Evidence map›Paper›PMID 40360294›Full record

ArticleBMJ health & care informatics2025

Assessing the validity of ICD-10 administrative data in coding comorbidities.

Jie Pan, Seungwon Lee, Cheligeer Cheligeer, Bing Li, Guosong Wu, Catherine A Eastwood, Yuan Xu, Hude Quan

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  5. Review
  6. Beyond Rurality: Individual Socioeconomic Status and Chronic Disease Prevalence.medRxiv : the preprint server for health sciences · 2026
    Article
  7. Article
  8. Article
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  10. Article
  11. Article
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  14. Data matter: the creation of an acute care surgery registry.Canadian journal of surgery. Journal canadien de chirurgie
    Article
  15. 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

8 authors.

Jie PanDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada jie.pan@ucalgary.ca.ORCID http://orcid.org/0000-0001-6398-1756
Seungwon LeeCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.ORCID http://orcid.org/0000-0002-6532-5303
Cheligeer CheligeerCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Bing LiCentre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
Guosong WuDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Catherine A EastwoodDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Yuan XuDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Hude QuanDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAdministrative data are commonly used to inform chronic disease prevalence and support health informatic research. This study assessed the validity of coding comorbidities in the International Classification of Diseases, 10th Revision (ICD-10) administrative data.

methodsWe analysed three chart review cohorts (4008 patients in 2003, 3045 in 2015 and 9024 in 2022) in Alberta, Canada. Nurse reviewers assessed the presence of 17 clinical conditions using a consistent protocol. The reviews were linked with administrative data using unique patient identifiers. We compared the accuracy in coding comorbidity by ICD-10, using chart review data as the reference standard.

resultsOur findings showed that the mean difference in prevalence between chart reviews and ICD-10 for these 17 conditions was 2.1% in 2003, 7.6% in 2015 and 6.3% in 2022. Some conditions were relatively stable, such as diabetes (1.9%, 2.1% and 1.1%) and metastatic cancer (0.3%, 1.1% and 0.4%). For these 17 conditions, the sensitivity ranged from 39.6-85.1% in 2003, 1.3%-85.2% in 2015 and 3.0-89.7% in 2022. The C-statistics for predicting in-hospital mortality using comorbidities by ICD-10 were 0.84 in 2003, 0.81 in 2015 and 0.78 in 2022. DISCUSSION: The undercoding could be primarily due to the increase in hospital patient volumes and the limited time allocated to coding specialists. There is the potential to develop artificial intelligence methods based on electronic health records to support coding practices and improve data quality.

conclusionComorbidities were increasingly undercoded over 20 years. The validity of ICD-10 decreased but remained relatively stable for certain conditions mandated for coding. The undercoding exerted minimal impact on in-hospital mortality prediction.

Indexed as

Clinical CodingComorbidityInternational Classification of DiseasesAgedAlbertaFemaleHospital MortalityHumansMaleMiddle AgedReproducibility of ResultsHealth Services ResearchPublic Health

Identifiers

PMID40360294
PMCPMC12083369

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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 Socratic graph.