Lidia M V R Moura1, Jason R Smith2, Deborah Blacker3, Christine Vogeli4, Lee H Schwamm5, John Hsu6. 1. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. Electronic address: lidia.moura@mgh.harvard.edu. 2. Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. Electronic address: jason.smith@mgh.harvard.edu. 3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. Electronic address: blacker@nmr.mgh.harvard.edu. 4. Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. Electronic address: cvogeli@mgh.harvard.edu. 5. 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: lschwamm@mgh.harvard.edu. 6. Mongan Institute, Department of Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA, 02114, USA; Department of Health Care Policy, Harvard Medical School, 677 Huntington Avenue, Boston, MA, 02115, USA. Electronic address: john.hsu@mgh.harvard.edu.
Abstract
OBJECTIVE: There have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE. METHODS: We linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity. RESULTS: Of 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]). CONCLUSION: Medicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.
OBJECTIVE: There have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE. METHODS: We linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity. RESULTS: Of 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]). CONCLUSION: Medicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.
Authors: Susan R Heckbert; Charles Kooperberg; Monika M Safford; Bruce M Psaty; Judith Hsia; Anne McTiernan; J Michael Gaziano; William H Frishman; J David Curb Journal: Am J Epidemiol Date: 2004-12-15 Impact factor: 4.897
Authors: E Wayne Holden; Hoang Thanh Nguyen; Elizabeth Grossman; Scott Robinson; Leila S Nelson; Margaret J Gunter; Ann Von Worley; David J Thurman Journal: Epilepsia Date: 2005-02 Impact factor: 5.864