title: Categorized diagnoses and procedure records in an administrative database improved mortality prediction.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  Subcategorized diagnoses allowed correct identification of comorbidities and procedures predicted mortality. Incorporation of these two features should be considered for other administrative databases.
authors.affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Bunkyo City Public Health Center, 1-16-21 Kasuga, Bunkyo-ku, Tokyo 112-8555, Japan.
authors.forename: Hayato
authors.initials: H
authors.lastname: Yamana
authors.affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
authors.forename: Hiroki
authors.initials: H
authors.lastname: Matsui
authors.affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Sasabuchi
authors.affiliation: Department of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School of Medicine, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
authors.forename: Kiyohide
authors.initials: K
authors.lastname: Fushimi
authors.affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. Electronic address: yasunagah-tky@umin.ac.jp.
authors.forename: Hideo
authors.initials: H
authors.lastname: Yasunaga
mesh_terms: D000368:Aged; D002395:Catecholamines / Q000627:therapeutic use; D015897:Comorbidity; D016208:Databases, Factual; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D038801:International Classification of Diseases; D007564:Japan / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D009026:Mortality / Q000639:trends*; D010102:Oxygen Inhalation Therapy; D011237:Predictive Value of Tests; D015995:Prevalence; D020379:Risk Adjustment; D012307:Risk Factors; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed; D014546:Urinary Catheterization; D014667:Vasopressins / Q000627:therapeutic use
chemical_list: D002395:Catecholamines; D014667:Vasopressins
keywords: Administrative data; Charlson; Comorbidity; Elixhauser; In-hospital mortality; Severity