Analysis and Visualization of Confounders and Treatment Pathways Leading to Amputation and Non-Amputation in Peripheral Artery Disease Patients Using Sankey Diagrams: Enhancing Explainability

Background/Objectives: This study uses Sankey diagrams to analyze treatment pathways in patients with peripheral artery disease (PAD), which is a vascular condition characterized by atherosclerotic occlusion of the arteries, particularly in the lower limbs, affecting up to 14% of the general population. This study focuses on the treatment pathways that lead to amputation versus those that do not, utilizing the STARR dataset and the All of Us dataset. Methods: The study utilized Sankey diagrams to visualize treatment pathways, highlighting the progression from initial treatments to outcomes. Odds ratio analysis was performed to quantify the association between treatment pathways and outcomes. Recognizing potential confounders, analyses were conducted by filtering patients with PAD into subgroups based on these coexisting conditions. Sankey diagrams were then generated for each sub-cohort to visualize treatment pathways. Results: Pathways including antiplatelet and lipid-lowering treatments accounted for 56% of non-amputation cases in the STARR data and 50% in the All of Us data. Amputation pathways frequently included revascularization procedures, representing 15% of amputations in the STARR data and 20% in the All of Us data. Confounder analysis revealed that most amputated PAD patients were over 50 years old and had one or more conditions, such as diabetes, hypertension, or hyperlipidemia. Conclusions: These visualizations provide insights into treatment pathways and their associations with outcomes in PAD patients, highlighting the potential impact of specific treatments on amputation and non-amputation cases. Future work should build on these findings by incorporating predictive models using machine learning techniques to further explore and quantify these relationships.