Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| 852795 | Other Identifier | University of Pennsylvania Institutional Review Board |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Eli Lilly and Company | INDUSTRY |
Not provided
Not provided
Not provided
Not provided
This study expands the application of an electronic health record (EHR) "nudge" used to prompt physicians' clinical practice to order molecular testing at the time of initial diagnosis for patients with specific types of advanced lung cancer. The primary goal is to have these test results available prior to starting treatment so that physicians can make molecularly-informed treatment decisions. The second goal is to better understand factors that contribute to whether or not the EHR-nudge implementation is successful.
At the University of Pennsylvania Health System (UPHS), a behavioral economics (BE) informed "nudge" strategy was piloted to guide physicians' clinical practice to include concurrent use of plasma and tissue-based next generation sequencing (NGS) testing at the time of initial diagnosis for patients with newly diagnosed metastatic non-squamous (mNSq) non-small cell lung cancer (NSCLC). These findings have demonstrated that behavioral, electronic health record (EHR)-based nudges are feasible and can promote guideline concordant diagnostic testing at both community and academic sites.
The overarching goal of this current trial is to expand the application of the BE informed nudges, which includes a Best Practice Advisory (BPA) and Electronic Decision Support Tool (e-CDS) approach, which has been operationalized within Epic, the EHR used at UPHS, to six satellite hospitals. Our central hypothesis is that this approach will dramatically increase adoption of comprehensive molecular testing and enhance the delivery of molecularly informed 1L therapy in patients with newly diagnosed mNSq NSCLC.
Intervention: A multicomponent BE-informed EHR-based nudge designed to facilitate comprehensive molecular testing by embedding a default P-NGS order into the EHR at the time of the NPV. If ordered, test results are incorporated into provider workflows and conveyed through electronic clinical decision support (e-CDS) notifications. This support program will notify clinicians of targetable mutations, potential clinical trials, as well as absence of mutations detected on plasma testing as a means of improving the timely delivery of molecularly informed therapy.
Study Design
Objective 1: In a stepped wedge cluster randomized trial of newly diagnosed patients with mNSq NSCLC, evaluate the effectiveness of a multicomponent BE-informed EHR-based nudge intervention at increasing timely receipt of comprehensive molecular test results prior to 1L therapy by incorporating P-NGS into the standard clinical workup.
The design of this trial will include 3 clusters, representing 6 community hospitals. There will be an initial period in which no clusters are exposed to the intervention. Subsequently, at regular intervals (the "steps") one cluster (or a group of clusters) will be randomized to cross from the control to the intervention under evaluation. This process will continue until all clusters have crossed over to be exposed to the intervention. At the end of the study there will be a period when all clusters are exposed. Data collection will continue throughout the study, so that each cluster will contribute observations under both control and intervention observation periods. Two years of baseline data will be obtained from all study sites for comparison.
Objective 2: Assess the contextual mechanisms influencing the adoption, reach, and effectiveness of EHR-based nudge interventions, with a lens for health equity in molecular testing using mixed-methods.
Using rigorous approaches proven successful in our prior work, we will recruit patient and clinician participants from each site to complete semi-structured interviews and structured questionnaires. The goal of this objective is to understand contextual mechanisms (e.g., patient, clinician, clinic, structural factors) shaping adoption, reach, and effectiveness of each intervention and identify how response may differ by key characteristics. These data will be analyzed using convergent mixed methods analysis, which employs the simultaneous collection and analysis of both quantitative and qualitative data to gain a comprehensive understanding of the multi-level factors shaping trial outcomes.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Penn Medicine New Jersey | Other | All sites with be randomized to implement the nudge at different points in time. Prospective data with be compared with each site's respective baseline numbers over a two-year period. |
