Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data
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...
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2025-04-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-06780-w |
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| author | Kira Griffiths Yida Won Zachery Lee Lu Wang Christoph U. Correll Rashmi Patel |
| author_facet | Kira Griffiths Yida Won Zachery Lee Lu Wang Christoph U. Correll Rashmi Patel |
| author_sort | Kira Griffiths |
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| description | 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. |
| format | Article |
| id | doaj-art-43602362b70845f885eddba56ab24b77 |
| institution | Kabale University |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-43602362b70845f885eddba56ab24b772025-08-20T03:53:32ZengBMCBMC Psychiatry1471-244X2025-04-012511910.1186/s12888-025-06780-wIdentifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record dataKira Griffiths0Yida Won1Zachery Lee2Lu Wang3Christoph U. Correll4Rashmi Patel5Holmusk Technologies Inc., UKHolmusk Technologies Inc., Singapore, Blk 71, Ayer Rajah CrescentHolmusk Technologies Inc., Singapore, Blk 71, Ayer Rajah CrescentHolmusk Technologies Inc., Singapore, Blk 71, Ayer Rajah CrescentZucker Hillside Hospital, Department of Psychiatry, Northwell HealthDepartment of Psychiatry, University of CambridgeAbstract 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.https://doi.org/10.1186/s12888-025-06780-wTardive dyskinesiaReal-world dataUnderdiagnosisICDElectronic health record |
| spellingShingle | Kira Griffiths Yida Won Zachery Lee Lu Wang Christoph U. Correll Rashmi Patel Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data BMC Psychiatry Tardive dyskinesia Real-world data Underdiagnosis ICD Electronic health record |
| title | Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data |
| title_full | Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data |
| title_fullStr | Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data |
| title_full_unstemmed | Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data |
| title_short | Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data |
| title_sort | identifying the diagnostic gap of tardive dyskinesia an analysis of semi structured electronic health record data |
| topic | Tardive dyskinesia Real-world data Underdiagnosis ICD Electronic health record |
| url | https://doi.org/10.1186/s12888-025-06780-w |
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