Adapting Data Merging Methods from ICD-9 to ICD-10
Author Information
Author(s): Southern Danielle A, Norris Colleen M, Quan Hude, Shrive Fiona M, Galbraith P Diane, Humphries Karin, Gao Min, Knudtson Merril L, Ghali William A
Primary Institution: University of Calgary
Hypothesis
Can the data merging method for missing data in a cardiac registry be successfully adapted from ICD-9 to ICD-10?
Conclusion
The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method.
Supporting Evidence
- The performance of the new data merging model was comparable to that of the previously reported methodology.
- The prevalence of individual clinical risk factors increased gradually across years.
- ICD-10 has almost twice as many categories as ICD-9, allowing for broader classification.
Takeaway
This study shows how to update a method for filling in missing health data when changing from one coding system to another, making sure the new method works just as well.
Methodology
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.
Potential Biases
ICD-10 coding quality may vary across jurisdictions, and there may be improved validity of ICD-10 coding over time as coders become more familiar with the new coding system.
Limitations
The data merging algorithm may need modification for use in other clinical data registry initiatives if the baseline clinical variables collected do not map directly to those defined in the coding algorithm.
Participant Demographics
The study included 86,649 adults (age ≥ 16 years) undergoing cardiac catheterization, with a mean age of 62.6 years and 69.4% male.
Statistical Information
Confidence Interval
95% CI 0.775, 0.802
Digital Object Identifier (DOI)
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