Development and validation of International Classification of Diseases, 9th and 10th Revision, Clinical Modification‐based algorithms to identify adult epilepsy in electronic health records
Bibliographic record
Abstract
OBJECTIVE: Electronic health records (EHRs) are increasingly used to conduct research and evaluate epilepsy quality of care. We examined the accuracy of International Classification of Diseases, 9th and 10th Revision, Clinical Modification (ICD-9-CM, ICD-10-CM)- and antiseizure medicine (ASM)-based algorithms for adult epilepsy. METHODS: Data from a diverse New York multicenter EHR were queried to identify encounters between January 1, 2012 and September 20, 2018 coded with an epilepsy/seizure ICD-CM code or an ASM. Eight hundred adults were randomly selected (350 with epilepsy-related codes, 150 with an ASM, and 300 with drug-resistant epilepsy codes). With chart review defined as the reference standard, sensitivity (Sn), specificity (Sp), negative predictive value (NPV), positive predictive value (PPV), and Youden index (YI) were calculated to evaluate various ICD-9-CM-, ICD-10-CM-, ± ASM-based algorithms' accuracy in predicting epilepsy. RESULTS: Ninety-four algorithms were tested. A total of 435 (54.4%) patients had definite epilepsy. Estimates ranged as follows: YI = .18-.68, Sn = .59-.95, Sp = .52-.97, PPV = .67-.92, and NPV = .51-.93. The best algorithms were as follows. Highest YI for ICD-9-CM was single encounter with 345 (except 345.2 or 345.3) or 345.2, 345.3, or 780.3 with an ASM (Sn = .95, Sp = .73, PPV = .81, NPV = .92, YI = .68). Highest Y1 for ICD-10-CM was one encounter with G40 in primary diagnostic position or ≥2 encounters with G40 in any diagnostic position (Sn = .82, Sp = .85, PPV = .87, NPV = .80, YI = .67). Highest sensitivity was any encounter with ICD-9-CM 345 or 780.39 or ICD-10-CM G40, G41, or R56.9 (Sn = .96, Sp = .57, PPV = .73, NPV = .93, YI = .53). Highest specificity was ≥1 hospitalization with ICD-9-CM 345.x (except 345.2 and 345.3) or ICD-10-CM G40.x (Sn = .21, Sp = .97, PPV = .89, NPV = .51, YI = .18). SIGNIFICANCE: We identified ICD-9/10-CM-based case definitions (with and without ASM) that were sensitive and specific for epilepsy. Ultimately, extensive algorithms are provided to help inform case definition selection according to future study aims.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".