{
  "abstract": "Background/Objectives: Acute aortic syndrome (AAS) is an underdiagnosed, time-critical medical emergency. Initial misdiagnosis as acute coronary syndrome (ACS) is common, delaying definitive treatment and exposing patients to inappropriate antithrombotic therapy. We aimed to develop and temporally test a machine learning (ML) model using routine admission variables to distinguish AAS from ACS. Methods: In this single-center retrospective study, we included 2828 consecutive adults with confirmed AAS or ACS (September 2018–April 2025); the initial 1979 patients formed the development cohort and the subsequent 849, a temporally separated test cohort. Least absolute shrinkage and selection operator regression selected predictors from nine prespecified candidates. Seven algorithms were fitted and assigned fixed Youden-derived cutoffs in the development cohort, then applied to the test cohort. Results: Six predictors were selected: D-dimer, leukocyte count, troponin I, C-reactive protein, oxygen saturation, and ST-segment changes. In the test cohort (112 AAS [13.2%]; 737 ACS), area under the receiver operating characteristic curve (AUC) ranged from 0.925 to 0.956 across the seven models. Extreme gradient boosting (XGBoost) showed the highest observed AUC (0.956; 95% confidence interval (CI), 0.935–0.976) and the lowest Brier score (0.047; 95% CI, 0.037–0.057), with sensitivity 0.884, specificity 0.931, and negative predictive value 0.981 at the development-cohort-derived cutoff (0.199); decision curve analysis indicated net benefit within the AAS/ACS test cohort. Shapley additive explanations ranked D-dimer as the dominant contributor. Conclusions: Six routine admission variables discriminated AAS from ACS across all seven ML algorithms in this single-center cohort. These findings support further prospective evaluation in a broader diagnostic population.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Xinshi District, Urumqi 830054, China",
    "The First Department of Gastroenterology, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Xinshi District, Urumqi 830054, China",
    "Hammersmith Medicines Research, Nucleus Network, 44 Cumberland Avenue, London NW10 7EW, UK",
    "Cardiology and Aortic Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London SW3 6NP, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xinliang Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Baozhu Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Tiantian Tang"
    },
    {
      "affs": [
        2
      ],
      "name": "Suzy Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiang Ma"
    },
    {
      "affs": [
        3
      ],
      "name": "Christoph A. Nienaber"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5v61ckbd5sc5r.bib",
  "cited_by": 0,
  "doi": "10.3390/jcm15218708",
  "fulltext_url": null,
  "id": "5v61ckbd5sc5r",
  "issns": [
    "2077-0383"
  ],
  "landing_url": "https://www.mdpi.com/2077-0383/15/21/8708",
  "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": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-07",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/5v61ckbd5sc5r.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:41Z",
      "native_id": "10.3390/jcm15218708",
      "source": "crossref",
      "url": "https://www.mdpi.com/2077-0383/15/21/8708"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Accurate Differentiation of Acute Aortic Syndrome from Acute Coronary Syndrome: A Machine Learning Diagnostic Model Based on Routine Admission Variables",
  "title_text": "Accurate Differentiation of Acute Aortic Syndrome from Acute Coronary Syndrome: A Machine Learning Diagnostic Model Based on Routine Admission Variables",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:41Z",
  "url": "https://research.draconitic.com/paper/5v61ckbd5sc5r",
  "venue": "Journal of Clinical Medicine",
  "versions": []
}