Abstract
Objective: Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of using regular expressions to identify risk factors in EMRs and (2) barriers to generalizability. Methods: Data included physician notes for 3000 patients from one medical center (home) and 1000 from five additional centers (away). Through chart review, we identified three SUDEP risk factors: (1) generalized tonic–clonic seizures, (2) refractory epilepsy, and (3) epilepsy surgery candidacy. Regular expressions of risk factors were manually created with home training data, and performance was evaluated with home test and away test data. Performance was evaluated by sensitivity, positive predictive value, and F-measure. Generalizability was defined as an absolute decrease in performance by <0.10 for away versus home test data. To evaluate underlying barriers to generalizability, we identified causes of errors seen more often in away data than home data. To demonstrate how small revisions can improve generalizability, we removed three “boilerplate” standard text phrases from away notes and repeated performance. Results: We observed high performance in home test data (F-measure range = 0.86-0.90), and low to high performance in away test data (F-measure range = 0.53-0.81). After removing three boilerplate phrases, away performance improved (F-measure range = 0.79-0.89) and generalizability was achieved for nearly all measures. The only significant barrier to generalizability was use of boilerplate phrases, causing 104 of 171 errors (61%) in away data. Significance: Regular expressions are a feasible and probably a generalizable method to identify variables related to SUDEP risk. Our methods may be implemented to create large patient cohorts for research and to generate electronic prompts for SUDEP counseling.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1209-1220 |
| Number of pages | 12 |
| Journal | Epilepsia |
| Volume | 60 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2019 |
Funding
Eisai, Upsher‐Smith Laboratories, Sunovion, Mallinckrodt, Empatica, Sage, and Pfizer, including past device donations from various companies, including SmartWatch, Empatica, and Neuro‐electrics. During the course of this work, Z.M.G. received research support from the Centers for Disease Control and Prevention (U01 DP006089), New York State, Weill Cornell Medicine, the Pediatric Epilepsy Research Foundation, the Epilepsy Research Fund, the Epilepsy Foundation, and the BAND Foundation. This work was supported through generous funding from the Centers for Disease Control and Prevention (U01DP006089) and the Epilepsy Research Fund (Epilepsy Research Risk Detection and Prevention of Death Fund). D.C.H. has received research support from the Centers for Disease Control and Prevention (U01 DP006089), National Institutes of Health (NIH), Patient‐Centered Outcomes Research Institute, and Epilepsy Study Consortium. T.L. has received research support from the NIH, the Epilepsy Research Fund, the Epilepsy Foundation of America, the Epilepsy Therapy Project, the Pediatric Epilepsy Research Foundation, Lundbeck,
Keywords
- electronic health records
- generalized tonic–clonic seizures
- natural language processing
- patient education
- sudden unexpected death in epilepsy
ASJC Scopus subject areas
- Neurology
- Clinical Neurology