Trial Title:
Prediction Augmented Screening Initiative
NCT ID:
NCT06538636
Condition:
Lung Cancer
Lung Cancer Screening
Conditions: Official terms:
Lung Neoplasms
Study type:
Interventional
Study phase:
N/A
Overall status:
Not yet recruiting
Study design:
Allocation:
Randomized
Intervention model:
Factorial Assignment
Intervention model description:
We will use a factorial stepped wedge design (SWD) over 15 calendar quarters, with 14
sequence arms and 2 trial sites randomized per sequence to support estimation of separate
and combined effects for two intervention types (1-PCP-facing tools and 2-LCS team
population management tools). Each half of the arms will sequentially implement one of
the interventions, followed by implementation of the second intervention
Primary purpose:
Screening
Masking:
None (Open Label)
Intervention:
Intervention type:
Other
Intervention name:
PCP-Facing Tools
Description:
PCP-facing tools will be turned on as a package: a) enhanced LCS clinical reminder
including DP+ generated smart message; b) Integrated one-click DP+ full decision tool for
automating personalized information about the net benefit of LCS based on patient
characteristics; c) the TurboHM tool: automated Health maintenance app for tracking all
preventive care, including a dedicated LCS section with personalized information
Arm group label:
LCS team population management tools
Arm group label:
PCP facing tools
Arm group label:
PCP facing tools plus LCS population management dashboard
Intervention type:
Other
Intervention name:
LCS team population management tools
Description:
site-specific dashboard and proactive outreach toolkit
Arm group label:
LCS team population management tools
Arm group label:
PCP facing tools
Arm group label:
PCP facing tools plus LCS population management dashboard
Summary:
Lung cancer is responsible for more deaths in the United States than breast, prostate and
colon cancer combined and is the number one cancer killer of Veterans. This is because
lung cancer is usually diagnosed when the disease has spread, and cure is less likely.
Lung cancer screening (LCS) finds cancer at an earlier stage when it is curable, yet only
20% of eligible Veterans have been screened. Uptake is even lower among Black Veterans
despite higher lung cancer risk. Using prediction models to identify high-benefit people
for whom LCS should be encouraged improves efficiency and reduces disparities. Moreover,
it is more patient-centered as shared decision-making conversations can be tailored with
personalized information. The US Preventive Services Task Force has called for research
to demonstrate that prediction-augmented LCS can be feasibly implemented at the
point-of-care. The investigators propose for VA to lead this effort with a large-scale
pragmatic clinical trial to show that prediction-augmented LCS is both feasible and
improves LCS uptake.
Detailed description:
Background: Despite large-randomized trials demonstrating the mortality benefit from lung
cancer screening (LCS) and a recommendation from the US Preventive Services Task Force
(USPSTF) and VHA since 2013, only 20% of eligible Veterans have received LCS. Uptake is
even lower among Black Veterans despite higher lung cancer risk. Current USPSTF
eligibility criteria of age and smoking history are simple, but do not incorporate the
heterogeneity in lung cancer risk and life expectancy across people and leads to
exclusion of some persons, especially Blacks, with potential for high benefit from LCS.
While the USPSTF acknowledged that using prediction models to augment simple eligibility
criteria is more efficient and equitable, they stopped short of recommending
prediction-augmented LCS, noting that a pragmatic trial was needed to demonstrate that
prediction-augmented LCS can be feasibly implemented in real-world settings and assess
its impact on LCS uptake. Significance: By demonstrating the real-world feasibility of
prediction-augmented LCS, and its ability to improve LCS uptake especially in those of
high-benefit, the VA as a learning healthcare system will influence national LCS
guidelines and improve LCS outcomes both inside and outside the VA. Innovation & Impact:
Prediction-augmented LCS is based on strong evidence, yet implementing this approach
would represent paradigm shift from typical preventive cancer screening. The proposed
work is a unique opportunity for the VA to advance implementation of more equitable,
personalized LCS by improving on the status quo of making broad 'one-size-fits-all'
recommendations. The innovation is the advancement of primary care-facing and population
management informatics tools that present individualized information on how strongly to
encourage LCS, with the potential to be expanded to other cancer screenings. Specific
Aims: 1. Conduct a pragmatic stepped wedge (site-level) factorial trial comparing usual
care (USPSTF criteria) versus prediction-augmented LCS (supported by primary care-facing
informatics tools, LCS team population management tools, external facilitation) on
effectiveness at increasing LCS uptake. 2. Determine what drives implementation success
of prediction-augmented LCS in various contexts, using mixed methods. Methodology: The
investigators will utilize a factorial stepped wedge design in the Lung Precision
Oncology Program network to establish the effectiveness and evaluate the implementation
of precision-augmented LCS. Veterans assigned a PCP at a participating site and who meet
inclusion criteria based on Clinical Data Warehouse data will be passively enrolled in
the study: 1) patients meeting USPSTF criteria for LCS; OR 2) patients whose predicted
LCS benefit exceeds a stringent high-benefit threshold of life gained with annual LCS (to
capture high-benefit Veterans currently excluded by USPSTF criteria). The primary outcome
is the percentage of eligible subjects who complete LCS during each study quarter. The
factorial design allows us to discern the effect on LCS uptake of primary care-facing vs
LCS team population management tools. Secondary outcomes include uptake among Veterans
with the highest predicted benefit from LCS, uptake among high-benefit Black Veterans,
effects on lung cancer detection rates, LCS outcomes (e.g., invasive procedures,
complications) and projected lung cancer deaths avoided among additional people screened
due to the interventions, and care gaps in which LCS was ordered but not completed.
