Accuracy of claims‐based algorithms for epilepsy research: Revealing the unseen performance of claims‐based studies

Moura, Lidia M.V.R.; Price, Maggie; Cole, Andrew J.; Hoch, Daniel B.; Hsu, John

doi:10.1111/epi.13691

Cited by 37 publications

(58 citation statements)

References 33 publications

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“…This definition of epilepsy, which includes both diagnostic code and antiepileptic medication, is based on validated criteria for administrative claims data (positive predictive value 84%). 14,15 Patients were excluded if they were not enrolled in insurance coverage for 5 continuous years, with at least 2 years of prediagnosis data and at least 3 years of follow-up data. In addition, to have a sufficient follow-up period to assess neurologist influence on longer-term outcomes, patients were excluded if their visit to a neurologist occurred >1 year after their incident diagnosis (figure 1).…”

Section: Data Source and Study Populationmentioning

confidence: 99%

Claims data analyses unable to properly characterize the value of neurologists in epilepsy care

Hill¹,

Lin²,

Burke³

et al. 2019

Neurology

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Objective To determine the association of a neurologist visit with health care use and cost outcomes for patients with incident epilepsy. Methods Using health care claims data for individuals insured by United Healthcare from 2001 to 2016, we identified patients with incident epilepsy. The population was defined by an epilepsy/ convulsion diagnosis code (ICD codes 345.xx/780.3x, G40.xx/R56.xx), an antiepileptic prescription filled within the succeeding 2 years, and neither criterion met in the 2 preceding years. Cases were defined as patients who had a neurologist encounter for epilepsy within 1 year after an incident diagnosis; a control cohort was constructed with propensity score matching. Primary outcomes were emergency room (ER) visits and hospitalizations for epilepsy. Secondary outcomes included measures of cost (epilepsy related, not epilepsy related, and antiepileptic drugs) and care escalation (including EEG evaluation and epilepsy surgery). Results After participant identification and propensity score matching, there were 3,400 cases and 3,400 controls. Epilepsy-related ER visits were more likely for cases than controls (year 1: 5.9% vs 2.3%, p < 0.001), as were hospitalizations (year 1: 2.1% vs 0.7%, p < 0.001). Total medical costs for epilepsy care, nonepilepsy care, and antiepileptic drugs were greater for cases (p ≤ 0.001). EEG evaluation and epilepsy surgery occurred more commonly for cases (p ≤ 0.001). Conclusions Patients with epilepsy who visited a neurologist had greater subsequent health care use, medical costs, and care escalation than controls. This comparison using administrative claims is plausibly confounded by case disease severity, as suggested by higher nonepilepsy care costs. Linking patient-centered outcomes to claims data may provide the clinical resolution to assess care value within a heterogeneous population.

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Section: Data Source and Study Populationmentioning

confidence: 99%

Claims data analyses unable to properly characterize the value of neurologists in epilepsy care

Hill¹,

Lin²,

Burke³

et al. 2019

Neurology

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“…Compliance of patients to take a prescribed drug is principally unknown. Identification of patients with a complex disease such as epilepsy via ICD codes is by itself not trivial (Moura et al, 2017). We tried to address these challenges by a number of strategies:…”

Section: Resultsmentioning

confidence: 99%

Predicting comorbidities of epilepsy patients using big data from Electronic Health Records combined with biomedical knowledge

Gerlach

Lu²,

Fröhlich

2017

Preprint

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Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities. While general comorbidity prevalence and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting individual comorbidities. To address these needs we used Big Data from electronic health records (˜100 Million raw observations), which provide a time resolved view on an individual's disease and medication history. A specific contribution of this work is an integration of these data with information from 14 biomedical sources (DisGeNET, TTD, KEGG, Wiki Pathways, DrugBank, SIDER, Gene Ontology, Human Protein Atlas, ...) to capture putative biological effects of observed diseases and applied medications. In consequence we extracted >165,000 features describing the longitudinal patient journey of >10,000 adult epilepsy patients. We used maximum-relevance-minimum-redundancy feature selection in combination with Random Survival Forests (RSF) for predicting the risk of 9 major comorbidities after first epilepsy diagnosis with high cross-validated C-indices of 76 -89% and analyzed the influence of medications on the risk to develop specific comorbidities. Altogether we see our work as a first step towards earlier detection and better prevention of common comorbidities of epilepsy patients.

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“…We used International Classification of Disease, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes at time of hospital discharge to identify index status epilepticus (in any diagnosis position) and epilepsy admissions (in the primary diagnosis position; Table ). These codes have been previously validated with a sensitivity of 100% and specificity of 100% for status epilepticus and a positive predictive value of 80%‐97.7% for epilepsy . Status epilepticus codes were validated against the widely accepted criteria of “≥5 min of (a) continuous seizure or (b) two or more discrete seizures between which there is incomplete recovery of consciousness.” We excluded the ICD‐9‐CM code 345.2 (petit mal status) from the status epilepticus group, because it lacked sufficient specificity.…”

Section: Methodsmentioning

confidence: 99%

“…These codes have been previously validated with a sensitivity of 100% and specificity of 100% for status epilepticus and a positive predictive value of 80%-97.7% for epilepsy. [7][8][9][10][11][12] Status epilepticus codes were validated against the widely accepted criteria of "≥5 min of (a) continuous seizure or (b) two or more discrete seizures between which there is incomplete recovery of consciousness." 10,13 We excluded the ICD-9-CM code 345.2 (petit mal status) from the status epilepticus group, because it lacked sufficient specificity.…”

Section: Methodsmentioning

confidence: 99%

Exacerbation of hepatic cirrhosis may trigger admission for epilepsy and status epilepticus

2020

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Objective To determine whether acute exacerbations of cirrhotic liver disease are associated with higher odds of readmission for epilepsy or status epilepticus. Methods The New York State Inpatient Database is a statewide dataset containing data on 97% of hospitalizations for New York State. In this retrospective, case‐crossover design study, we used International Classification of Diseases, Ninth Revision, Clinical Modification codes to identify index status epilepticus and epilepsy admissions. The primary exposure was defined as admission due to an acute exacerbation of cirrhotic liver disease. The case‐crossover analysis tested whether exposure to a hepatic exacerbation within progressively longer case periods (14, 30, 60, 90, 120, 150, and 180 days before index admission), compared to control periods 1 year before the case period, was associated with readmission for epilepsy or status epilepticus. Results The odds ratio for subsequent admission for epilepsy after exposure to an acute exacerbation of cirrhotic liver disease was significant in the 30‐day window at 2.072 (95% confidence interval [CI] = 1.095‐3.92, P = .0252) and peaked in the 150‐day window at 2.742 (95% CI = 1.817‐4.137, P < .0001). In the status epilepticus group, all case periods demonstrated significantly elevated odds of subsequent admission following hepatic exacerbation. Significance Hepatic exacerbations are associated with increased odds for hospital admissions for epilepsy and status epilepticus across several timeframes.

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Accuracy of claims‐based algorithms for epilepsy research: Revealing the unseen performance of claims‐based studies

Cited by 37 publications

References 33 publications

Claims data analyses unable to properly characterize the value of neurologists in epilepsy care

Claims data analyses unable to properly characterize the value of neurologists in epilepsy care

Predicting comorbidities of epilepsy patients using big data from Electronic Health Records combined with biomedical knowledge

Exacerbation of hepatic cirrhosis may trigger admission for epilepsy and status epilepticus

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