{
  "abstract": "BackgroundChronic lung disease (CLD) is an important cause of morbidity among postmenopausal women, yet simple non-invasive community screening tools are lacking. We aimed to develop and externally validate an interpretable multivariable prediction model for identifying prevalent self-reported physician-diagnosed CLD in this population.MethodsWe analyzed postmenopausal women from the US Health and Retirement Study (HRS; development n = 4,578, internal validation n = 1,962), with external validation in the English Longitudinal Study of Ageing (ELSA; n = 4,574) and a Chinese clinical cohort (n = 650). The primary outcome was prevalent self-reported or clinically recorded physician-diagnosed CLD, including chronic bronchitis, emphysema, or COPD where available. Predictors were selected via nested LASSO penalization. Twelve candidate statistical and machine-learning algorithms were benchmarked; the primary multivariable logistic regression model was evaluated on native uncalibrated probabilities at a locked screening threshold (9.81%) across discrimination, calibration, and clinical net benefit.ResultsThe current 9-predictor model retained smoking status, self-rated health, arthritis, heart disease, osteoporosis, basic activities of daily living difficulty, depressive symptoms, physical activity, and employment. The multivariable logistic regression model achieved AUROCs of 0.771 (95% CI, 0.748–0.793) in HRS development, 0.769 (0.734–0.803) in internal validation, 0.762 (0.735–0.790) in ELSA external validation, and 0.693 (0.634–0.750) in the Chinese cohort. Current smoking and poor self-rated health showed the strongest associations with prevalent CLD. In the Chinese clinical cohort, the model exhibited severe calibration underestimation (observed-to-expected ratio 2.763), demonstrating negative external validation driven by spectrum bias between community and hospital-based settings.ConclusionA parsimonious 9-predictor logistic regression model achieves acceptable discrimination and calibration across community cohorts for identifying prevalent self-reported physician-diagnosed CLD among community-dwelling postmenopausal women. However, direct transport to hospital-based settings is not supported without local recalibration because of pronounced spectrum bias.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Caizhong Du"
    },
    {
      "name": "Nana Xue"
    },
    {
      "name": "Liqin Dang"
    },
    {
      "name": "Gang Wang"
    },
    {
      "name": "Shiqin Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ajb04g2q0mvzm.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1982073",
  "fulltext_url": null,
  "id": "ajb04g2q0mvzm",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1982073",
  "language": null,
  "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": [
    [
      "feed-journal",
      "medicine/epidemiology-public-health"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/ajb04g2q0mvzm.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:18Z",
      "native_id": "10.3389/fpubh.2026.1982073",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1982073"
    },
    {
      "first_seen": "2026-11-05T12:21:30Z",
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      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1982073/full"
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  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/epidemiology-public-health",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Explainable machine learning and multivariable logistic regression for identifying prevalent self-reported physician-diagnosed chronic lung disease in postmenopausal women: development and multi-cohort external validation",
  "title_text": "Explainable machine learning and multivariable logistic regression for identifying prevalent self-reported physician-diagnosed chronic lung disease in postmenopausal women: development and multi-cohort external validation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T00:21:26Z",
  "url": "https://research.draconitic.com/paper/ajb04g2q0mvzm",
  "venue": "Frontiers in Public Health",
  "versions": []
}