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Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.
Swaminathan, Akshay; Ren, Alexander L; Wu, Janet Y; Bhargava-Shah, Aarohi; Lopez, Ivan; Srivastava, Ujwal; Alexopoulos, Vassilis; Pizzitola, Rebecca; Bui, Brandon; Alkhani, Layth; Lee, Susan; Mohit, Nathan; Seo, Noel; Macedo, Nicholas; Cheng, Winson; Wang, William; Tran, Edward; Thomas, Reena; Gevaert, Olivier.
Afiliación
  • Swaminathan A; Stanford University School of Medicine, Stanford, CA.
  • Ren AL; Stanford University School of Medicine, Stanford, CA.
  • Wu JY; Stanford University School of Medicine, Stanford, CA.
  • Bhargava-Shah A; Stanford University School of Medicine, Stanford, CA.
  • Lopez I; Stanford University School of Medicine, Stanford, CA.
  • Srivastava U; Department of Computer Science, Stanford University, Stanford, CA.
  • Alexopoulos V; Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Pizzitola R; Department of Symbolic Systems, Stanford University, Stanford, CA.
  • Bui B; Department of Human Biology, Stanford University, Stanford, CA.
  • Alkhani L; Department of Materials Science and Engineering, Stanford University, Stanford, CA.
  • Lee S; Department of Computer Science, Stanford University, Stanford, CA.
  • Mohit N; Department of Psychology, Stanford University, Stanford, CA.
  • Seo N; Department of Computer Science, Stanford University, Stanford, CA.
  • Macedo N; Department of Sociology, Stanford University, Stanford, CA.
  • Cheng W; Department of Biology, Stanford University, Stanford, CA.
  • Wang W; Department of Radiology, Stanford University School of Medicine, Stanford, CA.
  • Tran E; Department of Computer Science, Stanford University, Stanford, CA.
  • Thomas R; Department of Chemistry, Stanford University, Stanford, CA.
  • Gevaert O; Department of Biology, Stanford University, Stanford, CA.
JCO Clin Cancer Inform ; 8: e2300091, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38857465
ABSTRACT

PURPOSE:

Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM).

METHODS:

Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method.

RESULTS:

Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days.

CONCLUSION:

We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Glioblastoma / Registros Electrónicos de Salud Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: JCO Clin Cancer Inform Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Glioblastoma / Registros Electrónicos de Salud Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: JCO Clin Cancer Inform Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos