{
  "abstract": "Objectives\n\nEarly recognition of critical deterioration events (CDEs) in pediatric intensive care units (PICUs) is crucial to improving outcomes, but the real-time performance of machine-learning (ML)-based alert systems is rarely assessed. We prospectively evaluated the performance, operational utility, and deployment feasibility of the PICU Warning Index (P-WIN), a personalized ML alert system predicting CDE in the PICU.Materials and methods\n\nIn this prospective cohort study at a quaternary-care children's hospital, P-WIN estimated deterioration risk for PICU patients aged 0-24 years at 30-minute intervals between January 7 and April 7, 2026 using electronic health record data collected via Fast Healthcare Interoperability Resources and proprietary EPIC API endpoints. CDEs included cardiopulmonary resuscitation, initiation of extracorporeal membrane oxygenation, dilute (10 μg/mL) epinephrine bolus, or unplanned endotracheal intubation. P-WIN was compared to baseline workflows: an automated PICU Warning Tool and a clinician-huddle-based assessment. Primary measures were CDE-level sensitivity, admission-level positive predictive value (PPV), and alerts per patient-day.Results\n\nAmong 1005 PICU patients, 99 CDEs were experienced by 62 patients. P-WIN detected 73% of these CDE 1-24 hours in advance, outperforming the PICU Warning Tool (39%, P < .05) and huddle workflow (23%, P < .05). P-WIN generated 0.21 (95% CI, 0.18-0.23) alerts per patient-day. The PICU Warning Tool and huddle workflow had similar PPV to P-WIN.Conclusion\n\nP-WIN detected significantly more CDEs than existing PICU Warning tool and huddle workflows while maintaining a similar PPV. Our findings underscore the feasibility of ML-based personalized warning systems to enhance shared situational awareness of PICU deterioration.",
  "abstract_shown": true,
  "affiliations": [
    "Tsui Laboratory, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19146, United States.",
    "Division of Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.",
    "Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA 19104, United States.",
    "Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19146, United States.",
    "Digital and Technology Services, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.",
    "Center for Healthcare Quality and Analytics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.",
    "Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Eamonn Tweedy",
      "orcid": "0009-0007-2311-8852"
    },
    {
      "affs": [
        1,
        2,
        3
      ],
      "name": "Sanjiv Mehta"
    },
    {
      "affs": [
        0
      ],
      "name": "Sachin Grover"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Hannah R Stinson"
    },
    {
      "affs": [
        0
      ],
      "name": "Victor M Ruiz"
    },
    {
      "affs": [
        4
      ],
      "name": "Erika Pleskunas"
    },
    {
      "affs": [
        5
      ],
      "name": "James Sannino"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Akira Nishisaki"
    },
    {
      "affs": [
        0,
        1,
        2,
        6
      ],
      "name": "Fuchiang Rich Tsui",
      "orcid": "0000-0002-6383-8471"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7r86h9pgfprxx.bib",
  "cited_by": 0,
  "doi": "10.1093/jamiaopen/ooag202",
  "fulltext_url": "https://europepmc.org/articles/PMC13680097",
  "id": "7r86h9pgfprxx",
  "issns": [
    "2574-2531"
  ],
  "landing_url": "https://europepmc.org/article/MED/42899309",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7222982589",
  "pdf_url": "https://europepmc.org/articles/PMC13680097?pdf=render",
  "pmcid": "PMC13680097",
  "pmid": "42899309",
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7r86h9pgfprxx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42899309",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42899309"
    }
  ],
  "subjects": [],
  "title": "P-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit.",
  "title_text": "P-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:32Z",
  "url": "https://research.draconitic.com/paper/7r86h9pgfprxx",
  "venue": "JAMIA open",
  "versions": []
}