{
  "abstract": "Introduction Older adults with concomitant cardiogenic shock (CS) and sepsis represent a high-risk mixed-shock population in whom early prognosis is difficult. We aimed to develop and externally validate an interpretable machine-learning model for ICU mortality. Methods Patients aged ≥65 years with concomitant CS and sepsis were identified from MIMIC-IV v3.1 (n=1,219) and eICU-CRD v2.0 (n=166). MIMIC-IV was stratified 70:30 into training (n=853) and internal validation (n=366), while eICU-CRD was reserved for external validation. Feature selection used the intersection of Boruta and LASSO in the training cohort. Ten models were tuned by stratified 5-fold cross-validation; the primary model family was selected by the highest mean training cross-validated AUROC before validation. Performance assessment included bootstrap 95% confidence intervals, calibration, decision curve analysis, and SHAP. Results Nine predictors were retained: AKI, T2DM, respiratory rate, lactate, creatinine, BUN, ALT, SOFA, and SAPS II. Stacking ranked first by the training-CV model-selection rule (mean CV AUROC=0.690). AUROC was 0.742 (95% CI 0.690-0.791) in internal validation and 0.633 (0.546-0.718) in external validation. At the locked threshold, external sensitivity was 0.750 and specificity 0.441. The external Brier score of 0.287 exceeded the prevalence-only null Brier score of 0.237 (scaled Brier=-0.212). SOFA alone showed similar external discrimination (AUROC=0.646) with a lower Brier score (0.226), indicating no clear external advantage of the ensemble over simpler benchmarks. Cross-database standardized mean differences demonstrated substantial distribution shift. Conclusions The training-selected Stacking model showed moderate internal discrimination but limited external discrimination and probabilistic calibration, without a clear external advantage over simpler benchmarks. The model should be considered investigational pending prospective evaluation and site-specific validation or recalibration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Han Chen"
    },
    {
      "name": "Yuming Wu"
    },
    {
      "name": "Pu Chen"
    },
    {
      "name": "Zijian Lin"
    },
    {
      "name": "Qingjuan Zheng"
    },
    {
      "name": "Ling Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5kw227vegady1.bib",
  "cited_by": 0,
  "doi": "10.1177/20552076261501511",
  "fulltext_url": "https://doi.org/10.1177/20552076261501511",
  "id": "5kw227vegady1",
  "issns": [
    "2055-2076"
  ],
  "landing_url": "https://doi.org/10.1177/20552076261501511",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "2713|Sepsis Diagnosis and Treatment"
    ]
  ],
  "openalex": "W7223246307",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "SAGE Publishing",
  "ris": "https://research.draconitic.com/paper/5kw227vegady1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:25Z",
      "native_id": "1452b6c7ad314fd0a7403ae1786147c3",
      "source": "doaj",
      "url": "https://doi.org/10.1177/20552076261501511"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Sepsis Diagnosis and Treatment",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "An interpretable machine learning model for predicting intensive care unit mortality risk in elderly patients with concomitant cardiogenic shock and sepsis: Development and external validation using two large critical care databases",
  "title_text": "An interpretable machine learning model for predicting intensive care unit mortality risk in elderly patients with concomitant cardiogenic shock and sepsis: Development and external validation using two large critical care databases",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:50Z",
  "url": "https://research.draconitic.com/paper/5kw227vegady1",
  "venue": "Digital Health",
  "versions": []
}