Patients experiencing end-stage kidney disease need continuous dialysis treatments multiple times a week. The treatment requires large amounts of energy and water, relies on public infrastructure, and is vulnerable to extreme weather events. For example, Hurricane Helene in 2024 disabled production centers vital to dialysis supply. Hurricane Katrina in 2005 caused extended closures of 94 dialysis centers, affecting over 6,000 patients. Despite increasing extreme weather threats, there is a need to make dialysis treatment more resilient so that patients retain access to their almost daily treatments, including during extreme events. To this end, this study will use quantitative sustainable design, including life cycle assessment (LCA), to identify the most resilient dialysis modality (in-center hemodialysis, home hemodialysis, or peritoneal dialysis). We are quantifying life cycle energy and water use to reduce the reliance on these increasingly expensive and scarce resources and calculating carbon footprints (i.e., equivalent carbon dioxide emissions due to greenhouse gasses based on ecoinvent databases and TRACI impact method) to minimize the positive feedback loop between carbon emissions and the intensity and frequency of extreme events. These three metrics will be quantified by generating life cycle inventories of all the amounts and types of materials, energy, chemicals, and water required over a dialysis machine’s life cycle (e.g., production, disposal) and patient’s treatment sessions (e.g., transportation, medication). A range of representative patients will each have a digital twin to evaluate patient health as a fourth metric. With each digital twin, we will evaluate all four metrics, for each modality, over a range of extreme events (e.g., flooding, power loss, supply chain failure) to identify the most resilient modality based on patient type and extreme event. This data can be used to build resilience in the dialysis population, especially by informing dialysis technology development, deployment, and selection.