Development and Description of a National Cohort of Patients With Chronic Limb-Threatening Ischemia.

ABSTRACT

Background: Chronic limb-threatening ischemia (CLTI) is a common condition with high rates of morbidity and mortality. Despite extensive literature documenting poor outcomes in patients with CLTI, as well as racial, ethnic, socioeconomic, and geographic disparities in these outcomes, process measures for high-quality CLTI care have not been developed. We developed the Chronic Limb threatening Ischemia Process PERformace (CLIPPER) cohort to develop and test the validity of CLTI care quality measures. Methods: Using inpatient and outpatient claims data from patients with fee-for-service Medicare from 2010 to 2019, we created a coding algorithm to identify patients with CLTI. To qualify for a CLTI diagnosis, patients had to have either diagnostic codes for peripheral artery disease and for ulceration, infection, or gangrene on the same inpatient or outpatient claim or a CLTI-specific diagnostic code. Patients were also required to have a procedural code indicating arterial vascular testing within 6 months before or after the earliest qualifying CLTI diagnostic code(s). We describe baseline characteristics and long-term outcomes of this cohort. Results: The final cohort comprised 1,130,065 patients diagnosed with CLTI between 2010 and 2019. Mean (±SD) age of the cohort was 75 ± 5.8 years; 48.4% were women, and 14.6% were Black. Within 30 days of CLTI diagnosis, 20.4% of patients underwent either percutaneous or surgical revascularization. Within 6 months, 3.3% of patients underwent major amputation; 16.7% of patients died within 1 year and 50.3% within 5 years. Conclusions: We described the development of a cohort of fee-for-service Medicare patients with CLTI using inpatient and outpatient Medicare claims data. CLIPPER will be a resource for developing a set of process measures that can be captured from administrative claims data, with plans to describe their association with limb outcomes and corresponding racial, ethnic, socioeconomic, sex-based, and geographic variability.