NCT05122247

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
12,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2022

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

November 5, 2021

Completed
11 days until next milestone

First Posted

Study publicly available on registry

November 16, 2021

Completed
2 months until next milestone

Study Start

First participant enrolled

January 3, 2022

Completed
1.7 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

September 19, 2023

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

September 19, 2023

Completed
Last Updated

September 21, 2023

Status Verified

September 1, 2023

Enrollment Period

1.7 years

First QC Date

November 5, 2021

Last Update Submit

September 19, 2023

Conditions

Chemotherapeutic Toxicity

Keywords

machine learningalgorithmoutpatient

Outcome Measures

Primary Outcomes (1)

  • number of unplanned of hospital admission or emergency department visits during systemic therapy

    12 months

Interventions

Machine learning algorithmOTHER

machine learning directed identification of chemotherapy patients at high-risk for emergency department acute care and/or hospitalization

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Duke patients undergoing chemotherapy who had at least one treatment encounter between 1/7/2019 and 6/30/2019

You may qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Duke University Health System

Durham, North Carolina, 27710, United States

Location

Related Publications (1)

  • Hong JC, Eclov NCW, Dalal NH, Thomas SM, Stephens SJ, Malicki M, Shields S, Cobb A, Mowery YM, Niedzwiecki D, Tenenbaum JD, Palta M. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation. J Clin Oncol. 2020 Nov 1;38(31):3652-3661. doi: 10.1200/JCO.20.01688. Epub 2020 Sep 4.

    PMID: 32886536BACKGROUND

MeSH Terms

Interventions

Machine Learning Algorithms

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Study Officials

  • Manisha Palta, MD

    Duke Health

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

November 5, 2021

First Posted

November 16, 2021

Study Start

January 3, 2022

Primary Completion

September 19, 2023

Study Completion

September 19, 2023

Last Updated

September 21, 2023

Record last verified: 2023-09

Data Sharing

IPD Sharing
Will not share

Locations

US(1)