Prediction Augmented Screening Initiative (PASI)
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About
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.
Full 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.
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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
Trial design
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Interventional model
Masking
23,520 participants in 4 patient groups
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Central trial contact
Abby G Wenzel, PhD MS BS; Nichole T Tanner, MD MS BS
Data sourced from clinicaltrials.gov
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