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Research Snapshot Theater: Future of Critical Care, Pediatric I

A Machine Learning Computable Phenotype for Iatrogenic Withdrawal in Critically Ill Children

Tuesday, March 24, 2026
8:45 AM - 9:45 AM Central Time
Location: Connections Central - RST 03
  • AP

    Anita K. Patel, MD

    Childrens National Health Systems, District of Columbia

    Disclosure information not submitted.

  • HS

    Haoqiu Song, MS

    PhD candidate
    Virginia Polytechnic Institute, Virginia

    Disclosure information not submitted.

  • LW

    Layne Watson, PhD

    PhD
    Virginia Polytechnic Institute

    Disclosure information not submitted.

  • TO

    Taylor K. Olson, MD

    Assistant Professor of Pediatrics, Attending in PICU
    Children's National Health System, District of Columbia

    Disclosure information not submitted.

  • LZ

    Liqing Zhang, PhD

    Professor, Department of Computer Science,
    Virginia Polytechnic Institute

    Disclosure information not submitted.

  • MP

    Murray Pollack, MBA, MD

    Professor of Pediatrics with tenure
    Childrens National Health Systems

    Disclosure information not submitted.

Introduction: Iatrogenic Withdrawal Syndrome affects over 100,000 children annually, particularly following mechanical ventilation termination. Current nursing-based screening is time-consuming and episodic, limited by staffing availability. We developed and validated a computable phenotype using standard electronic health record variables to identify withdrawal through autonomic dysregulation patterns and medication histories.


Methods: This single-center, retrospective and prospective multi-cohort study included PICU patients from 2012-2025. Training/testing cohorts comprised 2,100 patients (2012-2023), with prospective validation in 400 patients (2023-2025). Inclusion criteria: extubation from mechanical ventilation and Withdrawal Assessment Tool (WAT-1) evaluation. Data were organized into 4-hour epochs. Variables included vital signs metrics (average, maximum, minimum, measurement frequency) and opioid/sedative exposures (cumulative dose, prior 4-hour dosage, average 4-hour dose). WAT-1 scores ≥3 defined withdrawal. Machine learning utilized XGBoost algorithm.


Results: Test cohort performance: AUROC 0.87, AUPRC 0.62, calibration curve R² 0.96 (slope 1.08, intercept -0.02). Prospective validation maintained performance despite lower withdrawal prevalence: AUROC 0.85, AUPRC 0.35, calibration R² 0.93 (slope 1.19, intercept -0.09). Pearson's correlation between computable phenotype risk and WAT-1 scores: r = 0.95 (p < 0.05). Prediction accuracy exceeded 87% at high/low WAT-1 values and ranged 54.8-73.3% near diagnostic thresholds.


Conclusions: We successfully developed and validated a novel machine learning computable phenotype using standard EHR data for pediatric ICU withdrawal risk identification. This objective, personalized approach demonstrated clinically suitable performance metrics with potential for real-time monitoring while reducing nursing administrative burden. The model's reliance on autonomic dysregulation indicators and medication profiles enables continuous, automated withdrawal surveillance in critically ill children.