Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing

Samin Panahi; Jamie Mayo; Eamonn Kennedy; Lee Christensen; Sreekanth Kamineni; Hari Krishna Raju Sagiraju; Tyler Cooper; David F Tate; Randall Rupper; Mary Jo Pugh

doi:10.3389/fneur.2024.1270688

Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing

Front Neurol. 2024 Feb 15:15:1270688. doi: 10.3389/fneur.2024.1270688. eCollection 2024.

Authors

Samin Panahi^#^{1

2}, Jamie Mayo^#^{1

2}, Eamonn Kennedy^{1

2}, Lee Christensen^{1

2}, Sreekanth Kamineni^{1

2}, Hari Krishna Raju Sagiraju³, Tyler Cooper^{1

2}, David F Tate^{1

2}, Randall Rupper⁴, Mary Jo Pugh^{1

2}

Affiliations

¹ VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States.
² Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States.
³ Preventive Oncology, All India Institute of Medical Sciences, New Delhi, India.
⁴ VA Salt Lake City Health Care System, Geriatric Research, Education and Clinical Center, Salt Lake City, UT, United States.

^# Contributed equally.

Abstract

Introduction: Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care.

Methods: A medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes.

Results: Veterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual.

Discussion: This study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.

Keywords: frontotemporal dementia; military health; natural language processing; phenotyping; traumatic brain injury; veterans.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by U.S. Army Medical Research and Development Command under award number W81XWH-20-1-0141. MP was also supported by VA Health Services Research and Development Service Research Career Scientist Award, 1 1IK6HX003762 RCS 17-297.