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Our team has previously described our data merging methodology for dealing with missing data in APPROACH.
The multi-step data merging methodology is summarized in Box 1.
Norris et al. previously demonstrated that the merged data performs better than the complete set of clinical variables without use of the administrative data merging methodology [ 1].
For the purposes of this data merging methodology research, clinical data were obtained for 86,649 adults (age ≥ 16 years) undergoing cardiac catheterization at one of the three hospitals in Alberta performing this procedure.
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The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 955% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 955% CI 0.780, 0.790) for the ICD-9-CM model.
Figure 3 The description of data merging process.
Structural constraints and keys are used to identify a certain entity, as rules for data merging.
The data merging and analysis were done using STATA Version 10.
Low and high concentration data were merged for data analysis.
MO, EAC & KBW assisted with all data linkage, merging, and recoding.
LL assisted with all data linkage, merging, and recoding.
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