Previously submitted to: JMIR Formative Research (no longer under consideration since Jan 09, 2026)
Date Submitted: Jul 25, 2025
Open Peer Review Period: Jul 29, 2025 - Sep 23, 2025
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Confounder Analysis and Visualization Treatment Pathways Associated to Amputation in patients with Diabetes Using Sankey Diagrams: Enhancing Explainability
ABSTRACT
Abstract: Background/Objectives: This study uses Sankey diagrams to analyze treatment pathways in patients with diabetes, focusing on amputation versus non-amputation outcomes. The study utilizes the All of Us dataset to investigate treatment patterns and their associations with amputation.
Methods:
Sankey diagrams visualized treatment sequences, displaying progression from initial treatments to subsequent therapies and outcomes. Logistic regression identified factors associated with amputation outcomes, calculating odds ratios to quantify relationships. Recognizing potential confounders, analyses were conducted by filtering patients into subgroups based on significant factors. Sankey diagrams were generated for statistically significant sub-cohorts to visualize treatment patterns.
Results:
In the All of Us dataset, metformin-based regimens were most common among patients without amputation, with metformin alone or combined with sulfonylureas and repaglinides predominating. Among patients who underwent amputation, GLP1 receptor agonist sequences were more frequent and showed significantly higher associations with amputation versus metformin (OR=4.53, 95% CI: 3.91-5.25, p<0.001; GLP1-insulin combination OR=8.38). Analysis identified significant amputation-associated factors: heart failure (OR=1.45), smoking (OR=1.63), male gender (OR=1.88), and Black race (OR=4.16, with 20.4% amputation rate vs 8.7% overall). Age-stratified analysis revealed patients aged 40-50 with amputation had 39.58% GLP1 agonist use versus 10.28% without amputation.
Conclusions:
Our observational findings demonstrate associations between treatment sequences and amputation outcomes, though causality cannot be established. Future research might incorporate machine learning models to predict amputation risk based on treatment sequences and patient factors, while accounting for treatment timing and duration, potentially enabling more personalized treatment approaches for at-risk diabetic patients.
Citation
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