Investigating uncommon vascular diseases using the Vascular Low Frequency Disease Consortium

Standardized databases such as the Vascular Quality Initiative and National Surgical Quality Improvement Project assess the quality of care related to common vascular surgery procedures, but are not designed for uncommon vascular diseases. We describe a method of assessing uncommon vascular diseases using a multi-institutional collaboration, the Vascular Low Frequency Disease Consortium (VLFDC).

Uncommon vascular diseases are identified through a systematic literature review. A disease-specific database is developed and tested at a single institution, then refined and circulated to participating VLFDC investigators. Detailed inclusion and exclusion criteria and data point definitions are provided, allowing for standardized data collection across institutions. Each participating institution identifies all patients over a specific time period and enters the data into a VLFDC-provided database. The data are then de-identified and transmitted to our centralized data center for analysis.

Since 2003, the VLFDC has conducted and published nine studies and enrolled 4532 patients, involving 232 institutions and 271 investigators. The studies include renal artery aneurysms, isolated femoral artery aneurysms, spontaneous mesenteric dissection, adventitial cystic disease, carotid body tumors, and vascular Ehlers-Danlos syndrome. Each published study reported on a minimum of 10 times the number of patients collected in previously published studies over the same time period, allowing stronger conclusions to be drawn from the larger sample size. Each study both confirmed previous management principles, which were based on small single-institution experiences, and challenged conventional management paradigms.

When only small clinical series exist to provide guidance in managing uncommon vascular diseases, and/or where conflicting recommendations are made on the treatment of uncommon vascular diseases, a multi-institutional consortium can provide high-volume standardized data that either confirm or changes prior management principles.