Evidence map›Paper›PMID 34022851›Full record

ArticleBMC medical informatics and decision making2021

Evaluation of the accuracy of diagnostic coding for influenza compared to laboratory results: the availability of test results before hospital discharge facilitates improved coding accuracy.

Nasir Wabe, Ling Li, Robert Lindeman, Jeffrey J Post, Maria R Dahm, Julie Li, Johanna I Westbrook, Andrew Georgiou

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Journal of preventive medicine and hygiene · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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.

Nasir WabeCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, 2109, Australia. nasir.wabe@mq.edu.au.ORCID 0000-0002-9740-6319
Ling LiCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, 2109, Australia.
Robert LindemanNew South Wales Health Pathology, St Leonards, NSW, 2065, Australia.
Jeffrey J PostDepartment of Infectious Diseases, Prince of Wales Hospital, Randwick, NSW, 2031, Australia.
Maria R DahmInstitute for Communication in Health Care, The Australian National University, 110 Ellery Crescent, Acton, ACT, 2601, Australia.
Julie LiCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, 2109, Australia.
Johanna I WestbrookCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, 2109, Australia.
Andrew GeorgiouCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, 2109, Australia.

Funding

National Health and Medical Research Council APP1111925
6 · The paper itself

Abstract

backgroundAssessing the accuracy of diagnostic coding is essential to ensure the validity and reliability of administrative coded data. The aim of the study was to evaluate the accuracy of assigned International Classification of Diseases version 10-Australian Modification (ICD-10-AM) codes for influenza by comparing with patients' results of their polymerase chain reaction (PCR)-based laboratory tests.

methodA retrospective study was conducted across seven public hospitals in New South Wales, Australia. A total of 16,439 patients who were admitted and tested by either cartridge-based rapid PCR or batched multiplex PCR between January 2016 and December 2017 met the inclusion criteria. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10-AM coding using laboratory results as a gold standard. Separate analyses were conducted to determine whether the availability of test results at the time of hospital discharge influenced diagnostic coding accuracy.

resultsLaboratory results revealed 2759 positive influenza cases, while ICD-10-AM coding identified 2527 patients. Overall, 13.7% (n = 378) of test positive patients were not assigned an ICD-10-AM code for influenza. A further 5.8% (n = 146) patients with negative test results were incorrectly assigned an ICD-10-AM code for influenza. The sensitivity, specificity, PPV and NPV of ICD-10-AM coding were 93.1%; 98.9%; 94.5% and 98.6% respectively when test results were received before discharge and 32.7%; 99.2%; 87.8% and 89.8% respectively when test results were not available at discharge. The sensitivity of ICD-10-AM coding varied significantly across hospitals. The use of rapid PCR or hospitalisation during the influenza season were associated with greater coding accuracy.

conclusionAlthough ICD-10-AM coding for influenza demonstrated high accuracy when laboratory results were received before discharge, its sensitivity was substantially lower for patients whose test results were not available at discharge. The timely availability of laboratory test results during the episode of care could contribute to improved coding accuracy.

Indexed as

Influenza, HumanPatient DischargeAustraliaClinical CodingHospitalsHumansInternational Classification of DiseasesLaboratoriesNew South WalesReproducibility of ResultsRetrospective StudiesDiagnostic accuracyDiagnostic codesICD-10-AMInfluenza diagnosis

Identifiers

PMID34022851
PMCPMC8141245

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.