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An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.
Parkes, Michael D; Aliabadi, Arezu Z; Cadeiras, Martin; Crespo-Leiro, Maria G; Deng, Mario; Depasquale, Eugene C; Goekler, Johannes; Kim, Daniel H; Kobashigawa, Jon; Loupy, Alexandre; Macdonald, Peter; Potena, Luciano; Zuckermann, Andreas; Halloran, Philip F.
Afiliación
  • Parkes MD; Alberta Transplant Applied Genomics Centre, University of Alberta, Edmonton, Alberta, Canada.
  • Aliabadi AZ; Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.
  • Cadeiras M; Ronald Reagan UCLA Medical Center, Los Angeles, California, USA.
  • Crespo-Leiro MG; Complexo Hospitalario Universitario A Coruña (CHUAC)-CIBERCV, A Coruña, Spain.
  • Deng M; Ronald Reagan UCLA Medical Center, Los Angeles, California, USA.
  • Depasquale EC; Ronald Reagan UCLA Medical Center, Los Angeles, California, USA.
  • Goekler J; Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.
  • Kim DH; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
  • Kobashigawa J; Cedars-Sinai Medical Center, Beverly Hills, California, USA.
  • Loupy A; Hôpital Necker, Paris, France.
  • Macdonald P; The Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia.
  • Potena L; Cardiovascular Department, University of Bologna, Bologna, Italy.
  • Zuckermann A; Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.
  • Halloran PF; Alberta Transplant Applied Genomics Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. Electronic address: phallora@ualberta.ca.
J Heart Lung Transplant ; 38(6): 636-646, 2019 06.
Article en En | MEDLINE | ID: mdl-30795962
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we examined the stability of machine-learning algorithms in new biopsies, compared 3AA vs 4AA algorithms, assessed supervised binary classifiers trained on histologic or molecular diagnoses, created a report combining many scores into an ensemble of estimates, and examined possible automated sign-outs. METHODS: We studied 889 EMBs from 454 transplant recipients at 8 centers: the initial cohort (N = 331) and a new cohort (N = 558). Published 3AA algorithms derived in Cohort 331 were tested in Cohort 558, the 3AA and 4AA models were compared, and supervised binary classifiers were created. RESULTS: A`lgorithms derived in Cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (areas under the curve [AUCs] >0.87) better than histologic rejection (AUCs <0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by 1 expert showed highly significant agreement with histology (p < 0.001), but with many discrepancies, as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated signouts. CONCLUSIONS: Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Trasplante de Corazón / Aprendizaje Automático / Rechazo de Injerto / Insuficiencia Cardíaca / Miocardio Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged Idioma: En Revista: J Heart Lung Transplant Asunto de la revista: CARDIOLOGIA / TRANSPLANTE Año: 2019 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Trasplante de Corazón / Aprendizaje Automático / Rechazo de Injerto / Insuficiencia Cardíaca / Miocardio Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged Idioma: En Revista: J Heart Lung Transplant Asunto de la revista: CARDIOLOGIA / TRANSPLANTE Año: 2019 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos