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

Olivia F Ryan; Merilyn Riley; Dominique A Cadilhac; Nadine E Andrew; Sibilah Breen; Kate Paice; Sam Shehata; Vijaya Sundararajan; Natasha A Lannin; Joosup Kim; Monique F Kilkenny

doi:10.1016/j.jstrokecerebrovasdis.2020.105469

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

J Stroke Cerebrovasc Dis. 2021 Feb;30(2):105469. doi: 10.1016/j.jstrokecerebrovasdis.2020.105469. Epub 2020 Nov 27.

Authors

Affiliations

¹ Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: olivia.ryan@florey.edu.au.
² 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.
³ 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.
⁴ 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.
⁵ Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: sibilah.breen@florey.edu.au.
⁶ Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: kate.paice@florey.edu.au.
⁷ Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia. Electronic address: sam@247.com.au.
⁸ 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.
⁹ Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia; Alfred Health, Melbourne, VIC, Australia. Electronic address: natasha.lannin@monash.edu.
¹⁰ 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.
¹¹ 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.

PMID: 33253990
DOI: 10.1016/j.jstrokecerebrovasdis.2020.105469

Abstract

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: 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: 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: 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.

Keywords: Health administrative data; International Classification of Diseases; Stroke; Validation studies.

Publication types

Comparative Study
Multicenter Study
Observational Study

MeSH terms

Administrative Claims, Healthcare
Aged
Aged, 80 and over
Australia / epidemiology
Databases, Factual
Female
Hemorrhagic Stroke / classification
Hemorrhagic Stroke / diagnosis*
Hemorrhagic Stroke / epidemiology
Hemorrhagic Stroke / therapy
Humans
International Classification of Diseases / standards*
Ischemic Attack, Transient / classification
Ischemic Attack, Transient / diagnosis*
Ischemic Attack, Transient / epidemiology
Ischemic Attack, Transient / therapy
Ischemic Stroke / classification
Ischemic Stroke / diagnosis*
Ischemic Stroke / epidemiology
Ischemic Stroke / therapy
Length of Stay
Male
Middle Aged
Patient Discharge
Registries
Reproducibility of Results
Terminology as Topic*