Categorized diagnoses and procedure records in an administrative database improved mortality prediction.

Objectives: Comorbidity measures are widely used in administrative databases to predict mortality. The Japanese Diagnosis Procedure Combination database is unique in that secondary diagnoses are recorded into subcategories, and procedures are precisely recorded. We investigated the influence of these features on the performance of mortality prediction models.
Study Design and Setting: We obtained data of adult patients with main diagnosis of acute myocardial infarction, congestive heart failure, acute cerebrovascular disease, gastrointestinal hemorrhage, pneumonia, or septicemia during a 1-year period. Multiple models were constructed representing different subcategories from which Charlson and Elixhauser comorbidities were extracted. Prevalence of comorbidities and C statistics of logistic regression models predicting in-hospital mortality was compared. Associations between four procedures (computed tomography, oxygen administration, urinary catheter, and vasopressors) and mortality were also evaluated.
Results: C statistics of the model using all secondary diagnoses (Charlson: 0.717; Elixhauser: 0.762) were greater than those using a limited subcategory to strictly specify comorbidities (Charlson: 0.708; Elixhauser: 0.744). However, misidentification of complications and main diagnoses as comorbidities was observed in the all-diagnosis model. The four procedures were associated with mortality.
Conclusion: Subcategorized diagnoses allowed correct identification of comorbidities and procedures predicted mortality. Incorporation of these two features should be considered for other administrative databases.
(Copyright © 2015 Elsevier Inc. All rights reserved.)