Precision Medicine Enables More TNM-Like Staging in Patients With Chronic Limb Threatening Ischemia.

Introduction: In cancer, there are survival-based staging systems and tailored, stage-based treatments. There is little personalized treatment in vascular disease. The 2019 Global Vascular Guidelines on the Management of CLTI proposed successful treatment hinges upon Patient risk, Limb severity, and ANatomic complexity (PLAN). We sought to confirm a three axis approach and define how increasing severity affects mortality, not just limb loss. Methods: Patients revascularized for incident CLTI at our institution from 2013 to 2017 were included. Outcomes were mortality, limb loss, the composite endpoint of amputation-free survival. Using Bayesian machine learning, specifically supervised topic modeling, clusters of patient features associated with mortality were formed after controlling for 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. Patient outcomes were used to order the clusters into stages with increasing mortality. Results: We defined three distinct clusters as the basis for patient- and limb-centered stages. Across stages, rates of 1-year mortality were 7.6, 13.8, 18.9% and rates of amputation-free survival were 84.8, 79.3, and 63.2%. Stage one had patients with rest pain and previous revascularization who were less likely to have wounds, diabetes, and renal disease. Stage two had doubled mortality, likely related to diabetes prevalence. Stage three is characterized by high rates of complicated comorbidities, particularly end stage renal disease, and significantly higher rate of limb loss (22.6 vs. 8% in stages one and two). Conclusion: Using precision medicine, we have demonstrated clustering of CLTI patients that can be used toward a robust staging system. We provide empiric evidence for PLAN and detail about how changes in each variable affect survival and amputation-free survival.

Duke Scholars

Author Nikki Freeman Biostatistics & Bioinformatics, Division of Biostatistics