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Table 2 Odds ratios and 95% CIs for multilevel logistic models estimating 30-day in-hospital mortality in the overall sample

From: 30-day in-hospital mortality after acute myocardial infarction in Tuscany (Italy): An observational study using hospital discharge data

 

Hierarchical null model

Hierarchical model without presence of a cardiac catheterisation lab.

Hierarchical model with presence of a cardiac catheterisation lab.

Patients Characteristics

   

Gender (male vs. female)

–

1.10 (0.90–1.36)

1.09 (0.89–1.35)

Age (years)

–

1.09 (1.08–1.10)

1.09 (1.08–1.10)

History of COPD

–

1.99 (1.31–3.01)

1.91 (1.26–2.88)

History of heart failure

–

1.47 (1.07–2.02)

1.46 (1.06–2.00)

History of cerebrovascular diseases

–

1.49 (1.04–2.14)

1.49 (1.04–2.14)

Cerebrovascular diseases

–

1.45 (1.01–2.09)

1.42 (0.99–2.04)

History of tumours

–

2.65 (1.73–4.05)

2.55 (1.67–3.90)

ST-segment elevation (STEMI vs. NSTEMI)

–

2.26 (1.83–2.78)

2.31 (1.88–2.84)

Hospital Characteristic

   

Presence of cardiac catheterisation lab.

–

–

0.71 (0.58–0.87)

Hospital Variance

   

σ 2 (p-value)*

0.12 (<0.001)

0.05 (0.084)

<0.01 (1.000)

Goodness of fit

   

Pseudo R 2

–

0.31

0.32

Wald χ 2 (p-value)

–

335.25 (<0.001)

350.58 (<0.001)

AIC

3,261.08

2,843.00

2,836.27

BIC

3,264.30

2,859.11

2,853.99

  1. * p-value from LR (likelihood ratio) test vs. logistic regression of σ 2 = 1.
  2. CI confidence interval, AIC Akaike Information Criterion, BIC Bayesian Information Criterion.