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Table 5 Comparison among unadjusted, adjusted and multilevel regression models for estimating the impact of carbapenem resistance

From: Economic and clinical burden from carbapenem-resistant bacterial infections and factors contributing: a retrospective study using electronic medical records in Japan

 

Logistic regression

  
 

OR (95% CI)

P-value

AIC

Death

   

 Unadjusted

1.23 (0.76–2.00)

0.407

10,007

 Adjusteda

1.18 (0.70–1.99)

0.543

10,007

 Multilevela,b

1.24 (0.72–2.11)

0.441

8827

 

Log-linear regression†‡

 

Percentage change (95% CI)

P-value

AIC

Total LOS

   

 Unadjusted

38.5% (23.0–53.9%)

 < 0.001

106,802

 Adjusteda

25.4% (10.0–40.8%)

 < 0.001

106,214

 Multilevela,b

42.1% (29.1–55.2%)

 < 0.001

104,230

Total Cost

   

 Unadjusted

50.1% (37.8–62.5%)

 < 0.001

266,426

 Adjusteda

44.7% (33.3–56.2%)

 < 0.001

264,132

 Multilevela,b

50.4% (40.9–60.0%)

 < 0.001

263,400

  1. The impacts of carbapenem resistance infections were calculated against carbapenem susceptible infections as reference
  2. Continuous outcomes of the LOS and cost were log-transformed. The estimated coefficient β of the carbapenem resistance was transformed as percent change per one-unit increase
  3. aAdjusted for age, sex, BMI, CCI, immunosuppressive drugs, antibiotic use before culture test, ICU admission, operation, disease type, and death (if outcome was death, it was excluded from the model)
  4. bHospital was treated as a random effect
  5. AIC Akaike information criterion, BMI body mass index, CCI Charlson Comorbidity Index, CI confidence interval, CR carbapenem-resistant; CS carbapenem-susceptible, LOS length of stay, OR odds ratio