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Table 3 Weighted quadratic or linear regression models to predict changes in incidence of primary and secondary outcomes during the study 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 MET Control: quadratic effect Control: linear effect MET
Baseline incidence (per 1000 admissions) -2.168
(0.017)*
-0.135
(0.562)
-0.592
(0.001)**¶
-0.945
(<0.001)**
-0.736
(<0.001)**
-0.161
(0.313)
-0.556
(0.002)**
-2.076
(0.002)**
-1.039
(<0.001)**
-0.676
(<0.001)**
Baseline incidence squared 0.125
(0.020)*
       0.256
(0.048)*
  
Constant 6.632
(0.041)*
-0.518
(0.753)
2.789
(0.005)**
1.487
(0.009)**
0.932
(0.002)**
0.215
(0.804)
2.214
(0.007)**
2.046
(0.001)**
1.182
(0.002)**
0.587
(0.004)**
R-squared 0.532 0.039 0.710 0.782 0.851 0.112 0.643 0.923 0.870 0.819
  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. ¶The sensitivity analysis by removing the two hospitals with highest baseline incidence in MET hospitals produced a regression coefficient for baseline incidence as -0.416 with p = 0.034;
  4. Only the MET hospitals showed a linear effect for all of the four outcomes.