ERA

Download the full-sized PDF of An Adminstrative Data Merging Solution For Dealing With Missing Data In A Clinical Registry: Adaptation To ICD-9 to ICD-10Download the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3VS1X

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Nursing, Faculty of

Collections

This file is in the following collections:

Chronicity

An Adminstrative Data Merging Solution For Dealing With Missing Data In A Clinical Registry: Adaptation To ICD-9 to ICD-10 Open Access

Descriptions

Author or creator
Southern, D.A.
Norris, C.M.
Quan, H.
Shrive, F.M.
Galbraith, P.D.
Humphries, K.
Gao, M.
Knudtson, M.L.
Ghali, W.A.
Additional contributors
Subject/Keyword
Cardiac catheterization
Alberta
Care outcome analyses
Disease
Revacularization
Survival
Association
Type of item
Journal Article (Published)
Language
English
Place
Time
Description
Background: We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance. Methods: A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995-2001 based on the ICD-9-CM coding algorithm, and for 2002-2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995-2005. The corresponding administrative data records were coded in ICD-9-CM for 1995-2001 and in ICD-10 for 2002-2005. The resulting datasets were then evaluated for their ability to predict death at one year. Results: The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit. Conclusion: The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.
Date created
2008
DOI
doi:10.7939/R3VS1X
License information
Creative Commons Attribution 3.0 Unported
Rights

Citation for previous publication
Southern DA, Norris CM, Quan H, Shrive, FM, Galbraith PD, Humphries K, Gao M, Knudtson ML, Ghali WA, for the APPROACH Investigators. (2008). An Administrative Data Merging Solution For Dealing With Missing Data In A Clinical Registry: Adaptation To ICD-9 to ICD-10. BMC Medical Research Methodology, 8(1), 1-9. doi:10.1186/1471-2288-8-1.
Source
Link to related item

File Details

Date Uploaded
Date Modified
2014-04-30T23:24:50.060+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 280544
Last modified: 2015:10:12 12:57:06-06:00
Filename: BMCMRM_8_2008_1.pdf
Original checksum: f45e026bcbfed1b8a1a860b67de614b8
Well formed: true
Valid: false
Status message: Invalid destination object offset=263818
Status message: Invalid destination object offset=263818
Status message: Invalid destination object offset=263818
Status message: Invalid destination object offset=263818
Status message: Invalid destination object offset=263818
File title: Abstract
File title: 1471-2288-8-1.fm
File author: Mosud.Ali
Page count: 9
Activity of users you follow
User Activity Date