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Prediction of neuropathologic lesions from clinical data.
Phongpreecha, Thanaphong; Cholerton, Brenna; Bukhari, Syed; Chang, Alan L; De Francesco, Davide; Thuraiappah, Melan; Godrich, Dana; Perna, Amalia; Becker, Martin G; Ravindra, Neal G; Espinosa, Camilo; Kim, Yeasul; Berson, Eloise; Mataraso, Samson; Sha, Sharon J; Fox, Edward J; Montine, Kathleen S; Baker, Laura D; Craft, Suzanne; White, Lon; Poston, Kathleen L; Beecham, Gary; Aghaeepour, Nima; Montine, Thomas J.
Afiliación
  • Phongpreecha T; Department of Pathology, Stanford University, Stanford, California, USA.
  • Cholerton B; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Bukhari S; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Chang AL; Department of Pathology, Stanford University, Stanford, California, USA.
  • De Francesco D; Department of Pathology, Stanford University, Stanford, California, USA.
  • Thuraiappah M; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Godrich D; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Perna A; Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Becker MG; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Ravindra NG; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Espinosa C; Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Kim Y; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Berson E; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Mataraso S; Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Sha SJ; Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA.
  • Fox EJ; Department of Pathology, Stanford University, Stanford, California, USA.
  • Montine KS; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Baker LD; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Craft S; Department of Pediatrics, Stanford University, Stanford, California, USA.
  • White L; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Poston KL; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
  • Beecham G; Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Aghaeepour N; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, California, USA.
  • Montine TJ; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
Alzheimers Dement ; 19(7): 3005-3018, 2023 07.
Article en En | MEDLINE | ID: mdl-36681388
INTRODUCTION: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. METHODS: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. RESULTS: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. DISCUSSION: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Alzheimers Dement Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Alzheimers Dement Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos