Jason R Smith1, Felipe J S Jones2, Brandy E Fureman3, Jeffrey R Buchhalter4, Susan T Herman5, Neishay Ayub6, Christopher McGraw7, Sydney S Cash8, Daniel B Hoch9, Lidia M V R Moura10. 1. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. Electronic address: jason.smith@mgh.harvard.edu. 2. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. Electronic address: felipe.jones@mgh.harvard.edu. 3. Research and New Therapies, Epilepsy Foundation, 8301 Professional Place West, Suite 230, Landover, MD, 20785, USA. Electronic address: bfureman@efa.org. 4. Department of Pediatrics, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. Electronic address: buchhalterj@gmail.com. 5. Department of Neurology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA. Electronic address: susan.herman@dignityhealth.org. 6. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. Electronic address: nayub@mgh.harvard.edu. 7. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. Electronic address: cmmcgraw@mgh.harvard.edu. 8. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. Electronic address: scash@mgh.harvard.edu. 9. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. Electronic address: dhoch@mgh.harvard.edu. 10. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. Electronic address: lidia.moura@mgh.harvard.edu.
Abstract
OBJECTIVE: To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. METHODS: We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019-09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). RESULTS: There were 673 eligible outpatient encounters. After review of EHRs for standardized documentation, an analytic sample consisted of 520 encounters representing 402 unique patients. In the EHR documentation, 93.5 % (n = 486/520) of encounters were with patients with a diagnosis of epilepsy. Of those, 66.0 % (n = 321/486) had ≥1 focal seizure, 41.6 % (n = 202/486) had ≥1 generalized seizure, and 7% (n = 34/486) had ≥1 unknown seizure. An ICD-10-CM definition for epilepsy (i.e., ICD-10 G40.X) achieved Sn = 84.4 % (95 % CI 80.8-87.5%), Sp = 79.4 % (95 % CI 62.1-91.3%), PPV = 98.3 % (95 % CI 96.6-99.3%), and NPV = 26.2 % (95 % CI 18.0-35.8%). The classification of focal vs generalized/unknown seizures achieved Sn = 69.8 % (95 % CI 64.4-74.8%), Sp = 79.4 % (95 % CI 72.4-85.3%), PPV = 86.8 % (95 % CI 82.1-90.7%), and NPV = 57.5 % (95 % CI 50.8-64.0%). CONCLUSIONS: Claims-based definitions using groups of ICD-10-CM codes assigned by neurologists in routine outpatient clinic visits at a level 4 epilepsy center performed well in discriminating between patients with and without a diagnosis of epilepsy and between seizure types.
OBJECTIVE: To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. METHODS: We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019-09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). RESULTS: There were 673 eligible outpatient encounters. After review of EHRs for standardized documentation, an analytic sample consisted of 520 encounters representing 402 unique patients. In the EHR documentation, 93.5 % (n = 486/520) of encounters were with patients with a diagnosis of epilepsy. Of those, 66.0 % (n = 321/486) had ≥1 focal seizure, 41.6 % (n = 202/486) had ≥1 generalized seizure, and 7% (n = 34/486) had ≥1 unknown seizure. An ICD-10-CM definition for epilepsy (i.e., ICD-10 G40.X) achieved Sn = 84.4 % (95 % CI 80.8-87.5%), Sp = 79.4 % (95 % CI 62.1-91.3%), PPV = 98.3 % (95 % CI 96.6-99.3%), and NPV = 26.2 % (95 % CI 18.0-35.8%). The classification of focal vs generalized/unknown seizures achieved Sn = 69.8 % (95 % CI 64.4-74.8%), Sp = 79.4 % (95 % CI 72.4-85.3%), PPV = 86.8 % (95 % CI 82.1-90.7%), and NPV = 57.5 % (95 % CI 50.8-64.0%). CONCLUSIONS: Claims-based definitions using groups of ICD-10-CM codes assigned by neurologists in routine outpatient clinic visits at a level 4 epilepsy center performed well in discriminating between patients with and without a diagnosis of epilepsy and between seizure types.
Keywords:
Administrative claims; Clinical coding [D059019]; Healthcare [D000067575]; International classification of diseases [D038801]; Population surveillance [D011159]; Validation study [D023361]
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