{
  "abstract": "Background: Mortality in sepsis-associated liver injury (SALI) remains high, and early risk stratification is difficult. Existing prognostic models commonly focus on a single endpoint, usually 28-day mortality, and rarely undergo independent external validation.\n\nMethods: We developed machine-learning models using clinical features recorded during the first 24 h to predict all-cause in-hospital mortality within 7, 14, and 28 days. In MIMIC-IV (v3.1), 1,550 adults meeting the prespecified SALI criteria were allocated to a training cohort (n = 1,119) and a held-out internal validation cohort (n = 431). An independent Chinese cohort from Xiaogan Central Hospital (n = 229; 28-day in-hospital mortality, 45.9%) was used for external validation. One hundred candidate pipelines were evaluated using stratified 10-fold cross-validation. SHAP was used to describe feature attribution across prediction horizons.\n\nResults: AUROCs in the held-out internal validation cohort were 0.840, 0.841, and 0.850 for 7-, 14-, and 28-day mortality, respectively; corresponding external AUROCs were 0.720, 0.717, and 0.763. The external 28-day model outperformed modified SOFA (0.763 vs. 0.673; P = 0.011) and MELD-Na (0.763 vs. 0.636; P = 0.002). A parsimonious logistic-regression sensitivity model achieved an external 28-day AUROC of 0.762, not significantly different from the full model (P = 0.970). The 14-day model was miscalibrated externally; recalibration improved the calibration slope from 0.563 to 0.970 but did not materially improve the Brier score. SHAP rankings varied across horizons and were interpreted descriptively rather than causally.\n\nConclusion: The models showed moderate transportability to one independent retrospective cohort. Multicentre prospective validation is needed before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Critical Care Medicine Department, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology , , ,",
    "Medical College, Wuhan University of Science and Technology , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hao Yao"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhaopu Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Jinxiao Lü"
    },
    {
      "affs": [
        0
      ],
      "name": "Wei Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dy65dzjhdnygr.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1942203",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719580/?tool=EBI",
  "id": "dy65dzjhdnygr",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719580",
  "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": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2713|Sepsis Diagnosis and Treatment"
    ]
  ],
  "openalex": "W7222726224",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719580/pdf/?tool=EBI",
  "pmcid": "PMC13719580",
  "pmid": "42964336",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dy65dzjhdnygr.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719580",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719580"
    },
    {
      "first_seen": "2026-11-02T12:23:31Z",
      "native_id": "PMC13719580",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1942203"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Sepsis Diagnosis and Treatment",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Development and external validation of interpretable machine learning models for 7-, 14-, and 28-day mortality in sepsis-associated liver injury",
  "title_text": "Development and external validation of interpretable machine learning models for 7-, 14-, and 28-day mortality in sepsis-associated liver injury",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:37Z",
  "url": "https://research.draconitic.com/paper/dy65dzjhdnygr",
  "venue": "Frontiers in medicine",
  "versions": []
}