NCT05122247

Machine Learning to Predict Acute Care During Cancer Therapy

Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 12,000 (actual)
• Sponsor: Duke University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.

Detailed description

A previously described machine learning (ML)-based model accurately predicted ED visits or hospitalizations for cancer patients undergoing radiation therapy or chemoradiation. An IRB approved prospective randomized trial, SHIELD-RT (NCT04277650) found that preemptive intervention for patients undergoing radiation and chemoradiation based on the ML model's risk stratification decreased the relative risk of acute care visits by 50%, showing that ML-guided escalation of care improved personalized supportive care and treatment compliance while decreasing healthcare costs. The objective of this study is to apply this validated ML based model to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Once validated, this study will add to the previously published body of evidence supporting a randomized trial evaluating the ML algorithm's ability to assign intervention for patients receiving systemic therapy at highest risk for acute care encounters.

Conditions

• Chemotherapeutic Toxicity

Interventions

Type	Name	Description
OTHER	Machine learning algorithm	machine learning directed identification of chemotherapy patients at high-risk for emergency department acute care and/or hospitalization

Timeline

• Start date: 2022-01-03
• Primary completion: 2023-09-19
• Completion: 2023-09-19
• First posted: 2021-11-16
• Last updated: 2023-09-21

Locations

1 site across 1 country: United States