{
  "abstract": "The American Heart Association's PREVENT equations are now embedded in three major AHA/ACC guidelines governing hypertension, dyslipidemia, and cardiovascular-kidney-metabolic syndrome, including a specific risk threshold that gates initiation of GLP-1-based therapy and SGLT2 inhibitors. This rapid guideline uptake has outpaced a persistent limitation: PREVENT requires laboratory values, chiefly lipids and estimated glomerular filtration rate, that are frequently missing in the populations where risk prediction matters most, including patients seen in community health centers, emergency departments, and substance use treatment programs. Real-world validation studies across multiple health systems show that PREVENT underestimates cardiovascular risk most severely in these same subgroups, with the largest underestimation among Medicaid-insured, uninsured, and Black patients. Imputation, whether simple substitution or multiple imputation, does not resolve this problem because the probability that a laboratory value is missing is itself associated with the underlying risk the model is trying to estimate, a form of informative missingness that standard imputation methods are not designed to address. We argue that the field should invest in developing and validating a standalone cardiovascular risk prediction model that does not require laboratory measures. Such a model would function as a complement to PREVENT, not a replacement, providing risk estimates for patients without laboratory data and helping identify those who warrant expedited evaluation. As PREVENT becomes the gatekeeper for guideline-directed therapy across three clinical domains simultaneously, closing this coverage gap is no longer a methodological refinement but a clinical implementation imperative.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.",
    "Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA.",
    "Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.",
    "Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.",
    "Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.",
    "Center for Preventive Cardiology and Lipid Management, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.",
    "Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.",
    "Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA.",
    "Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
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        0,
        1,
        2
      ],
      "name": "Rebecca Arden Harris"
    },
    {
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        3
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      "name": "Warren B Bilker"
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    {
      "affs": [
        4,
        5
      ],
      "name": "Douglas S Jacoby"
    },
    {
      "affs": [
        4,
        6
      ],
      "name": "Michael G Levin"
    },
    {
      "affs": [
        0,
        2,
        7
      ],
      "name": "Richard C Wender"
    },
    {
      "affs": [
        2,
        8
      ],
      "name": "Sean Hennessy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0j6mdrrcbwbdj.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ajpc.2026.101782",
  "fulltext_url": "https://europepmc.org/articles/PMC13740879",
  "id": "0j6mdrrcbwbdj",
  "issns": [
    "2666-6677"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997027",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2739|Health Promotion and Cardiovascular Prevention"
    ]
  ],
  "openalex": "W7172423753",
  "pdf_url": "https://europepmc.org/articles/PMC13740879?pdf=render",
  "pmcid": "PMC13740879",
  "pmid": "42997027",
  "published": "2026-08-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0j6mdrrcbwbdj.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:42Z",
      "native_id": "MED:42997027",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997027"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Health Promotion and Cardiovascular Prevention",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "The coverage gap in PREVENT: Why cardiovascular risk prediction needs a lab-free complement.",
  "title_text": "The coverage gap in PREVENT: Why cardiovascular risk prediction needs a lab-free complement.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:42Z",
  "url": "https://research.draconitic.com/paper/0j6mdrrcbwbdj",
  "venue": "American journal of preventive cardiology",
  "versions": []
}