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. 2024 Jun 3;26(6):1163-1170.
doi: 10.1093/neuonc/noad249.

Developing a computable phenotype for glioblastoma

Sandra Yan  1 , Kaitlyn Melnick  1 , Xing He  2   3 , Tianchen Lyu  2   3 , Rachel S F Moor  1 , Megan E H Still  1 , Duane A Mitchell  1 , Elizabeth A Shenkman  2   3 , Han Wang  2 , Yi Guo  2   3 , Jiang Bian  2   3 , Ashley P Ghiaseddin  1
Affiliations

Developing a computable phenotype for glioblastoma

Sandra Yan et al. Neuro Oncol. .

Abstract

Background: Glioblastoma is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are nonspecific. The aim of this study was to create a computable phenotype (CP) for glioblastoma multiforme (GBM) from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR).

Methods: We used the University of Florida (UF) Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords.

Results: We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule.

Conclusions: We developed and validated a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance, which minimizes possible biases from misclassification errors.

Keywords: Electronic Health Records (EHRs); computable phenotype; glioblastoma; structured data; unstructured data.

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Conflict of interest statement

D.A.M. holds patented technologies that have been licensed or have exclusive options to license to Celldex Therapeutics, Annias, Immunomic Therapeutics, and iOncologi. D.A.M. received research funding from Immunomic Therapeutics. D.A.M. serves/served as an advisor/consultant to Bristol-Myers Squibb, Tocagen, Oncorus, and RM Global. D.A.M. is co-founder of iOncologi, Inc., an immuno-oncology biotechnology company. A.P.G. serves/served as an advisor/consultant to Neosoma and Monteris Medical. A.P.G. has received honoraria for advisory board participation from Alexion Pharmaceuticals, Servier, and Aptitude Health. A.P.G. has held stock in Viatris Inc. All other authors have no conflicts to report.

Figures

Figure 1.
Figure 1.
Workflow for the development of the computable phenotype algorithm.
See this image and copyright information in PMC

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