Using precision medicine methods to identify disease stages for chronic limb-threatening ischemia in participants of the BEST-CLI trial.

BACKGROUND: Chronic limb-threatening ischemia (CLTI), the most severe form of peripheral artery disease, is associated with a high risk of limb loss. CLTI clinical presentation is highly heterogeneous, ranging from neuropathic ulcers with only mild to moderate ischemia to gangrene resulting from severe ischemia. Understanding the etiology and limb- and systemic-based disease patterns, as well as differential procedural risks and outcomes, is pivotal for making treatment choices over the course of this chronic disease. In other words, accurate staging (and restaging over time) of CLTI that includes limb-based and systemic features is needed to improve the treatment decision-making process and clinical outcomes. Precision medicine analytics can integrate and synthesize multimodal data, in this case anatomy data alongside comorbidities and physical examination findings, offering a more complete staging system from which to make nuanced treatment decisions well-tailored to patient-specific risks. METHODS: Using data from the BEST-CLI (Best Endovascular vs Best Surgical Therapy in Patients with Critical Limb Ischemia) international randomized controlled trial, we used supervised latent topic modeling to identify clusters of patient features associated with amputation-free survival after stratifying for each assigned revascularization type. Patients were assigned to the cluster they belonged to with highest probability; clusters were characterized by analyzing the characteristics of patients within them. Although the clusters were not naturally ordinal, we subsequently organized them to mirror stages of disease progression for clearer clinical interpretation. RESULTS: Based on patient- and limb-focused characteristics, we identified three distinct clusters as disease stages. Across the three stages, rates of 2-year mortality were 11.59%, 20.91%, and 24.73% and rates of 2-year amputation-free survival were 83.26%, 70.03%, and 65.82%, respectively, for patients undergoing open bypass. Patients receiving endovascular therapy had 2-year mortality rates of 15.88%, 22.62%, and 20.32% and 2-year amputation-free survival rates of 77.98%, 66.06%, and 67.74%, respectively, for stages 1, 2, and 3. Stage 1 generally included patients who were less likely to have wounds, diabetes, and renal disease. Stage 2 was primarily driven by diabetes and some foot infection. Stage 3 is characterized by high rates of comorbidities, particularly end-stage renal disease and diabetes, as well as higher Wound, Ischemia, and foot Infection grades. CONCLUSIONS: We identified three distinct stages of CLTI using precision medicine methods. The results from this analysis of the BEST-CLI randomized clinical trial dataset are consistent with previous findings in other cohorts. Future research focused on tailored treatment algorithms for each specific stage of CLTI is warranted.

Duke Scholars

Author Nikki Freeman Biostatistics & Bioinformatics, Division of Biostatistics