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The Charlson and Elixhauser indices are the most commonly used comorbidity indices with risk prediction models using administrative data.
AMI mortality risk-adjustment models using administrative data typically adjust for baseline differences in mortality risk with a limited set of common and definite comorbidities.
We validated this higher accuracy by computing the MAPE across all data points for both models, and found a 20%to50%0% higher error for the models using administrative boundaries.
Our mortality prediction models using administrative data showed good discriminatory power in CAP patients.
Previous studies have shown that the performance of risk-adjustment models using administrative databases improves when detailed clinical information is added.
However, previous studies have also shown that using self-report comorbidity indices, although different from HRQ, performed similarly to predictive models using administrative medical records data providing additional case for reliability of the data [ 28, 29].
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In the model using administrative data, the ever hospitalised group was defined based on HA's individual-level hospitalisation episode records whereas the never hospitalised group was derived from the difference between the territory-wide population figure and the former hospitalised headcounts.
Statistical models developed using administrative databases are powerful and inexpensive tools for predicting survival.
With regard to the type of data used for model development, the BAR score and VGNW model were built using clinical datasets, and the Medicare model was developed using administrative data.
Model-based algorithms using administrative data failed to identify screening colonoscopies with sufficient accuracy.
Mixed-effect models using an administrative sub-location as a random effect were also fitted to adjust for possible clustering of PPRV sero-positivity.
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