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Table 5 Results of original Elixhauser models 1 and enhanced Elixhauser 2 models

From: The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data

MRDx3

c statistics

Hosmer-Lemeshow test

Elixhauser4

Enhanced5

Elixhauser4

Enhanced5

 

(95%CI6)

(95%CI6)

chi-square (P)

chi-square (P)

ICH7

0.736

0.748

7.3 (0.50)

7.8 (0.46)

(0.723-0.749)

(0.736-0.760)

Pneumonia

0.917

0.920

26.8 (<0.01)

26.8 (<0.01)

(0.912-0.922)

(0.915-0.925)

Ischemic infarct

0.787

0.805

14.6 (0.07)

17.2 (0.03)

(0.767-0.807)

(0.786-0.824)

AMI8

0.809

0.825

35.8 (<0.01)

35.4 (<0.01)

(0.795-0.823)

(0.811-0.839)

Non-alcoholic liver disease

0.798

0.811

24.1 (<0.01)

25.4 (<0.01)

(0.782-0.814)

(0.796-0.826)

Intracranial injury

0.778

0.790

12.3 (0.14)

13.5 (0.10)

(0.759-0.797)

(0.771-0.809)

CRF9

0.832

0.840

19.3 (0.01)

28.0 (<0.01)

(0.816-0.848)

(0.825-0.855)

COPD10

0.810

0.815

8.7 (0.37)

12.2 (0.14)

(0.789-0.831)

(0.795-0.835)

Alcoholic liver disease

0.777

0.788

8.9 (0.35)

3.2 (0.92)

(0.753-0.801)

(0.764-0.812)

Aspiration pneumonia

0.730

0.734

3.2 (0.92)

8.1 (0.42)

(0.703-0.757)

(0.707-0.761)

CHF11

0.699

0.707

3.5 (0.90)

4.7 (0.79)

(0.672-0.726)

(0.680-0.734)

Coronary atherosclerosis

0.881

0.889

22.7 (<0.01)

14.2 (0.08)

 

(0.862-0.900)

(0.869-0.909)

  
  1. 1 & 2 Multiple logistic regression models for predicting in-hospital mortalities composed of age + sex + status of health insurance + admission category (emergent or not) + operation (yes or no) + presence of each Elixhauser comorbidity (yes or no), before (Elixhauser models) and after (enhanced Elixhauser models) adding comorbidities inferred by drug prescription information, 3 Most responsive diagnoses, 4 Elixhauser models, 5 Enhanced Elixhauser models, 6 95% confidence interval calculated by bootstrapping, 7 Intracranial hemorrhage, 8 Acute myocardial infarction, 9 Chronic renal failure, 10 Chronic obstructive pulmonary disease, 11 Congestive heart failure.