Your browser doesn't support javascript.
loading
Harnessing electronic medical records to advance research on multiple sclerosis.
Damotte, Vincent; Lizée, Antoine; Tremblay, Matthew; Agrawal, Alisha; Khankhanian, Pouya; Santaniello, Adam; Gomez, Refujia; Lincoln, Robin; Tang, Wendy; Chen, Tiffany; Lee, Nelson; Villoslada, Pablo; Hollenbach, Jill A; Bevan, Carolyn D; Graves, Jennifer; Bove, Riley; Goodin, Douglas S; Green, Ari J; Baranzini, Sergio E; Cree, Bruce Ac; Henry, Roland G; Hauser, Stephen L; Gelfand, Jeffrey M; Gourraud, Pierre-Antoine.
Afiliación
  • Damotte V; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Lizée A; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France.
  • Tremblay M; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Department of Neurology, John Dempsey Hospital, University of Connecticut Health Center, Farmington, CT, USA.
  • Agrawal A; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Khankhanian P; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
  • Santaniello A; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Gomez R; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Lincoln R; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Tang W; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Chen T; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Lee N; Information Technology, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Villoslada P; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/IDIBAPS-Hospital Clinic of Barcelona, Barcelona, Spain.
  • Hollenbach JA; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Bevan CD; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Graves J; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Bove R; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Goodin DS; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Green AJ; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Baranzini SE; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Cree BA; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Henry RG; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Hauser SL; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Gelfand JM; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
  • Gourraud PA; MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France.
Mult Scler ; 25(3): 408-418, 2019 03.
Article en En | MEDLINE | ID: mdl-29310490
BACKGROUND: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. OBJECTIVES: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. METHODS: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. RESULTS: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing-remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. CONCLUSION: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.
Asunto(s)
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Almacenamiento y Recuperación de la Información / Investigación Biomédica / Registros Electrónicos de Salud / Esclerosis Múltiple Tipo de estudio: Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Mult Scler Asunto de la revista: NEUROLOGIA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Almacenamiento y Recuperación de la Información / Investigación Biomédica / Registros Electrónicos de Salud / Esclerosis Múltiple Tipo de estudio: Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Mult Scler Asunto de la revista: NEUROLOGIA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos