{
  "abstract": "Background: Traditional Cox proportional hazards models overestimate the risk of progression from peritoneal dialysis (PD) to hemodialysis (HD). This study aims to screen high-risk conversion populations using competing risk models.\n\nMethods: In this multicenter retrospective cohort study, we enrolled patients aged ≥16 years who were undergoing regular PD for >1 month; transition from PD to HD was the target event. When death was recognized as a competing event, we employed cause-specific proportional hazards and Fine-Gray models for multivariate analysis. A conventional LASSO-Cox model was also established for comparative purposes. Model performance was evaluated using time-dependent area under the curve (AUC) and C-index curves. The best model was identified to construct the cumulative incidence nomogram for PD to HD at the 3-, 6-, and 9-year time points.\n\nResults: We enrolled 700 valid samples that were randomly allocated at a ratio of 7:3 to the training (n = 491) and test sets (n = 209). The penalized Fine-Gray model was identified as the optimal model (AUC: 0.716, C-index: 0.662). Body mass index, history of peritonitis hospitalization, and nutrition-related parameters were identified as optimal predictors for transitioning from PD to HD. AUC values for the penalized Fine-Gray model at 3, 6, and 9 years were 0.675, 0.715, and 0.714, respectively. We constructed a nomogram for 3, 6, and 9 years based on the penalized Fine-Gray model, which demonstrated significant clinical utility.\n\nConclusion: The penalized Fine-Gray model serves as the optimal choice for predicting the transition from PD to HD, and the nomogram constructed based on this model exhibits favorable clinical applicability.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Nephrology, Daping Hospital, Army Medical University, Chongqing, China",
    "School of Mathematics and Statistic, Southwest University, Chongqing, China",
    "Department of Nephrology, Kaizhou District People’s Hospital, Chongqing, China",
    "Department of Nephrology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China",
    "State Key Laboratory of Trauma, Burns and Combined Injury, Wound Trauma Medical Center, Army Medical University, Chongqing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jingfang Wan"
    },
    {
      "affs": [
        1
      ],
      "name": "Qingyu Wei"
    },
    {
      "affs": [
        1
      ],
      "name": "Dong Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shihui Hou"
    },
    {
      "affs": [
        0
      ],
      "name": "Kaizhen Yv"
    },
    {
      "affs": [
        0
      ],
      "name": "Rui Tang"
    },
    {
      "affs": [
        2
      ],
      "name": "Jian He"
    },
    {
      "affs": [
        2
      ],
      "name": "Xuelian Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Yang Li"
    },
    {
      "affs": [
        3
      ],
      "name": "Yulin Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Jie Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Tingting Li"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Kehong Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9m523emxrp0tf.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1843381",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13730853/",
  "id": "9m523emxrp0tf",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://doi.org/10.3389/fmed.2026.1843381",
  "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",
      "Original Research"
    ],
    [
      "openalex",
      "2727|Dialysis and Renal Disease Management"
    ]
  ],
  "openalex": "W7223111051",
  "pdf_url": "https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1843381/pdf",
  "pmcid": "PMC13730853",
  "pmid": "42984727",
  "published": "2026-10-21",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9m523emxrp0tf.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:23:50Z",
      "native_id": "PMC13730853",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1843381"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2727|Dialysis and Renal Disease Management",
      "subfield": "general"
    }
  ],
  "title": "Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model",
  "title_text": "Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:10Z",
  "url": "https://research.draconitic.com/paper/9m523emxrp0tf",
  "venue": "Frontiers in Medicine",
  "versions": []
}