An automated histological classification system for precision diagnostics of kidney allografts

Authors

Daniel Yoo, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Valentin Goutaudier, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Gillian Divard, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Juliette Gueguen, Néphrologie-Immunologie Clinique, Hôpital Bretonneau, CHU Tours, Tours, France.
Brad C. Astor, Division of Nephrology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Olivier Aubert, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Marc Raynaud, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Zeynep Demir, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Julien Hogan, Université Paris Cité, INSERM U970, Paris Institute for Transplantation and Organ Regeneration, Paris, France.
Patricia Weng, Pediatric Nephrology, David Geffen School of Medicine at UCLA, UCLA Mattel Children's Hospital, Los Angeles, CA, USA.
Jodi Smith, Department of Pediatrics, University of Washington School of Medicine, Seattle Children's Hospital, Seattle, WA, USA.
Arjang Djamali, Maine Medical Center

Document Type

Article

Publication Date

5-2023

Journal Title

Nature medicine

MeSH Headings

Adult; Humans; Male; Female; Child; Prospective Studies; Kidney (pathology); Kidney Transplantation (adverse effects); Transplantation, Homologous; Allografts; Graft Rejection (diagnosis); Biopsy

Abstract

For three decades, the international Banff classification has been the gold standard for kidney allograft rejection diagnosis, but this system has become complex over time with the integration of multimodal data and rules, leading to misclassifications that can have deleterious therapeutic consequences for patients. To improve diagnosis, we developed a decision-support system, based on an algorithm covering all classification rules and diagnostic scenarios, that automatically assigns kidney allograft diagnoses. We then tested its ability to reclassify rejection diagnoses for adult and pediatric kidney transplant recipients in three international multicentric cohorts and two large prospective clinical trials, including 4,409 biopsies from 3,054 patients (62.05% male and 37.95% female) followed in 20 transplant referral centers in Europe and North America. In the adult kidney transplant population, the Banff Automation System reclassified 83 out of 279 (29.75%) antibody-mediated rejection cases and 57 out of 105 (54.29%) T cell-mediated rejection cases, whereas 237 out of 3,239 (7.32%) biopsies diagnosed as non-rejection by pathologists were reclassified as rejection. In the pediatric population, the reclassification rates were 8 out of 26 (30.77%) for antibody-mediated rejection and 12 out of 39 (30.77%) for T cell-mediated rejection. Finally, we found that reclassification of the initial diagnoses by the Banff Automation System was associated with an improved risk stratification of long-term allograft outcomes. This study demonstrates the potential of an automated histological classification to improve transplant patient care by correcting diagnostic errors and standardizing allograft rejection diagnoses.ClinicalTrials.gov registration: NCT05306795 .

First Page

1211

Last Page

1220

Recommended Citation

Yoo D, Goutaudier V, Divard G, et al. An automated histological classification system for precision diagnostics of kidney allografts. Nat Med. 2023;29(5):1211-1220. doi:10.1038/s41591-023-02323-6