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Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.
Vaulet, Thibaut; Divard, Gillian; Thaunat, Olivier; Lerut, Evelyne; Senev, Aleksandar; Aubert, Olivier; Van Loon, Elisabet; Callemeyn, Jasper; Emonds, Marie-Paule; Van Craenenbroeck, Amaryllis; De Vusser, Katrien; Sprangers, Ben; Rabeyrin, Maud; Dubois, Valérie; Kuypers, Dirk; De Vos, Maarten; Loupy, Alexandre; De Moor, Bart; Naesens, Maarten.
Afiliación
  • Vaulet T; Department of Electrical Engineering, Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
  • Divard G; Université de Paris, National Institutes of Health and Medical Research, Paris Translational Research Centre for Organ Transplantation, Paris, France.
  • Thaunat O; Kidney Transplant Department, Necker Hospital, Assistance Publique Hôpitaux de Paris, Paris, France.
  • Lerut E; French National Institutes of Health and Medical Research, Lyon, France.
  • Senev A; Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Edouard Herriot Hospital, Lyon, France.
  • Aubert O; Department of Imaging and Pathology, University Hospitals Leuven, Leuven, Belgium.
  • Van Loon E; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • Callemeyn J; Histocompatibility and Immunogenetics Laboratory, Belgian Red Cross-Flanders, Mechelen, Belgium.
  • Emonds MP; Université de Paris, National Institutes of Health and Medical Research, Paris Translational Research Centre for Organ Transplantation, Paris, France.
  • Van Craenenbroeck A; Kidney Transplant Department, Necker Hospital, Assistance Publique Hôpitaux de Paris, Paris, France.
  • De Vusser K; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • Sprangers B; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • Rabeyrin M; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • Dubois V; Histocompatibility and Immunogenetics Laboratory, Belgian Red Cross-Flanders, Mechelen, Belgium.
  • Kuypers D; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • De Vos M; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium.
  • Loupy A; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
  • De Moor B; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium.
  • Naesens M; Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
J Am Soc Nephrol ; 32(5): 1084-1096, 2021 05 03.
Article en En | MEDLINE | ID: mdl-33687976
BACKGROUND: Over the past decades, an international group of experts iteratively developed a consensus classification of kidney transplant rejection phenotypes, known as the Banff classification. Data-driven clustering of kidney transplant histologic data could simplify the complex and discretionary rules of the Banff classification, while improving the association with graft failure. METHODS: The data consisted of a training set of 3510 kidney-transplant biopsies from an observational cohort of 936 recipients. Independent validation of the results was performed on an external set of 3835 biopsies from 1989 patients. On the basis of acute histologic lesion scores and the presence of donor-specific HLA antibodies, stable clustering was achieved on the basis of a consensus of 400 different clustering partitions. Additional information on kidney-transplant failure was introduced with a weighted Euclidean distance. RESULTS: Based on the proportion of ambiguous clustering, six clinically meaningful cluster phenotypes were identified. There was significant overlap with the existing Banff classification (adjusted rand index, 0.48). However, the data-driven approach eliminated intermediate and mixed phenotypes and created acute rejection clusters that are each significantly associated with graft failure. Finally, a novel visualization tool presents disease phenotypes and severity in a continuous manner, as a complement to the discrete clusters. CONCLUSIONS: A semisupervised clustering approach for the identification of clinically meaningful novel phenotypes of kidney transplant rejection has been developed and validated. The approach has the potential to offer a more quantitative evaluation of rejection subtypes and severity, especially in situations in which the current histologic categorization is ambiguous.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trasplante de Riñón / Rechazo de Injerto / Enfermedades Renales Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Am Soc Nephrol Asunto de la revista: NEFROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trasplante de Riñón / Rechazo de Injerto / Enfermedades Renales Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Am Soc Nephrol Asunto de la revista: NEFROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos