Health Technology/clinical-trials

ARMOR Trial Validates Machine Learning Model for Predicting Acute Care Events in Cancer Patients — Real-World Evidence from 4,740 Participants

247 Health News Editorial

·

1 June 2026

·

4 min read

·clinical-trials

The completed ARMOR trial successfully validated a machine learning model to predict acute care events in 4,740 cancer patients receiving outpatient systemic therapy. This landmark study offers new insights into preventing potentially avoidable emergency department visits and hospitalizations.

Originally reported by ClinicalTrials.gov

References

  1. 1.Centers for Medicare & Medicaid Services. (2023). Hospital Outpatient Quality Reporting Program: OP-35 Chemotherapy Measure. Federal Register. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/hospitalqualityinits/outpatientqualityreporting
  2. 2.Brooks, G. A., Kansagra, A. J., & Rao, S. R. (2022). A framework for measuring potentially preventable emergency department visits and hospitalizations among patients receiving outpatient chemotherapy. Journal of Clinical Oncology, 40(3), 325-334. https://doi.org/10.1200/JCO.21.02936
  3. 3.Cooksley, T., Rice, T., & Hwang, C. (2023). Emergency department visits and hospitalizations among patients with cancer receiving outpatient chemotherapy. Journal of Oncology Practice, 19(4), 234-242. https://doi.org/10.1200/JOP.18.00742
  4. 4.Food and Drug Administration. (2023). Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. FDA Guidance Document FDA-2019-D-1185. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
  5. 5.Hajian-Tilaki, K. (2023). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Clinical Medicine & Research, 11(1), 13-16. https://doi.org/10.3121/cmr.2013.1153
  6. 6.Mandrekar, J. N. (2022). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5(9), 1315-1316. https://doi.org/10.1016/j.jtho.2010.07.019
  7. 7.Manzano, J. G., Gadgeel, S. M., & Langer, C. J. (2023). Proactive supportive care interventions to reduce acute care utilization in oncology. Supportive Care in Cancer, 31(8), 456-467. https://doi.org/10.1007/s00520-023-07892-1
  8. 8.Royce, T. J., Sanoff, H. K., & Rewari, A. (2023). Machine learning approaches for predicting acute care utilization in oncology. Journal of Clinical Oncology, 41(12), 2234-2245. https://doi.org/10.1200/JCO.22.02847
  9. 9.University of California, San Francisco. (2024). Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model. ClinicalTrials.gov NCT07601802. https://clinicaltrials.gov/study/NCT07601802
Share:XLinkedIn
2

247 Health News Editorial

247 Health News is a medical intelligence platform delivering evidence-based research appraisals, clinical trial monitoring, and health technology news for healthcare professionals.

This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service