Clinical trial NCT05122247: Machine Learning to Predict Acute Care During Cancer Therapy
Official title: Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Chemotherapeutic Toxicity
Keywords: machine learning
algorithm
outpatient
Lead sponsor: Duke University
Sponsor class: OTHER
Collaborators: name: University of California, San Francisco
First posted: 2021-11-16
Enrollment: 12000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Duke patients undergoing chemotherapy who had at least one treatment encounter between 1/7/2019 and 6/30/2019

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.

Eligibility criteria: 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:

-

Primary outcomes:
measure: number of unplanned of hospital admission or emergency department visits during systemic therapy
timeFrame: 12 months
Secondary outcomes:
Other outcomes:
Locations:
city: Durham
country: United States
facility: Duke University Health System
state: North Carolina
Study officials:
affiliation: Duke Health
name: Manisha Palta, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.