Developing a computable phenotype for glioblastoma

• DOI: 10.1093/neuonc/noad249
• Publication Date: 2023-12-23T20:53:57Z
• Authors: Sandra Yan; Kaitlyn Melnick; Xing He; Tianchen Lyu; Rachel S F Moor; Megan E H Still; Duane A Mitchell; Elizabeth A Shenkman; Han Wang; Yi Guo; Jiang Bian; Ashley P Ghiaseddin
• Keywords: 03 medical and health sciences; Phenotype; 0302 clinical medicine; Brain Neoplasms; Humans; Electronic Health Records; Female; Glioblastoma; Algorithms

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.