Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06538636
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-30
Brief Title:
Prediction Augmented Screening Initiative
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Prediction Augmented Screening Initiative
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PASI
Brief 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 eg 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None