Evidence map›Paper›PMID 34629073›Full record

ArticleBMC medical research methodology2021

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.

Kristin M Lenoir, Lynne E Wagenknecht, Jasmin Divers, Ramon Casanova, Dana Dabelea, Sharon Saydah, Catherine Pihoker, Angela D Liese, Debra Standiford, Richard Hamman and 2 more

Abstract read
In one paragraph

Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Validation of an Algorithm to Identify End-Stage Kidney Disease in Electronic Health Records Data.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2025
    Article
  3. Article
  4. Review
  5. 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.

Kristin M LenoirDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA. klenoir@wakehealth.edu.
Lynne E WagenknechtDivision of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Jasmin DiversDivision of Health Services Research, NYU Winthrop Research Institute, NYU Long Island School of Medicine, Mineola, NY, USA.
Ramon CasanovaDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Dana DabeleaDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA.
Sharon SaydahDivision of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Catherine PihokerDepartment of Pediatrics, University of Washington, Seattle, WA, USA.
Angela D LieseDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Debra StandifordCincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Richard HammanDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA.
Brian J WellsDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
SEARCH for Diabetes in Youth Study Group

Funding

Limited Competition for the Closeout of the SEARCH for Diabetes in Youth Cohort StudyR01DK127208 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DAGOSTINO, RALPH B., JENSEN, ELIZABETH T · 2020 to 2020
$2.0M
SEARCH for Diabetes in Youth Registry Study, Phase 4: Carolina Center (Component A)U18DP006138 · DP · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MAYER-DAVIS, ELIZABETH J · 2015 to 2019
$1.9M
The SEARCH for Diabetes in Youth Registry StudyU18DP006139 · DP · UNIVERSITY OF COLORADO DENVER · PI DABELEA, DANA · 2015 to 2019
$1.9M
NCCDPHP CDC HHS U18 DP006131NCCDPHP CDC HHS U18 DP006138NCCDPHP CDC HHS U18 DP006139NIDDK NIH HHS R01 DK127208
6 · The paper itself

Abstract

backgroundDisease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes.

methodsA rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review.

resultsAmong 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time.

conclusionsYear of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Indexed as

Diabetes MellitusElectronic Health RecordsAdolescentAlgorithmsChildHumansInternational Classification of DiseasesAdolescentsAlgorithmsChildrenDiabetes mellitusElectronic health recordsEpidemiologyInfantsSurveillanceYoung adults

Identifiers

PMID34629073
PMCPMC8502379

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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.