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Record W4415015213 · doi:10.1053/j.ajkd.2025.08.010

Environmental Impacts of Kidney Replacement Therapies: A Comparative Lifecycle Assessment

2025· article· en· W4415015213 on OpenAlexafffund
Saba Saleem, Caroline Stigant, Tasleem Rajan, Kasun Hewage, Rehan Sadiq, Andrea J. MacNeill, Christopher Nguan

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of British Columbia HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMitacs
KeywordsRenal replacement therapyHealth careEnvironmental impact assessmentKidney diseaseResource (disambiguation)Risk assessmentMEDLINE

Abstract

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RATIONALE & OBJECTIVE: Health care delivery is associated with considerable emissions of greenhouse gases and other pollutants. Although the relative health and economic impacts of kidney replacement therapies (KRTs) have been examined, their comparative environmental impacts have been poorly described. This study sought to characterize these impacts, comparing them across types of KRT. STUDY DESIGN: A comparative lifecycle assessment (LCA). SETTING & PARTICIPANTS: Data collection implemented at Vancouver General Hospital in Vancouver, British Columbia, Canada. EXPOSURE: Three KRTs: deceased-donor kidney transplant (KT), automated/cycler peritoneal dialysis (PD), or in-center hemodialysis (HD). OUTCOME: Environmental impacts of KRTs over 1 year were evaluated using the World ReCiPe (H) 2016 method. ANALYTICAL APPROACH: Lifecycle inventory results were transformed into 3 end-point and 18 midpoint environmental impact categories including climate change, air pollution, human toxicity, and water depletion. RESULTS: Across the majority of environmental impact categories, including climate change, air pollution, human toxicity, and water depletion, HD had the highest environmental impact and KT the lowest. The climate impact from a patient receiving HD was 74% and 46% more than from patients receiving KT and PD, respectively. Similarly, HD accounted for 65% of total air pollution impacts, 54% of human toxicity, and 44% of water depletion. The highest impact of PD was on water depletion (41%) and metal depletion (81%). KT demonstrated the lowest impact across all categories except terrestrial ecotoxicity. Within each therapy, patient and staff travel and consumables were the largest contributors to greenhouse gas emissions. LIMITATIONS: Pharmaceuticals were excluded from this study because of a lack of publicly available data. CONCLUSIONS: KT is the most environmentally preferred KRT. PD had fewer environmental impacts than HD. Understanding the relative environmental impacts of KRTs can help inform clinical decision-making in the management of kidney failure. PLAIN-LANGUAGE SUMMARY: The environmental impacts of health care are gaining attention, yet kidney care, and especially kidney replacement therapies (KRTs), have been underexamined. This study was inspired by growing concerns about the environmental consequences of KRTs like hemodialysis, peritoneal dialysis, and transplantation. We used environmental assessment tools to measure emissions and resource use across different KRTs in a clinical setting in Vancouver, Canada. We found that these therapies vary widely in their environmental impacts, with in-center hemodialysis having the greatest negative impact and kidney transplant the least impact. This study also explored the sources of these impacts and can inform health systems and health care policymakers regarding opportunities for more environmentally informed practices in kidney care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.333
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractno

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