Evidence map›Paper›PMID 38141226›Full record

ArticleNeuro-oncology2024

Developing a computable phenotype for glioblastoma.

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 and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Sandra YanDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.
Kaitlyn MelnickDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.
Xing HeDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.ORCID 0000-0003-0290-8058
Tianchen LyuDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Rachel S F MoorDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.
Megan E H StillDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.
Duane A MitchellDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.
Elizabeth A ShenkmanDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Han WangDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Yi GuoDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.ORCID 0000-0003-0587-4105
Jiang BianDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.ORCID 0000-0002-2238-5429
Ashley P GhiaseddinDepartment of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.ORCID 0000-0001-5599-4610

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
NCATS NIH HHS UL1 TR001427University of Florida Clinical and Translational Science Institute
6 · The paper itself

Abstract

backgroundGlioblastoma 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).

methodsWe 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.

resultsWe 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.

conclusionsWe 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.

Indexed as

Brain NeoplasmsElectronic Health RecordsGlioblastomaPhenotypeAlgorithmsFemaleHumanscomputable phenotypeElectronic Health Records (EHRs)glioblastomastructured dataunstructured data

Identifiers

PMID38141226
PMCPMC11145437

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.