{
  "abstract": "Introduction: Risk-based tools to guide care after percutaneous kidney biopsy (PKB) remain limited. We developed a simple preadmission risk score to predict post-PKB bleeding and support same-day discharge decisions, leveraging Japan’s routine inpatient observation practice.\n\nMethods: This retrospective study included 78,817 adults undergoing native PKB, using a nationwide Japanese inpatient database (2016–2021). Risk factors for early hemostatic intervention, defined as blood transfusion or surgical or interventional hemostasis within 6 days after PKB, were evaluated using logistic and L2-penalized multinomial logistic regressions. The fitted coefficients for day 0 were discretized to develop the Kidney biopsy Bleeding Index for Triage (K-BIT), using readily obtainable prebiopsy clinical variables. The scoring was internally validated using a held-out test cohort from the same dataset.\n\nResults: The overall bleeding rate was 1.46%. Compared with chronic glomerulonephritis, bleeding risk was higher in acute kidney injury (odds ratio [OR], 10.9; 95% confidence interval [CI], 8.55–13.9) and rapidly progressive glomerulonephritis (OR, 6.67; 95% CI 5.48–8.12). Increasing age and lower body mass index (BMI) were independently associated with higher bleeding risk. Age, prebiopsy diagnostic risk tier, BMI, and Charlson Comorbidity Index (CCI) were selected for score construction (score range, 0–9). In the validation cohort, patients with scores ≤ 2 (55.4%) had a bleeding risk of 0.37%. Simulation analysis suggested that restricting early discharge to this low-risk group could reduce hospitalization by an estimated 332 hospital days per 100 patients while maintaining low postdischarge bleeding risk.\n\nConclusion: Prebiopsy diagnosis, age, BMI, and comorbidities were key predictors of postbiopsy bleeding. K-BIT may support risk stratification and outpatient kidney biopsy pathways.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Nephrology, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan",
    "Department of Nephrology, Musashino Red Cross Hospital, Musashino, Tokyo, Japan",
    "M&D Data Science Centre, Institute of Integrated Research, Institute of Science Tokyo, Tokyo, Japan",
    "Department of Population Health Sciences, University of Bristol, Bristol, Bristol, UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rena Suzukawa"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Daiei Takahashi"
    },
    {
      "affs": [
        2
      ],
      "name": "Takanori Hasegawa"
    },
    {
      "affs": [
        0
      ],
      "name": "Shunsuke Inaba"
    },
    {
      "affs": [
        0
      ],
      "name": "Ken Ikenouchi"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Yuta Nakano"
    },
    {
      "affs": [
        1
      ],
      "name": "Yurina Yataka"
    },
    {
      "affs": [
        1
      ],
      "name": "Mizuki Watada"
    },
    {
      "affs": [
        1
      ],
      "name": "Motoki Hoshino"
    },
    {
      "affs": [
        1
      ],
      "name": "Wakana Shoda"
    },
    {
      "affs": [
        1
      ],
      "name": "Tamaki Kuyama"
    },
    {
      "affs": [
        0
      ],
      "name": "Eisei Sohara"
    },
    {
      "affs": [
        2
      ],
      "name": "Kiyohide Fushimi"
    },
    {
      "affs": [
        0
      ],
      "name": "Shinichi Uchida"
    },
    {
      "affs": [
        0
      ],
      "name": "Shintaro Mandai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/51214bcyd4rmb.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ekir.2026.107118",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13733331/",
  "id": "51214bcyd4rmb",
  "issns": [
    "2468-0249"
  ],
  "landing_url": "https://doi.org/10.1016/j.ekir.2026.107118",
  "language": "en",
  "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": [
    [
      "heading",
      "Clinical Research"
    ],
    [
      "openalex",
      "2727|Renal Diseases and Glomerulopathies"
    ]
  ],
  "openalex": "W7215117544",
  "pdf_url": "https://www.kireports.org/article/S2468-0249(26)03350-4/pdf",
  "pmcid": "PMC13733331",
  "pmid": "42985525",
  "published": "2026-10-01",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/51214bcyd4rmb.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:23:08Z",
      "native_id": "PMC13733331",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.ekir.2026.107118"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2727|Renal Diseases and Glomerulopathies",
      "subfield": "general"
    }
  ],
  "title": "Creation of a Pre-biopsy Clinical Risk Score for Major Bleeding After Kidney Biopsy",
  "title_text": "Creation of a Pre-biopsy Clinical Risk Score for Major Bleeding After Kidney Biopsy",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:23:08Z",
  "url": "https://research.draconitic.com/paper/51214bcyd4rmb",
  "venue": "Kidney International Reports",
  "versions": []
}