{
  "abstract": "Background\n\nInvasive candidiasis carries disproportionate mortality in hospitalised patients, yet the clinical determinants distinguishing invasive from non-invasive disease remain inconsistently characterised across healthcare settings. This study aimed to identify independent predictors of invasive candidiasis and in-hospital mortality, describe species distribution and antifungal resistance patterns, and evaluate machine learning models for risk stratification.Methods\n\nA retrospective episode-level analysis was conducted on 206 laboratory-confirmed Candida-positive microbiological episodes at a single secondary care centre. Binary logistic regression univariable and multivariable was used to identify predictors of invasive candidiasis and all-cause mortality. Four machine learning classifiers (Random Forest, Gradient Boosting, Extra Trees, Logistic Regression) were developed and internally validated using five-fold stratified cross-validation. Permutation feature importance was applied for model explainability.Results\n\nOf 206 episodes, 37 (18.0%) were invasive. Overall, in-hospital mortality among invasive cases was 73.0%. Elderly age (≥ 65 years; aOR 53.22) and male sex (aOR 4.38) independently predicted invasive disease. Elderly age (aOR 5.12), male sex (aOR 4.96), and diabetes mellitus (aOR 2.60) were independent mortality predictors. Non-albicans species predominated among invasive isolates (73.0%), with Candida glabrata and Candida parapsilosis most over-represented. Fluconazole and caspofungin resistance were detected in 3/20 (15.0%) and 2/21 (9.5%) tested invasive isolates, respectively. The Random Forest classifier achieved AUROC values of 0.987 and 0.776 for invasive candidiasis and mortality prediction, respectively; the near-perfect invasive-candidiasis performance should be interpreted as exploratory and largely driven by marked age-based separation.Conclusion\n\nElderly age, male sex, and diabetes mellitus consistently predicted invasive disease and/or mortality in this episode-level cohort. Machine learning identified these routinely available variables as dominant discriminative features, but the models remain exploratory and require patient-level and external validation before clinical deployment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Microbiology, College of Medicine and Health Sciences, National University of Science and Technology, Sohar, Oman. drmohan123@gmail.com.",
    "Department of Microbiology, College of Medicine and Health Sciences, National University of Science and Technology, Sohar, Oman.",
    "All India Institute of Medical Sciences, Mangalagiri, Hyderabad, India.",
    "Department of Pathology and Blood Bank, Sohar Hospital, Sohar, Oman.",
    "Department of Physiology, Faculty of Medicine, Neelain University, Khartoum city, AL, Sudan. Shahenazsatti@gmail.com."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohan Bilikallahalli Sannathimmappa",
      "orcid": "0000-0002-3429-7004"
    },
    {
      "affs": [
        1
      ],
      "name": "Vinod Nambiar"
    },
    {
      "affs": [
        1
      ],
      "name": "Mohammmed Al Amin Ali Alsharawy"
    },
    {
      "affs": [
        1
      ],
      "name": "Motassim Kamran Baig"
    },
    {
      "affs": [
        1
      ],
      "name": "Hussein Muqdad Mahmood"
    },
    {
      "affs": [
        1
      ],
      "name": "Ahmed Ali Abudlamir Al-Shamaa"
    },
    {
      "affs": [
        1
      ],
      "name": "Miysaa Ali Abdallah Alabri"
    },
    {
      "affs": [
        1
      ],
      "name": "Rayyan Mohammed Almaskri"
    },
    {
      "affs": [
        2
      ],
      "name": "Rajeev Aravindakshan"
    },
    {
      "affs": [
        3
      ],
      "name": "Salima Al-Maqbali"
    },
    {
      "affs": [
        4
      ],
      "name": "Shahenaz Satti"
    },
    {
      "affs": [
        1
      ],
      "name": "Rayaan Chowdhury"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/73k7ye13h8me3.bib",
  "cited_by": 0,
  "doi": "10.1186/s12879-026-14105-9",
  "fulltext_url": "https://europepmc.org/articles/PMC13721703",
  "id": "73k7ye13h8me3",
  "issns": [
    "1471-2334"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958078",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2725|Antifungal resistance and susceptibility"
    ]
  ],
  "openalex": "W7171931929",
  "pdf_url": "https://europepmc.org/articles/PMC13721703?pdf=render",
  "pmcid": "PMC13721703",
  "pmid": "42958078",
  "published": "2026-07-31",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/73k7ye13h8me3.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:47Z",
      "native_id": "MED:42958078",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958078"
    },
    {
      "first_seen": "2026-11-03T12:22:21Z",
      "native_id": "PMC13721703",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12879-026-14105-9"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2725|Antifungal resistance and susceptibility",
      "subfield": "infectious-disease"
    }
  ],
  "title": "Clinical predictors, species distribution, and machine learning-based risk stratification of invasive candidiasis and in-hospital mortality: a retrospective episode-level cohort analysis.",
  "title_text": "Clinical predictors, species distribution, and machine learning-based risk stratification of invasive candidiasis and in-hospital mortality: a retrospective episode-level cohort analysis.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:21Z",
  "url": "https://research.draconitic.com/paper/73k7ye13h8me3",
  "venue": "BMC infectious diseases",
  "versions": []
}