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Table 5 Weighted quadratic or linear regression models to predict the changes in incidence of primary and secondary outcomes during the implementation period

From: Baseline hospital performance and the impact of medical emergency teams: Modelling vs. conventional subgroup analysis

  Change of primary outcome Change of unexpected cardiac arrests Change of unplanned ICU admission Change of unexpected death
  Control: quadratic effect Control: linear effect MET Control MET Control: quadratic effect Control: linear effect MET Control: quadratic effect Control: linear effect MET
Baseline incidence (per 1000 admissions) -2.393
(0.008)**
0.099
(0.699)
-0.497
(0.001)**
-0.163
(0.761)
-0.585
(<0.001)**
-0.941
(0.046)*
-0.081
(0.547)
-0.504
(<0.001)**
-2.810
(0.001)**
-0.844
(0.003)**
-0.587
(<0.001)**
Baseline incidence squared 0.154
(0.006)**
     0.061
(0.056)
   0.487
(0.006)**
  
Constant 7.342
(0.022)*
-1.379
(0.455)
2.191
(0.008)**
0.292
(0.841)
0.952
(0.004)**
2.160
(0.089)
0.053
(0.943)
1.767
(0.007)**
2.700
(0.001)**
1.067
(0.026)*
0.497
(0.019)*
R-squared 0.645 0.017 0.717 0.011 0.731 0.411 0.042 0.719 0.870 0.653 0.732
  1. Note: P values (in the parentheses) for the regression coefficients and the constant; control hospitals showed a quadratic effect for only the primary outcome and unexpected deaths,.
  2. * P < 0.05; ** P < 0.01
  3. Only the MET hospitals showed a linear effect for all of the four outcomes.