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. 2025 Apr 21;25(1):407.
doi: 10.1186/s12888-025-06780-w.

Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data

Kira Griffiths  1 , Yida Won  2 , Zachery Lee  2 , Lu Wang  2 , Christoph U Correll  3   4   5   6   7 , Rashmi Patel  8
Affiliations

Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data

Kira Griffiths et al. BMC Psychiatry. .

Abstract

Background: Tardive dyskinesia (TD) is a severe and persistent involuntary movement disorder associated with long-term antipsychotic treatment. TD is likely underreported and misdiagnosed in routine practice, and there is a need to understand the proportion of patients who may experience TD but receive no formal diagnosis. This information could support the characterisation of patient populations that may benefit from novel therapeutic interventions. This study aimed to identify and describe patients with diagnosed or undiagnosed TD. Demographic and clinical features associated with an ICD-9/10 diagnosis of TD were explored.

Methods: A retrospective study was conducted using de-identified electronic health record (EHR) data captured between 1999 and 2021 in the US. A cohort of 32,558 adults with schizophrenia-spectrum disorders, major depressive disorder with psychosis or bipolar disorder with psychosis who were prescribed antipsychotics was selected. Abnormal movements associated with TD and presence of TD documented in semi-structured EHR data were extracted through manual review of text recorded as part of the mental state examination. Patients with a recorded diagnosis of TD were identified based on the presence ICD-9/10 codes within structured portions of medical records: ICD-9: 333.85; ICD-10: G24.01. Logistic regression was used to assess the association between patient characteristics and an ICD diagnosis.

Results: Altogether, 1,301 (4.0%) patients had either description of abnormal movements associated with TD (n=691) or documented TD (n=610) within semi-structured EHR data. Of those patients, only 64 (4.9%) had an ICD-TD diagnosis in structured EHR data. When the cohort was limited to those with documented TD in semi-structured EHR data, 56 (9.2%) had an ICD-TD diagnosis. Black/African-American race was associated with lower odds of ICD diagnosis compared with white race (OR=0.46, 95%CI=0.20-0.95, p=0.04). Treatment in community mental health centres was associated with increased odds of an ICD diagnosis compared to an academic medical centre (OR=adjusted OR=2.02, 95%CI=1.09-3.74, p=0.03).

Conclusions: This study highlights a pressing need for clinicians to better recognise and diagnose TD, which in turn may contribute to increased access to treatments for patients. A recorded ICD diagnosis of TD may be driven by factors related to both the patient and clinical setting.

Keywords: Electronic health record; ICD; Real-world data; Tardive dyskinesia; Underdiagnosis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was conducted in accordance with the 1964 Declaration of Helsinki and its subsequent amendments. The version of NeuroBlu used contained de-identified data from the MindLinc EHR. For the MindLinc EHR, institutional review board (IRB) approval for this study was not required because MindLinc data are anonymised and thus exempt from Health Insurance Portability and Accountability Act requirements. The NeuroBlu Database platform has received a waiver of informed consent prior to study conduct from the WCG Institutional Review Board (Ref: The Holmusk Real-World Evidence Parent Protocol; IRB registration number 1–1470336-1; Protocol ID HolmuskRWE_1.0). Consent for publication: Not applicable. Competing interests: At the time of this study, KG, and YW report employment with and equity ownership in KKT Technologies Pte. Ltd. or its subsidiaries. RP, MR, ZL and LW are previous employees of Holmusk Technologies Inc. RP has received grant funding from the National Institute for Health and Care Research (NIHR301690), the Medical Research Council (MR/S003118/1). RP has been a Scientific Advisory Board member for Boehringer Ingelheim, has received grant funding from Janssen, and has received consulting fees from Holmusk, Akrivia Health, Columbia Data Analytics, Clinilabs, Social Finance, Boehringer Ingelheim, Bristol Myers Squibb, Teva and Otsuka. CC has been a consultant and/or advisor to or has received honoraria from: AbbVie, Alkermes, Allergan, Angelini, Aristo, Boehringer-Ingelheim, Bristol-Meyers Squibb, Cardio Diagnostics, Cerevel, CNX Therapeutics, Compass Pathways, Darnitsa, Delpor, Denovo, Eli Lilly, Gedeon Richter, Hikma, Holmusk, IntraCellular Therapies, Jamjoom Pharma, Janssen/J&J, Karuna, LB Pharma, Lundbeck, MedInCell, MedLink, Merck, Mindpax, Mitsubishi Tanabe Pharma, Maplight, Mylan, Neumora Therapeutics, Neurocrine, Neurelis, Newron, Noven, Novo Nordisk, Otsuka, PPD Biotech, Recordati, Relmada, Reviva, Rovi, Saladax, Sanofi, Seqirus, Servier, Sumitomo Pharma America, Sunovion, Sun Pharma, Supernus, Tabuk, Takeda, Teva, Terran, Tolmar, Vertex, Viatris and Xenon Pharmaceuticals. He provided expert testimony for Janssen, Lundbeck and Otsuka. He served on a Data Safety Monitoring Board for Compass Pathways, IntraCellular Therapies, Relmada, Reviva, Rovi. He has received grant support from Boehringer-Ingelheim, Janssen and Takeda. He received royalties from UpToDate and is also a stock option holder of Cardio Diagnostics, Kuleon Biosciences, LB Pharma, Medlink, Mindpax, Quantic, Terran.

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