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.

Prediction Augmented Screening Initiative

Lung Cancer

Lung Cancer Screening

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.

PCP-Facing Tools

LCS team population management tools

the probability of receiving an initial LDCT LCS scan at a given site among eligible
subjects during each quarter of the study (quarter assigned by date of the LDCT order as
this event is most proximal to the LCS decision; passage of time between order and scan
completion could lead to erroneous attribution of primary outcome to the wrong study
phase if scan date is used

LCS orders and uptake among high-benefit Veterans

LCS orders and uptake among high-benefit Black Veterans

LCS orders and uptake among eligible but non-high-benefit Veterans and among those with
high comorbidity

invasive procedures resulting from lung cancer screening exam findings

Complications following invasive procedures done as a result of LCS

this will be calculated among additional people screened due to study interventions

Prediction Augmented Screening Initiative

About this trial

Study ID

Status

Type

Phase

Placebo

Accepting

Not accepting

Trial Timing

What is this trial about?

What are the participation requirements?

Locations

Location

Status