{
  "abstract": "Background:: Postoperative pulmonary infection (PPI) is a common complication after microsurgical clipping for aneurysmal subarachnoid hemorrhage (aSAH) and is associated with impaired recovery and greater use of hospital resources.\n\nObjective: To develop and temporally validate an explainable machine-learning model for PPI risk stratification and to explore the association between enteral nutrition (EN) initiation timing and subsequent PPI risk.\n\nMethods: This dual-cohort retrospective study was reported in accordance with Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) principles. Candidate predictors were selected using a combined least absolute shrinkage and selection operator (LASSO) regression and random-forest importance strategy. Nine machine-learning algorithms were developed and compared in terms of discrimination, calibration, and clinical utility using decision-curve analysis.\n\nResults: In the validation cohort, the Naive Bayes model showed the best overall performance [area under the receiver operating characteristic curve (AUC), 0.811; 95% confidence interval (CI), 0.755–0.855]. SHAP analysis identified the major contributors to the primary 24-h prediction model. Separately, a secondary exploratory analysis of continuous EN initiation timing identified a nonlinear pattern, with a bootstrap median model-derived inflection point of 24.0 h (95% bootstrap interval, 23.0–27.0 h).\n\nConclusions: This explainable model may support early PPI risk stratification after aSAH clipping. The observed EN timing signal should be interpreted as exploratory and warrants prospective validation before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Nursing, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital , ,",
    "Department of Neurosurgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lin Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Shufang Shi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5phm6gwnd1hev.bib",
  "cited_by": 0,
  "doi": "10.3389/fneur.2026.1873566",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13686907/?tool=EBI",
  "id": "5phm6gwnd1hev",
  "issns": [
    "1664-2295"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13686907",
  "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": [
    [
      "openalex",
      "2728|Intracranial Aneurysms: Treatment and Complications"
    ]
  ],
  "openalex": "W7221007273",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13686907/pdf/?tool=EBI",
  "pmcid": "PMC13686907",
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/5phm6gwnd1hev.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "PMC:PMC13686907",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13686907"
    },
    {
      "first_seen": "2026-10-30T12:20:16Z",
      "native_id": "10.3389/fneur.2026.1873566",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1873566/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2728|Intracranial Aneurysms: Treatment and Complications",
      "subfield": "neurology"
    }
  ],
  "title": "Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study",
  "title_text": "Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:16Z",
  "url": "https://research.draconitic.com/paper/5phm6gwnd1hev",
  "venue": "Frontiers in neurology",
  "versions": []
}