|
| Penn Medicine Lancaster General Health | Other | All sites with be randomized to implement the nudge at different points in time. Prospective data with be compared with each site's respective baseline numbers over a two-year period. |
|
| Penn Presbyterian Medical Center | Other | All sites with be randomized to implement the nudge at different points in time. Prospective data with be compared with each site's respective baseline numbers over a two-year period. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| iNUDGE | Behavioral | Electronic health record nudge which prompts physicians to order plasma-based NGS testing for eligible patients with newly diagnosed lung cancer. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Availability of comprehensive molecular test results prior to first line therapy for patients with newly diagnosed mNSq NSCLC | Were comprehensive molecular test results available prior to initiation of 1L therapy? (Yes/No) | Measured up to 6 weeks from initial diagnosis |
| Measure | Description | Time Frame |
|---|---|---|
| Successful EHR based nudge delivery | Amongst eligible patients, calculate the proportion of patients for whom the EHR nudge fired successfully (Yes/No). Applicable for the patients enrolled in the time periods following randomization. | Measured up to 6 weeks from randomization |
| Turnaround time of delivery of provider focused alerts |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Charu Aggarwal, MD, MPH | Penn Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Penn Medicine Cherry Hill | Cherry Hill | New Jersey | 08003 | United States | ||
| Penn Medicine Princeton Health |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32918064 | Background | Aggarwal C, Rolfo CD, Oxnard GR, Gray JE, Sholl LM, Gandara DR. Strategies for the successful implementation of plasma-based NSCLC genotyping in clinical practice. Nat Rev Clin Oncol. 2021 Jan;18(1):56-62. doi: 10.1038/s41571-020-0423-x. Epub 2020 Sep 11. | |
| 34246791 | Background | Rolfo C, Mack P, Scagliotti GV, Aggarwal C, Arcila ME, Barlesi F, Bivona T, Diehn M, Dive C, Dziadziuszko R, Leighl N, Malapelle U, Mok T, Peled N, Raez LE, Sequist L, Sholl L, Swanton C, Abbosh C, Tan D, Wakelee H, Wistuba I, Bunn R, Freeman-Daily J, Wynes M, Belani C, Mitsudomi T, Gandara D. Liquid Biopsy for Advanced NSCLC: A Consensus Statement From the International Association for the Study of Lung Cancer. J Thorac Oncol. 2021 Oct;16(10):1647-1662. doi: 10.1016/j.jtho.2021.06.017. Epub 2021 Jul 8. |
Not provided
Not provided
A Clinical Study Report will be made available to the study sponsor.
Not provided
Not provided
Not provided
Not provided
Not provided
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol: Revised iNUDGE Protocol 04.08.2025 | Apr 8, 2025 | Jun 27, 2025 | Prot_001.pdf |
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan: Original Protocol 03.24.2023 | Mar 24, 2023 | Apr 3, 2023 | Prot_SAP_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan: iNUDGE Statistical Analysis Plan 04.30.2025 | Apr 30, 2025 | Jun 27, 2025 | SAP_002.pdf |
Not provided
This study employs a stepped-wedge cluster randomized clinical trial design. Randomization will occur at the group (site) level. Sites will be turned on according to the stepped-wedge cluster randomized design but will run in parallel.
Not provided
Not provided
This intervention is directed towards physicians. Individuals will not be randomized.
Not provided
Reported as number of days, median. Applicable for the patients enrolled in the time periods following randomization. |
| Measured up to 6 weeks from randomization |
| Completion of comprehensive molecular testing & modality used | Relative and absolute change in completion of comprehensive testing by tissue and plasma, plasma alone, or tissue alone will be tabulated. | Measured up to 3 months from initial diagnosis |
| Reasons for failure to complete comprehensive molecular testing: | Summarize reasons for failure of completion of testing i. Tissue related (QNS) ii. Patient related factors (unable to biopsy, patient declined biopsy etc.) iii. Assay related factors (plasma assay does not detect mutations) iv. Other | Measured up to 3 months from initial diagnosis |