Implementation evaluation will be guided by the i-PARIHS framework using mixed methods.
This approach combines data drawn from across all trial sites (tool usage data, clinician
surveys, facilitation data, patient interviews) to gain a broad view of implementation,
with in-depth ethnographic assessments from 6 selected sites that will provide deeper
insights into how and why implementation succeeded (or faced challenges) in different
contexts. Our findings will inform creation of an implementation playbook to support
enterprise-wide spread of prediction-augmented LCS. Next Steps / Implementation: The
investigators will work with our operational partners to spread implementation of
prediction-augmented LCS across the VA enterprise.
Criteria for eligibility:
Criteria:
Inclusion Criteria:
Veterans assigned a PCP at a participating site and who meet inclusion criteria at any
point during the study timeframe will be enrolled into the trial. There will be two paths
to patient inclusion:
- meeting USPSTF eligibility criteria for LCS, as currently encoded in the background
logic of LCS clinical reminders maintained by the VA National Center for Lung Cancer
Screening (i.e., age 50-80 years; smoked 20 pack-years; current smoking or quit <15
years ago) OR
- predicted benefit calculated using LYFS-CTVA model exceeds a stringent high-benefit
threshold of life-year gains with annual LCS, as recommended in the 2021 CHEST LCS
guidelines
Exclusion Criteria:
- Veterans who have previously undergone lung cancer screening, are diagnosed with
lung cancer, or who do not meet eligibility criteria outlined above
Gender:
All
Minimum age:
N/A
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
VA Bedford HealthCare System, Bedford, MA
Address:
City:
Bedford
Zip:
01730-1114
Country:
United States
Contact:
Last name:
Gemmae Fix, PhD
Email:
gemmae.fix@va.gov
Contact backup:
Last name:
Renda Wiener
Email:
renda.wiener@va.gov
Facility:
Name:
VA Boston Healthcare System Jamaica Plain Campus, Jamaica Plain, MA
Address:
City:
Boston
Zip:
02130-4817
Country:
United States
Contact:
Last name:
Renda Wiener, MD
Email:
Renda.Wiener@va.gov
Contact backup:
Last name:
Gemmae Fix
Email:
gemmae.fix@va.gov
Facility:
Name:
VA Ann Arbor Healthcare System, Ann Arbor, MI
Address:
City:
Ann Arbor
Zip:
48105-2303
Country:
United States
Contact:
Last name:
Tanner Caverly, MD
Email:
tanner.caverly@va.gov
Contact backup:
Last name:
Sarah Dorin
Email:
sarah.dorin@va.gov
Facility:
Name:
Ralph H. Johnson VA Medical Center, Charleston, SC
Address:
City:
Charleston
Zip:
29401-5703
Country:
United States
Contact:
Last name:
Qwaneshia Wilson, MS
Phone:
843-789-6710
Phone ext:
206710
Email:
Qwaneshia.Wilson@va.gov
Contact backup:
Last name:
Sarah A Jackson, BA MA
Phone:
(843) 789-6700
Email:
sarah.jackson@va.gov
Investigator:
Last name:
Nichole T Tanner, MD MS BS
Email:
Principal Investigator
Start date:
June 2, 2025
Completion date:
September 30, 2029
Lead sponsor:
Agency:
VA Office of Research and Development
Agency class:
U.S. Fed
Source:
VA Office of Research and Development
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06538636
