test Set, ARIC, CARDIA, and MESA Cohorts.

Abbreviations: ARIC, Atherosclerosis Risk in Communities; ASCVD, atherosclerotic cardiovascular disease; CARDIA, Coronary Artery Risk Development in Young Adults; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; MESA, Multi-Ethnic Study of Atherosclerosis; SHAP, SHapley Additive exPlanations; UKB, UK Biobank. Across the held out test set and the external validation cohorts, we describe the impact of the 6 ARISE features on the model output probability.

Extended Data Fig. 3 ARISE’s Performance and Number Needed to Test Relative Reduction Across Demographic and Clinical Subgroups in the ARIC Cohort.

Abbreviations: ARIC, Atherosclerosis Risk in Communities; ASCVD, atherosclerotic cardiovascular disease; AUROC, area under the receiver operating characteristic curve; CI, confidence interval; DM, diabetes mellitus; IHD, ischemic heart disease; NNT, number needed to test. The measure of center and the error bars represent the area under the receiver operating characteristic curve and the 95% confidence intervals.

Source data

Extended Data Fig. 4 ARISE’s Performance and Number Needed to Test Relative Reduction Across Demographic and Clinical Subgroups in the CARDIA Cohort.

Abbreviations: ASCVD, atherosclerotic cardiovascular disease; AUROC, area under the receiver operating characteristic curve; CARDIA, Coronary Artery Risk Development in Young Adults; CI, confidence interval; DM, diabetes mellitus; IHD, ischemic heart disease; NNT, number needed to test. *Comprises of less than 10 participants with elevated Lp(a). The measure of center and the error bars represent the area under the receiver operating characteristic curve and the 95% confidence intervals.

Source data

Extended Data Fig. 5 ARISE’s Performance and Number Needed to Test Relative Reduction Across Demographic and Clinical Subgroups in the MESA Cohort.

Abbreviations: ASCVD, atherosclerotic cardiovascular disease; AUROC, area under the receiver operating characteristic curve; CI, confidence interval; DM, diabetes mellitus; MESA, Multi-Ethnic Study of Atherosclerosis; NNT, number needed to test. The measure of center and the error bars represent the area under the receiver operating characteristic curve and the 95% confidence intervals.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–15.

Source data

Source Data Fig. 2

Raw data for receive operating characteristic curves.

Source Data Fig. 3

Raw odds ratios data for forest plots.

Source Data Fig. 4

Raw data for performance metrics across thresholds.

Source Data Fig. 5

Raw data for number of individuals, area under the receive operating characteristic curves and the relative reduction in number needed to treat.

Source Data Extended Data Fig. 3

Raw data for number of individuals, area under the receive operating characteristic curves and the relative reduction in number needed to treat.

Source Data Extended Data Fig. 4

Raw data for number of individuals, area under the receive operating characteristic curves and the relative reduction in number needed to treat.

Source Data Extended Data Fig. 5

Raw data for number of individuals, area under the receive operating characteristic curves and the relative reduction in number needed to treat.

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Aminorroaya, A., Dhingra, L.S., Oikonomou, E.K. et al. Development and multinational validation of an algorithmic strategy for high Lp(a) screening. Nat Cardiovasc Res 3, 558–566 (2024). https://doi.org/10.1038/s44161-024-00469-1

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