Machine Learning to Predict Acute Care During Cancer Therapy

Completed

• Conditions: Chemotherapeutic Toxicity

• Registration Number: NCT05122247
• Lead Sponsor: Duke University

Brief 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.

Study Timeline

• Study First Submitted: 2021-11-05
• Study First Submitted QC: 2021-11-05
• Study First Posted: 2021-11-16
• Study Start: 2022-01-03
• Status Verified: 2023-09
• Primary Completion: 2023-09-19
• Study Completion: 2023-09-19
• Last Update Submitted: 2023-09-19
• Last Update Posted: 2023-09-21

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 12000

Inclusion Criteria

• had treatment encounter in the Duke Medical Oncology department from January 7th, 2019 to June 30th, 2019
• DUHS medical record available

Exclusion Criteria

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
number of unplanned of hospital admission or emergency department visits during systemic therapy	12 months

Trial Locations

Locations (1)

Duke University Health System; 🇺🇸 Durham, North Carolina, United States