Evidence map›Paper›PMID 32534422›Full record

ArticleEpilepsy & behavior : E&B2020

Using ICD-10 codes to identify elective epilepsy monitoring unit admissions from administrative billing data: A validation study.

Brad K Kamitaki, Shelly Rishty, Ram Mani, Stephen Wong, Lisa M Bateman, Charlotte Thomas-Hawkins, Joel C Cantor, Lawrence C Kleinman

Erratum issuedOpen access · bronzeAbstract readValidation Study
In one paragraph

Article in Epilepsy & behavior : E&B, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Brad K KamitakiDepartment of Neurology, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street Suite 6200, New Brunswick, NJ 08901, United States. Electronic address: brad.kamitaki@rutgers.edu.
Shelly RishtyDepartment of Neurology, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street Suite 6200, New Brunswick, NJ 08901, United States.
Ram ManiDepartment of Neurology, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street Suite 6200, New Brunswick, NJ 08901, United States.
Stephen WongDepartment of Neurology, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street Suite 6200, New Brunswick, NJ 08901, United States.
Lisa M BatemanDepartment of Neurology, Columbia University Medical Center, 710 West 168th Street 7th Floor, New York, NY 10032, United States.
Charlotte Thomas-HawkinsDivision of Nursing Science, School of Nursing, Rutgers University, 180 University Ave ACK 330, Newark, NJ 07102, United States.
Joel C CantorCenter for State Health Policy, Rutgers University, 112 Paterson Street 5th Floor, New Brunswick, NJ 08901, United States.
Lawrence C KleinmanDepartment of Pediatrics, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street 7th Floor, New Brunswick, NJ 08901, United States.
Rutgers, The State University of New Jersey · USColumbia University Irving Medical Center · USJohnson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Video-electroencephalogram (EEG) monitoring in the epilepsy monitoring unit (EMU) is essential for managing epilepsy and seizure mimics. Evaluation of care in the EMU would benefit from a validated code set capable of identifying EMU admissions from administrative databases comprised of large, diverse cohorts. We assessed the ability of code-based queries to parse EMU admissions from administrative billing records in a large academic medical center over a four-year period, 2016-2019. We applied prespecified queries for admissions coded as follows: 1) elective, 2) receiving video-EEG monitoring, and 3) including diagnoses typically required by major US healthcare payers for EMU admission. Sensitivity (Sn), specificity (Sp), and predictive value positive/negative (PVP, PVN) were determined. Two approaches were highly effective. Incorporating epilepsy, seizure, or seizure mimic codes as the admitting diagnosis (assigned at admission; Sn 96.3%, Sp 100.0%, PVP 98.3%, and PVN 100.0%) or the principal diagnosis (assigned after discharge; Sn 94.9%, Sp 100.0%, PVP 98.8%, and PVN 100.0%) identified elective adult EMU admissions with comparable reliability (p = 0.096). The addition of surgical procedure codes further separated EMU admissions for intracranial EEG monitoring. When applied to larger, more comprehensive datasets, these code-based queries should enhance our understanding of EMU utilization and access to care on a scalable basis.

Indexed as

AdultCohort StudiesDatabases, FactualElectroencephalographyEpilepsyFemaleHospital AdministrationHumansInternational Classification of DiseasesMaleMiddle AgedMonitoring, PhysiologicPatient AdmissionReproducibility of ResultsRetrospective StudiesYoung AdultCode setsEMUEpidemiologyHealth services researchInternational classification of diseasesSecondary analysis

Identifiers

PMID32534422
PMCPMC7286261
OpenAlexW3034721411

What OpenQuestion holds

Textmetadata
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.