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  1. Home
  3. Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction To Clinical Decision Support Using A Two-model Approach.
  1. Home
  3. Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction To Clinical Decision Support Using A Two-model Approach.

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Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction to Clinical Decision Support Using a

Jacob Newton Stein1,2, Soroush Fariman3, Yishu Zhang4

  • 1Division of Oncology, Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC.

JCO Oncology Practice
|March 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

New models predict acute care events in cancer patients, identifying high-risk individuals for preventive interventions. These models utilize clinical data to anticipate and update risk, improving patient outcomes.

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Area of Science:

  • Oncology
  • Health Services Research
  • Biostatistics

Background:

  • Acute care events (ACEs) are a significant burden for cancer patients, often preventable.
  • Existing prognostic risk models are underutilized for ACE prevention.
  • A Clinical Advisory Panel (CAP) was formed to overcome translational barriers and develop effective risk models.

Purpose of the Study:

  • To develop clinically and statistically valid prognostic models for predicting ACEs in cancer patients.
  • To enable risk-stratified interventions to reduce the incidence of preventable ACEs.
  • To address the translational gap in deploying prognostic models for cancer patient care.

Main Methods:

  • Patients aged 21+ initiating cancer treatment were identified from electronic health records.
  • Data were split into training (50%), validation (25%), and test (25%) sets.
  • Constrained elastic-net logistic regression was used, incorporating clinically informed coefficient constraints.

    • Main Results:

    • Two models were developed: a baseline model and a follow-up model incorporating clinical changes.
    • Key predictors included prior ACEs, chemotherapy, heart failure, abnormal INR, and advanced cancer stage.
    • Both models demonstrated acceptable statistical performance (C-statistic 0.71 and 0.70) in identifying high-risk patients.

    Conclusions:

    • A novel approach using two predictive models facilitates ACE risk assessment before and during cancer treatment.
    • Risk-stratified interventions targeting comorbidities, treatment toxicities, and disease stage are recommended for ACE prevention.
    • Clinical Advisory Panel (CAP) engagement was crucial in developing these clinically relevant models.