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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.