Evidence map›Paper›PMID 38365631›Full record

ArticleBMC health services research2024

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease.

Seungwon Lee, Abdel Aziz Shaheen, David J T Campbell, Christopher Naugler, Jason Jiang, Robin L Walker, Hude Quan, Joon Lee

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 41% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Potential for error when relying on administrative data.The British journal of surgery · 2025
    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 at 1 institution in 1 country.

Seungwon LeeDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada. seungwon.lee@ucalgary.ca.
Abdel Aziz ShaheenDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
David J T CampbellDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
Christopher NauglerDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
Jason JiangCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Robin L WalkerDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
Hude QuanDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
Joon LeeDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
University of Calgary · CA

Funding

CIHR Foundation Grant FDN-167272
6 · The paper itself

Abstract

backgroundNon-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.

methodsThe Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.

resultsA total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.

conclusionDiabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Indexed as

Diabetes Mellitus, Type 2Non-alcoholic Fatty Liver DiseaseAlbertaComorbidityHumansPatient DischargeDiabetes mellitusHealth services researchInternational classification of diseasesNon-alcoholic fatty liver diseaseRoutinely collected health data

Identifiers

PMID38365631
PMCPMC10874028
OpenAlexW4391876467

What OpenQuestion holds

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LicenceCC BY
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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.