{
  "abstract": "Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over one-third of the global population, yet follow-up frequency for patients with mild-to-moderate fibrosis (F1–F2) remains undefined. Variable adherence to lifestyle modification and to pharmacotherapies such as resmetirom and semaglutide further complicates management, risking both resource overuse and undetected disease progression.\n\nMethods: We developed a Bayesian-informed expected net monetary benefit (NMB) decision framework comparing three strategies: aggressive follow-up (AF, visits every 6 months), conservative follow-up (CF, annual visits), and CF with mandatory between-visit Enhanced Liver Fibrosis testing (CF + ELF). Game theory concepts of adverse selection, moral hazard, and signaling motivated the model structure. Payoffs used published fibrosis transition probabilities, US EQ-5D-5L utilities, and Medicare costs at a willingness-to-pay threshold of $100,000/QALY. Adherence probability p was calibrated to therapy-specific proxy estimates. Sensitivity analyses varied the fibrosis improvement probability, the early-detection benefit of AF (0–75%), and the accountability effect of mandatory ELF (δ).\n\nResults: AF was preferred over CF when p < 0.559, and CF + ELF was preferred over AF when p > 0.787. For lifestyle modification ( p = 0.30), AF was model-preferred. For semaglutide ( p = 0.835) and resmetirom ( p = 0.855), CF + ELF was model-preferred if mandatory ELF reduced non-adherent progression by at least 68% and 77%, respectively, and CF otherwise. CF was model-preferred in the intermediate zone (0.559 < p < 0.787), which is relevant to future therapies. Thresholds were invariant to the fibrosis improvement probability, and therapy-specific recommendations held when AF reduced non-adherent progression by approximately 31–63%. Patients weakly preferred AF on health outcome grounds, strictly so when non-adherent, suggesting that observed preferences for CF reflect burden. In a worked Bayesian example, two consecutive worsening ELF results lowered a resmetirom patient’s adherence probability from 0.855 to 0.506, making escalation to AF model-preferred.\n\nDiscussion: Follow-up intensity in F1–F2 MASLD may be better calibrated to treatment context and adherence probability than to uniform schedules. A hybrid approach combining patient signaling with physician oversight, informed by non-invasive testing and social determinants of health, offers an adaptable framework across lifestyle and pharmacological treatment settings. Prospective estimation of the early-detection benefit of AF and the accountability effect of mandatory ELF is needed before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Siemens Medical Solutions USA Inc.",
    "Northwell Health Feinstein Institutes for Medical Research",
    "Donald and Barbara Zucker School of Medicine at Hofstra/Northwell",
    "Siemens Healthcare Diagnostics Inc.",
    "Medical Affairs, Universitat de Barcelona",
    "Instituto de Salud Global de Barcelona",
    "CUNY Graduate School of Public Health & Health Policy",
    "Division of Gastroenterology, Virginia Commonwealth University School of Medicine",
    "Thomas Jefferson University College of Population Health"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Artem T. Boltyenkov"
    },
    {
      "affs": [
        1
      ],
      "name": "Michael Eiswerth"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Pina C. Sanelli"
    },
    {
      "affs": [
        4,
        5,
        6
      ],
      "name": "Jeffrey V. Lazarus"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Jason J. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wanyi Chen"
    },
    {
      "affs": [
        7
      ],
      "name": "Szu-Yu Kao"
    },
    {
      "affs": [
        0,
        8
      ],
      "name": "Donald B. Chalfin"
    },
    {
      "affs": [
        1
      ],
      "name": "Kinpritma Sangha"
    },
    {
      "affs": [
        1
      ],
      "name": "Arun J. Sanyal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4s880e2ehx1s5.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1930169",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1930169/full",
  "id": "4s880e2ehx1s5",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1930169/full",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "openalex",
      "2713|Liver Disease Diagnosis and Treatment"
    ]
  ],
  "openalex": "W7223692050",
  "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1930169/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/4s880e2ehx1s5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:24Z",
      "native_id": "10.3389/fpubh.2026.1930169",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1930169/full"
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      "first_seen": "2026-10-30T13:47:23Z",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Liver Disease Diagnosis and Treatment",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A Bayesian decision framework for F1–F2 MASLD follow-up under adherence uncertainty",
  "title_text": "A Bayesian decision framework for F1–F2 MASLD follow-up under adherence uncertainty",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:23Z",
  "url": "https://research.draconitic.com/paper/4s880e2ehx1s5",
  "venue": "Frontiers in Public Health",
  "versions": []
}