Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Transplant. Sep 18, 2026; 16(3): 121821
Published online Sep 18, 2026. doi: 10.5500/wjt.121821
Published online Sep 18, 2026. doi: 10.5500/wjt.121821
Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering
Juanita Castellanos De Brigard, Ervandy Rangganata, Department of Surgery and Cancer, Imperial College London, London W12 0HS, United Kingdom
Vassilios E Papalois, Imperial College Renal and Transplant Centre, Imperial College Heal
Author contributions: Castellanos De Brigard J designed and conducted the study and drafted the manuscript; Rangganata E, reviewed and edited the manuscript; and Papalois VE supervised the study and reviewed the final version of the manuscript.
AI contribution statement: AI tools, specifically ChatGPT (OpenAI) and Claude (Anthropic), were used during manuscript preparation and data analysis. These tools were used to assist with Python programming tasks, including code development, visualisation formatting, and implementation of statistical and machine learning workflows, as well as for language editing and grammar correction. Given the computational and machine learning nature of this study, AI assistance was limited to supporting the technical implementation of analyses and manuscript preparation. AI tools were not used to generate research data, determine the study design, select the analytical approach, interpret the results, formulate the conclusions, or prepare the references. All AI-generated outputs were critically reviewed, verified, and revised by the authors. The authors take full responsibility for the accuracy, originality, integrity, and content of the manuscript.
Institutional review board statement: Data access was granted after de-identification and the necessary ethical approvals by the NHSBT through the National Research Pathway. The studies were conducted in accordance with the local legislation and institutional requirements.
Informed consent statement: Data acquisition was performed by NHSBT, which maintains the United Kingdom Transplant Registry on behalf of United Kingdom transplant centres under the United Kingdom General Data Protection Regulation, allowing NHSBT to use anonymised patient information for service evaluation, without additional patient consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement- checklist of items.
Data sharing statement: Technical appendix and code available from the corresponding author at j.castellanos-de-brigard24@imperial.ac.uk. Data access application should be submitted directly to NHSBT.
Corresponding author: Juanita Castellanos De Brigard, MD, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London W12 0HS, United Kingdom.
Received: April 17, 2026
Revised: June 21, 2026
Accepted: June 29, 2026
Published online: September 18, 2026
Processing time: 150 Days and 0.3 Hours
Revised: June 21, 2026
Accepted: June 29, 2026
Published online: September 18, 2026
Processing time: 150 Days and 0.3 Hours
Core Tip
Core Tip: Using unsupervised clustering, we identified latent clinical phenotypes that demonstrated a non-random distribution of machine perfusion (MP) across the population. MP cases were notably concentrated within specific clusters, while the overall cluster structure remained mostly stable when focusing solely on MP recipients. This indicates that MP is integrated within the wider phenotypic landscape rather than creating distinct subgroups. Importantly, these phenotypes continued to hold outcome patterns within the MP cohort, although some varied among certain clusters. These findings suggest that MP interacts with the underlying profiles of recipients and donors, potentially altering outcome trajectories depending on the specific phenotype.