{
  "abstract": "Antimicrobial resistance (AMR) drives a growing share of healthcare-associated infection (HAI) mortality; however, data resources from Chinese hospitals outside university referral networks remain scarce. We conducted a retrospective analysis of 39 months of prospectively collected, hospital-wide HAI surveillance data (February 2023–April 2026) from a 1,100-bed district-level general hospital (accredited Class III Grade A) in Chongqing, China, comprising 1,240 episodes among 1,054 patients across 256,993 admissions and 1.14 million patient-days. The hospital-wide episode incidence was stable at 0.48 per 100 admissions and 0.82 per 1,000 patient-days, with no seasonal pattern or residual autocorrelation. Acinetobacter baumannii was the leading pathogen (19.4% of isolates), with carbapenem resistance reaching 92.0% hospital-wide and 98.5% in the intensive care unit, exceeding national benchmarks. Mechanical ventilation, bedridden status, central venous catheterization, and stroke were enriched among resistant infections (odds ratios 2.4–5.2; all Bonferroni-adjusted p ≤ 0.001). We then benchmarked three transparent machine-learning baselines on routinely collected, pre-infection variables using patient-grouped, 10 × 5-fold repeated cross-validation. AMR phenotype prediction achieved a mean area under the receiver operating characteristic curve of 0.74–0.75 (area under the precision–recall curve 0.55–0.56 against a prevalence of 0.31) for logistic regression and random forest, whereas infection-site classification was not clinically useful (accuracy 0.39–0.52 versus a 0.46 majority-class baseline). A previously reported AUROC of 0.91 was traced to two forms of leakage (patient overlap between partitions and inclusion of total length of stay) and is corrected here. The de-identified dataset, bilingual codebook, and complete reproducibility pipeline are deposited under permissive licences, providing an open, leakage-audited benchmark for AMR prediction research in an under-represented hospital tier.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Dan Han"
    },
    {
      "name": "Yang Hu"
    },
    {
      "name": "Xin Shu"
    },
    {
      "name": "Hao Zou"
    },
    {
      "name": "Chong Wang"
    },
    {
      "name": "Xu Peng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9zde424backvj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-79197-4",
  "fulltext_url": null,
  "id": "9zde424backvj",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-79197-4",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9zde424backvj.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T06:20:25Z",
      "native_id": "10.1038/s41598-026-79197-4",
      "source": "feed-nature",
      "url": "https://www.nature.com/articles/s41598-026-79197-4"
    },
    {
      "first_seen": "2026-11-01T12:20:45Z",
      "native_id": "10.1038/s41598-026-79197-4",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-79197-4"
    }
  ],
  "subjects": [],
  "title": "Surveillance epidemiology and an open machine-learning benchmark of antimicrobial-resistant healthcare-associated infections in a Chinese district hospital",
  "title_text": "Surveillance epidemiology and an open machine-learning benchmark of antimicrobial-resistant healthcare-associated infections in a Chinese district hospital",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T00:20:25Z",
  "url": "https://research.draconitic.com/paper/9zde424backvj",
  "venue": "Scientific Reports",
  "versions": []
}