Factors Associated with Stroke Coding Quality: A Comparison of Registry and Administrative Data.


Journal

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
ISSN: 1532-8511
Titre abrégé: J Stroke Cerebrovasc Dis
Pays: United States
ID NLM: 9111633

Informations de publication

Date de publication:
Feb 2021
Historique:
received: 04 08 2020
revised: 14 10 2020
accepted: 08 11 2020
pubmed: 1 12 2020
medline: 26 1 2021
entrez: 30 11 2020
Statut: ppublish

Résumé

The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM) codes are commonly used to identify patients with diseases or clinical conditions for epidemiological research. We aimed to determine the diagnostic agreement and factors associated with a clinician-assigned stroke diagnosis in a national registry and the ICD-10-AM codes recorded in government-held administrative data. Data from 39 hospitals (2009-2013) participating in the Australian Stroke Clinical Registry (AuSCR) were linked and merged with person-level administrative data. The AuSCR clinician-assigned stroke diagnosis was the reference standard. Concordance was defined as agreement between the clinician-assigned diagnosis and the ICD-10-AM codes for acute stroke or transient ischemic attack (TIA) (ICD-10-AM codes: I61-I64, G45.9). Multivariable logistic regression was undertaken to assess factors associated with coded diagnostic concordance. A total of 14,716 patient admissions were included (46% female, 63% ischemic, 14% intracerebral hemorrhage [ICH], 18% TIA and 5% unspecified stroke based on the reference standard). Principal ICD-10-AM code concordance was ICH: 76.7%; ischemic stroke: 72.2%; TIA: 80.2%; unspecified stroke: 50.8%. Factors associated with a greater odds of ischemic stroke concordance included: treatment in a stroke unit (adjusted Odds Ratio, aOR:1.58; 95% confidence interval (CI) 1.37, 1.82); length of stay >4 days (aOR:1.30; 95% CI 1.17, 1.45); and discharge destination other than home (Residential care aOR:1.57; 95% CI 1.24, 1.96; Inpatient rehabilitation aOR:1.63; 95% CI 1.43, 1.86). Diagnostic concordance varied based on stroke type. Future research to improve the quality of coding for stroke should focus on patients not treated in stroke units or with shorter lengths of stay where documentation in medical records may be limited.

Sections du résumé

BACKGROUND BACKGROUND
The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM) codes are commonly used to identify patients with diseases or clinical conditions for epidemiological research. We aimed to determine the diagnostic agreement and factors associated with a clinician-assigned stroke diagnosis in a national registry and the ICD-10-AM codes recorded in government-held administrative data.
MATERIALS AND METHODS METHODS
Data from 39 hospitals (2009-2013) participating in the Australian Stroke Clinical Registry (AuSCR) were linked and merged with person-level administrative data. The AuSCR clinician-assigned stroke diagnosis was the reference standard. Concordance was defined as agreement between the clinician-assigned diagnosis and the ICD-10-AM codes for acute stroke or transient ischemic attack (TIA) (ICD-10-AM codes: I61-I64, G45.9). Multivariable logistic regression was undertaken to assess factors associated with coded diagnostic concordance.
RESULTS RESULTS
A total of 14,716 patient admissions were included (46% female, 63% ischemic, 14% intracerebral hemorrhage [ICH], 18% TIA and 5% unspecified stroke based on the reference standard). Principal ICD-10-AM code concordance was ICH: 76.7%; ischemic stroke: 72.2%; TIA: 80.2%; unspecified stroke: 50.8%. Factors associated with a greater odds of ischemic stroke concordance included: treatment in a stroke unit (adjusted Odds Ratio, aOR:1.58; 95% confidence interval (CI) 1.37, 1.82); length of stay >4 days (aOR:1.30; 95% CI 1.17, 1.45); and discharge destination other than home (Residential care aOR:1.57; 95% CI 1.24, 1.96; Inpatient rehabilitation aOR:1.63; 95% CI 1.43, 1.86).
CONCLUSIONS CONCLUSIONS
Diagnostic concordance varied based on stroke type. Future research to improve the quality of coding for stroke should focus on patients not treated in stroke units or with shorter lengths of stay where documentation in medical records may be limited.

Identifiants

pubmed: 33253990
pii: S1052-3057(20)30887-9
doi: 10.1016/j.jstrokecerebrovasdis.2020.105469
pii:
doi:

Types de publication

Comparative Study Journal Article Multicenter Study Observational Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

105469

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Olivia F Ryan (OF)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: olivia.ryan@florey.edu.au.

Merilyn Riley (M)

Department of Public Health, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Bundoora, VIC, Australia. Electronic address: merilyn.riley@latrobe.edu.au.

Dominique A Cadilhac (DA)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia; Translational Public Health & Evaluation Division, Stroke & Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. Electronic address: dominique.cadilhac@monash.edu.au.

Nadine E Andrew (NE)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia; Peninsula Clinical School, Central Clinical School, Monash University, VIC, Australia. Electronic address: nadine.andrew@monash.edu.

Sibilah Breen (S)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: sibilah.breen@florey.edu.au.

Kate Paice (K)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: kate.paice@florey.edu.au.

Sam Shehata (S)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: sam@247.com.au.

Vijaya Sundararajan (V)

Department of Public Health, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Bundoora, VIC, Australia. Electronic address: V.Sundararajan@latrobe.edu.au.

Natasha A Lannin (NA)

Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia; Alfred Health, Melbourne, VIC, Australia. Electronic address: natasha.lannin@monash.edu.

Joosup Kim (J)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia; Translational Public Health & Evaluation Division, Stroke & Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. Electronic address: joosup.kim@monash.edu.

Monique F Kilkenny (MF)

Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia; Translational Public Health & Evaluation Division, Stroke & Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. Electronic address: monique.kilkenny@monash.edu.

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