Comparing Long-term Mortality After Carotid Endarterectomy vs Carotid Stenting Using a Novel Instrumental Variable Method for Risk Adjustment in Observational Time-to-Event Data

Journal JAMA Network Open
Authors Jesse A. Columbo; Pablo Martinez-Camblor; Todd A. MacKenzie; Douglas O. Staiger; Ravinder Kang; Philip P. Goodney; A. James O’Malley
Year Published 2018
Link to publication

Abstract

IMPORTANCE:

Choosing between competing treatment options is difficult for patients and clinicians when results from randomized and observational studies are discordant. Observational real-world studies yield more generalizable evidence for decision making than randomized clinical trials, but unmeasured confounding, especially in time-to-event analyses, can limit validity.

OBJECTIVES:

To compare long-term survival after carotid endarterectomy (CEA) and carotid artery stenting (CAS) in real-world practice using a novel instrumental variable method designed for time-to-event outcomes, and to compare the results with traditional risk-adjustment models used in observational research for survival analyses.

DESIGN, SETTING, AND PARTICIPANTS:

A multicenter cohort study was performed. The Vascular Quality Initiative, an observational quality improvement registry, was used to compare long-term mortality after CEA vs CAS. The study included 86 017 patients who underwent CEA (n = 73 312) or CAS (n = 12 705) between January 1, 2003, and December 31, 2016. Patients were followed up for long-term mortality assessment by linking the registry data to Medicare claims. Medicare claims data were available through September 31, 2015.

EXPOSURE:

Procedure type (CEA vs CAS).

MAIN OUTCOMES AND MEASURES:

The hazard ratios (HRs) of all-cause mortality using unadjusted, adjusted, propensity-matched, and instrumental variable methods were examined. The instrumental variable was the proportion of CEA among the total carotid procedures (endarterectomy and stenting) performed at each hospital in the 12 months before each patient’s index operation and therefore varies over the study period.

RESULTS:

Participants who underwent CEA had a mean (SD) age of 70.3 (9.4) years compared with 69.1 (10.4) years for CAS, and most were men (44 191 [60.4%] for CEA and 8117 [63.9%] for CAS). The observed 5-year mortality was 12.8% (95% CI, 12.5%-13.2%) for CEA and 17.0% (95% CI, 16.0%-18.1%) for CAS. The unadjusted HR of mortality for CEA vs CAS was 0.67 (95% CI, 0.64-0.71), and Cox-adjusted and propensity-matched HRs were similar (0.69; 95% CI, 0.65-0.74 and 0.71; 95% CI, 0.65-0.77, respectively). These findings are comparable with published observational studies of CEA vs CAS. However, the association between CEA and mortality was more modest when estimated by instrumental variable analysis (HR, 0.83; 95% CI, 0.70-0.98), a finding similar to data reported in randomized clinical trials.

CONCLUSIONS AND RELEVANCE:

The study found a survival advantage associated with CEA over CAS in unadjusted and Cox-adjusted analyses. However, this finding was more modest when using an instrumental variable method designed for time-to-event outcomes for risk adjustment. The instrumental variable-based results were more similar to findings from randomized clinical trials, suggesting this method may provide less biased estimates of time-dependent outcomes in observational analyses.

EHR Patient Data Utilized in Mercy, Johnson & Johnson Collaboration

Mercy recently announced a data sharing partnership with Johnson and Johnson that received significant coverage in the health business press.  Our intent is to collaborate with J&J to continue the development of methods for using EHR and other health system data in the evaluation of a variety of medical devices.  We, of course, began this work in the MDEpiNet Mercy UDI Demonstration Project and are continuing it in the MDEpiNet BUILD initiative.  We announced a similar relationship with Medtronic last year and continue to work with them on one of the initial NEST demonstration pilots.  We hope to establish similar arrangements with other manufacturers in the future.  Our ability to form these significant strategic partnerships stems directly from our MDEpiNet work and the atmosphere of trust engendered by the public private partnership.

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Registry Assessment of Peripheral Interventional Devices (RAPID) ― Registry Assessment of Peripheral Interventional Devices Core Data Elements

Journal

J-STAGE

Authors W. Schuyler Jones, Mitchell W. Krucoff, Pablo Morales, Rebecca W. Wilgus, Anne H. Heath, Mary F. Williams, James E. Tcheng, J. Danica Marinac-Dabic, Misti L. Malone, Terrie L. Reed, Rie Fukaya, Robert Lookstein, Nobuhiro Handa, Herbert D. Aronow, Daniel J. Bertges, Michael R. Jaff, Thomas T. Tsai, Joshua A. Smale, Margo J. Zaugg, Robert J. Thatcher, Jack L. Cronenwett, Durham NC, Silver Spring Md, Tokyo Japan, New York NY, Providence RI, Burlington Vt, Newton Mass, Denver Colo, Tempe Ariz, Santa Clara Calif, Minneapolis Minn, Lebanon NH

Year Published 2018
Link to publication

Abstract

Background

The current state of evaluating patients with peripheral artery disease and more specifically of evaluating medical devices used for peripheral vascular intervention (PVI) remains challenging because of the heterogeneity of the disease process, the multiple physician specialties that perform PVI, the multitude of devices available to treat peripheral artery disease, and the lack of consensus about the best treatment approaches. Because PVI core data elements are not standardized across clinical care, clinical trials, and registries, aggregation of data across different data sources and physician specialties is currently not feasible.

Methods

Under the auspices of the U.S. Food and Drug Administration’s Medical Device Epidemiology Network initiative—and its PASSION (Predictable and Sustainable Implementation of the National Registries) program, in conjunction with other efforts to align clinical data standards—the Registry Assessment of Peripheral Interventional Devices (RAPID) workgroup was convened. RAPID is a collaborative, multidisciplinary effort to develop a consensus lexicon and to promote interoperability across clinical care, clinical trials, and national and international registries of PVI.

Results

The current manuscript presents the initial work from RAPID to standardize clinical data elements and definitions, to establish a framework within electronic health records and health information technology procedural reporting systems, and to implement an informatics-based approach to promote the conduct of pragmatic clinical trials and registry efforts in PVI.

Conclusions

Ultimately, we hope this work will facilitate and improve device evaluation and surveillance for patients, clinicians, health outcomes researchers, industry, policymakers, and regulators.

 

Invitation to join the AHRMM Learning UDI Community (LUC) Device Categorization Working Group

Please consider this invitation to join a new Association for Healthcare Resource & Materials Management (AHRMM) Learning UDI Community Working Group that will focus on Device Categorization—specifically, the analysis & evaluation of GMDN/SNOMED for PAD devices.

This LUC Working Group will provide an opportunity for you to finish what was raised as an issue during RAPID Phase I and take the work back to RAPID in Phase II/III. It is also an opportunity for you to set the foundation for device categorization work in other areas—hopefully by generalizing the findings to apply to other categories of medical devices.

The LUC Device Categorization WG Charter can be found at: http://www.ahrmm.org/resources/learning-udi-community/pdfs/work-groups/device-categorization-work-group-summary-statement-103116.pdf.

Please contact AHRMM (ahrmm@aha.org) with questions or if you are interested in joining this important Working Group or visit http://www.ahrmm.org/resources/learning-udi-community for information about other LUC Working Groups.