# Table 3 Weighted quadratic or linear regression models to predict changes in incidence of primary and secondary outcomes during the study period

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.