Evidence map›Paper›PMID 40317947›Full record

ArticleEpilepsia2025

Development and validation of International Classification of Diseases, 9th and 10th Revision, Clinical Modification-based algorithms to identify adult epilepsy in electronic health records.

Hernan Nicolas Lemus, Jonathan Goldstein, Hua-Hsin Tai, Jung-Yi Lin, Churl-Su Kwon, Parul Agarwal, Benjamin Kummer, Mandip Dhamoon, Kusum Mathews, Sharon Nirenberg and 2 more

Abstract readValidation StudyMulticenter Study
In one paragraph

Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hernan Nicolas LemusDepartment of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID https://orcid.org/0000-0003-2485-461X
Jonathan GoldsteinDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0001-5778-4078
Hua-Hsin TaiDepartment of Medicine, Mount Sinai Morningside, New York, New York, USA.
Jung-Yi LinDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Churl-Su KwonDepartment of Neurology, Sergievsky Center, Columbia University, New York, New York, USA.ORCID https://orcid.org/0000-0001-9904-2240
Parul AgarwalDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0002-7497-8162
Benjamin KummerDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0002-1413-8014
Mandip DhamoonDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Kusum MathewsDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0002-8810-2794
Sharon NirenbergScientific Computing, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Nathalie JettéDepartment of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0003-1351-5866
Leah J BlankDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0001-8719-6752

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
Research Training for the Care of Vulnerable Older Adults with Alzheimer’s Disease and Related Dementias and Other Chronic ConditionsT32AG066598 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Alex D Federman · 2020 to 2026
$2.3M
Seizure, cognitive change and dementia: Understanding the use and safety of anti-seizure medicationsK23AG080163 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Leah Blank · 2023 to 2026
$664k
NCATS NIH HHS UL1TR004419NIA NIH HHS K23 AG080163NIA NIH HHS T32 AG066598
6 · The paper itself

Abstract

objectiveElectronic 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.

methodsData 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.

resultsNinety-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.

Indexed as

AlgorithmsElectronic Health RecordsEpilepsyInternational Classification of DiseasesAdultAgedFemaleHumansMaleMiddle AgedSensitivity and SpecificityYoung Adultaccuracyadministrative dataelectronic medical recordsICD‐10‐CMICD‐9‐CMInternational Classification of Diseasesmedical recordsseizure

Identifiers

PMID40317947
PMCPMC13112042

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.