{
  "abstract": "ObjectiveTo develop and evaluate a dynamic model that repeatedly estimates the 30-day risk of a first grade [≥]2 immune-related adverse event (irAE) during immune checkpoint inhibitor (ICI) therapy.\n\nMaterials and MethodsAdults initiating ICI therapy during 2015-2022 comprised the development cohort; 2023 initiators formed a nonoverlapping internal temporal evaluation cohort. Every 21 days during the first treatment year, models used available EHR information to predict a first structured phenotype-defined grade [≥]2 irAE during the next 30 days. Evaluation included pooled and within-landmark discrimination, precision-recall performance, calibration, and patient-clustered bootstrap confidence intervals. Feature-state ablations and alternative update schedules examined the contribution of dynamic information.\n\nResultsDevelopment included 1,522 patients and 18,744 evaluable landmarks; temporal evaluation included 526 patients and 6,263 landmarks, of which 184 were positive (2.94%). The multiscale XGBoost model achieved pooled AUROC 0.716 (95% CI, 0.676-0.755), AUPRC 0.067, within-landmark AUROC 0.672 (95% CI, 0.631-0.716), Brier score 0.0281, and log loss 0.1245. The time-aware baseline achieved AUROC 0.631 and within-landmark AUROC 0.536. Updated treatment exposure and laboratory trajectories accounted for most of the gain. A complete-state extension achieved AUROC 0.719 and AUPRC 0.073. The 21-day schedule had the highest discrimination among tested cadences.\n\nConclusionLongitudinal EHR updating improved near-term irAE prediction beyond pretreatment characteristics and treatment time. Phenotype adjudication and independent evaluation of calibration, transportability, and clinical utility are needed before implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Columbia University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Gong"
    },
    {
      "name": "A. Agyekum"
    },
    {
      "name": "N. Aswani"
    },
    {
      "name": "H. R. Nieva"
    },
    {
      "name": "C. Hur"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7e6pnbaq54ntf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.06.26364907",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.06.26364907v1.full",
  "id": "7e6pnbaq54ntf",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.06.26364907v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2730|Cancer Immunotherapy and Biomarkers"
    ]
  ],
  "openalex": "W7220988082",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.06.26364907v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7e6pnbaq54ntf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:29Z",
      "native_id": "10.64898/2026.10.06.26364907",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.06.26364907v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Dynamic near-term risk prediction of clinically significant immune-related adverse events using longitudinal electronic health records",
  "title_text": "Dynamic near-term risk prediction of clinically significant immune-related adverse events using longitudinal electronic health records",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:29Z",
  "url": "https://research.draconitic.com/paper/7e6pnbaq54ntf",
  "venue": "medRxiv",
  "versions": []
}