Evidence map›Paper›PMID 34091800›Full record

ArticleDigestive diseases and sciences2022

International Classification of Diseases Codes are Useful in Identifying Cirrhosis in Administrative Databases.

Monica Dahiya, Ejemai Eboreime, Ashley Hyde, Sholeh Rahman, Meghan Sebastianski, Michelle Carbonneau, Elliot B Tapper, Puneeta Tandon

Abstract read
In one paragraph

Article in Digestive diseases and sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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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.

Monica DahiyaDivision of Gastroenterology (Liver Unit), University of Alberta, 130-University Campus, Edmonton, AB, T6G2X8, Canada.
Ejemai EboreimeDivision of Gastroenterology (Liver Unit), University of Alberta, 130-University Campus, Edmonton, AB, T6G2X8, Canada.
Ashley HydeDivision of Gastroenterology (Liver Unit), University of Alberta, 130-University Campus, Edmonton, AB, T6G2X8, Canada.
Sholeh RahmanDepartment of Pediatrics, Alberta Research Centre for Health Evidence, University of Alberta, Edmonton, Canada.
Meghan SebastianskiAlberta Strategy for Patient-Oriented Research SUPPORT Unit (AbSPORU) Knowledge Translation Platform, University of Alberta, Edmonton, Canada.
Michelle CarbonneauDivision of Gastroenterology (Liver Unit), University of Alberta, 130-University Campus, Edmonton, AB, T6G2X8, Canada.
Elliot B Tapper *Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, USA.
Puneeta Tandon *Division of Gastroenterology (Liver Unit), University of Alberta, 130-University Campus, Edmonton, AB, T6G2X8, Canada. ptandon@ualberta.ca.ORCID 0000-0002-9698-5814

Funding

Optimizing Measures for Clinical Trials in Covert Hepatic EncephalopathyK23DK117055 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TAPPER, ELLIOT · 2019 to 2022
$776k
NIDDK NIH HHS K23 DK117055
6 · The paper itself

Abstract

backgroundHealth administrative databases are essential to define patient populations, make socioeconomic predictions, and facilitate medical research and healthcare planning. The accuracy of this data is dependent on valid codes/coding algorithms.

aimsThe aim of this study was to systematically identify and summarize the validity of International Classification of Diseases (ICD) codes for identifying patients with cirrhosis in administrative data.

methodsElectronic databases, MEDLINE (via Ovid), EMBASE (via Ovid), the Web of Science, and CINAHL (via EBSCOhost), were searched for validation studies which compared ICD codes related to cirrhosis to a clinical reference standard, and reported statistical measures of performance.

resultsFourteen studies were included in the review. There was a large variation in the algorithms used to validate ICD codes to diagnose cirrhosis. Despite the variation, the positive predictive value (PPV) was greater than 84% and the specificity was greater than 75% in the majority of the studies. The negative predictive value (NPV) was lower, but still was associated with values greater than 70% in the majority of studies. Sensitivity data varied significantly with values ranging from 0.27 to 99%.

conclusionsEvaluated ICD codes for cirrhosis, including codes for chronic liver disease, cirrhosis-specific codes, and cirrhosis-related complications, have demonstrated variable sensitivity and reasonable specificity for the identification of cirrhosis. Additional research is needed to maximize the identification of persons with cirrhosis to avoid underestimating the burden of disease.

Indexed as

International Classification of DiseasesLiver CirrhosisAlgorithmsDatabases, FactualData CollectionHumansPredictive Value of TestsAdministrativeCirrhosisICDIdentificationValidation

Identifiers

PMID34091800
PMCPMC10291482

What OpenQuestion holds

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Registered trials

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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.