| Time to molecularly informed treatment initiation | i. Calculated as time to therapy from the date of diagnosis of Stage IV disease (date of biopsy) ii. Calculated as time to therapy from the date of first new patient visit with medical oncology | Measured up to 6 weeks from initial diagnosis |
| Type of therapy received | i. Targeted therapy ii. Chemo-immunotherapy iii. Immunotherapy iv. Clinical trial or n v. None | Measured up to 3 months from initial diagnosis |
| Overall survival | i. Time from initial diagnosis to date of death or last follow up. ii. 1 year and 2-year overall survival rates will be calculated for the intervention group, and compared to baseline. | Measured up to 1 year from the time of randomization to death from any cause |
| Plainsboro |
| New Jersey |
| 08536 |
| United States |
| Penn Medicine Washington Township | Sewell | New Jersey | 08080 | United States |
| Penn Medicine Voorhees | Voorhees Township | New Jersey | 08043 | United States |
| Penn Medicine Lancaster General Health | Lancaster | Pennsylvania | 17602 | United States |
| Penn Presbyterian Medical Center | Philadelphia | Pennsylvania | 19104 | United States |
| 30964529 | Background | Singal G, Miller PG, Agarwala V, Li G, Kaushik G, Backenroth D, Gossai A, Frampton GM, Torres AZ, Lehnert EM, Bourque D, O'Connell C, Bowser B, Caron T, Baydur E, Seidl-Rathkopf K, Ivanov I, Alpha-Cobb G, Guria A, He J, Frank S, Nunnally AC, Bailey M, Jaskiw A, Feuchtbaum D, Nussbaum N, Abernethy AP, Miller VA. Association of Patient Characteristics and Tumor Genomics With Clinical Outcomes Among Patients With Non-Small Cell Lung Cancer Using a Clinicogenomic Database. JAMA. 2019 Apr 9;321(14):1391-1399. doi: 10.1001/jama.2019.3241. |
| 35313244 | Background | Robert NJ, Espirito JL, Chen L, Nwokeji E, Karhade M, Evangelist M, Spira A, Neubauer M, Bullock S, Walberg J, Cheng SK, Coleman RL. Biomarker testing and tissue journey among patients with metastatic non-small cell lung cancer receiving first-line therapy in The US Oncology Network. Lung Cancer. 2022 Apr;166:197-204. doi: 10.1016/j.lungcan.2022.03.004. Epub 2022 Mar 10. |
| 27601595 | Background | Thompson JC, Yee SS, Troxel AB, Savitch SL, Fan R, Balli D, Lieberman DB, Morrissette JD, Evans TL, Bauml J, Aggarwal C, Kosteva JA, Alley E, Ciunci C, Cohen RB, Bagley S, Stonehouse-Lee S, Sherry VE, Gilbert E, Langer C, Vachani A, Carpenter EL. Detection of Therapeutically Targetable Driver and Resistance Mutations in Lung Cancer Patients by Next-Generation Sequencing of Cell-Free Circulating Tumor DNA. Clin Cancer Res. 2016 Dec 1;22(23):5772-5782. doi: 10.1158/1078-0432.CCR-16-1231. Epub 2016 Sep 6. |
| 30988079 | Background | Leighl NB, Page RD, Raymond VM, Daniel DB, Divers SG, Reckamp KL, Villalona-Calero MA, Dix D, Odegaard JI, Lanman RB, Papadimitrakopoulou VA. Clinical Utility of Comprehensive Cell-free DNA Analysis to Identify Genomic Biomarkers in Patients with Newly Diagnosed Metastatic Non-small Cell Lung Cancer. Clin Cancer Res. 2019 Aug 1;25(15):4691-4700. doi: 10.1158/1078-0432.CCR-19-0624. Epub 2019 Apr 15. |
| 30325992 | Background | Aggarwal C, Thompson JC, Black TA, Katz SI, Fan R, Yee SS, Chien AL, Evans TL, Bauml JM, Alley EW, Ciunci CA, Berman AT, Cohen RB, Lieberman DB, Majmundar KS, Savitch SL, Morrissette JJD, Hwang WT, Elenitoba-Johnson KSJ, Langer CJ, Carpenter EL. Clinical Implications of Plasma-Based Genotyping With the Delivery of Personalized Therapy in Metastatic Non-Small Cell Lung Cancer. JAMA Oncol. 2019 Feb 1;5(2):173-180. doi: 10.1001/jamaoncol.2018.4305. |
| 32478025 | Background | Shelton RC, Chambers DA, Glasgow RE. An Extension of RE-AIM to Enhance Sustainability: Addressing Dynamic Context and Promoting Health Equity Over Time. Front Public Health. 2020 May 12;8:134. doi: 10.3389/fpubh.2020.00134. eCollection 2020. |
| 31462482 | Background | Rendle KA, Abramson CM, Garrett SB, Halley MC, Dohan D. Beyond exploratory: a tailored framework for designing and assessing qualitative health research. BMJ Open. 2019 Aug 27;9(8):e030123. doi: 10.1136/bmjopen-2019-030123. |
| 24904704 | Background | Kane H, Lewis MA, Williams PA, Kahwati LC. Using qualitative comparative analysis to understand and quantify translation and implementation. Transl Behav Med. 2014 Jun;4(2):201-8. doi: 10.1007/s13142-014-0251-6. |
| 33308250 | Background | Whitaker RG, Sperber N, Baumgartner M, Thiem A, Cragun D, Damschroder L, Miech EJ, Slade A, Birken S. Coincidence analysis: a new method for causal inference in implementation science. Implement Sci. 2020 Dec 11;15(1):108. doi: 10.1186/s13012-020-01070-3. |