Aortoiliac occlusive disease - a novel classification system based on anatomical segments and disease severity for management planning.

Aims: The aim of this classification system is to provide vascular surgeons with a simple tool that categorises disease severity by anatomical segment in aortoiliac occlusive disease and thus guide decision making and management strategies. Disease of the common femoral arteries is included as the distal extent of disease with respect to access for both open and endovascular intervention is essential to management planning.

Methods: The classification system designates diseased segment letters and numbers to guide treatment planning. The degree of disease other than stenotic or occluded is not required. In a similar manner to the TNM classification, anatomy and disease severity - based on angiography, CTA, and MRA - are categorised using a simple, user-friendly method. Two clinical cases are presented to exemplify the clinical application of this classification system.

Results: A simple and useful classification system is presented and ease of use exemplified by two clinical cases.

Conclusions: Management strategies for peripheral artery disease in general, aortoiliac occlusive disease specifically, have evolved rapidly in recent years. Existing classification systems, such as TASC II, steer the clinician towards specific treatment approaches. However, the first step in the management decision-making process is the accurate identification of the arterial segments that require treatment. None of the existing classification systems specifically address anatomy as an entity in itself. This classification system provides an intuitive framework, based on letters and numbers, that provides specific information on arterial segments and disease severity in aortoiliac occlusive disease on which clinicians can base management decisions. It has been developed to bolster this aspect of the vascular surgery armamentarium; to be used as a decision making and management planning tool, in partnership with, not instead of, existing classification systems.