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Table 3 Comparison of methicillin-resistant Staphylococcus aureus (MRSA) screening strategies using different predictive models

From: Variable performance of models for predicting methicillin-resistant Staphylococcus aureus carriage in European surgical wards

Cut-off for screening

No. of patients to be screened (%)

No. of patients MRSA positive

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

Universal screening

1,451 (100)

49

100

0

3.4

-

Predicted probability ≥ 2%

      

  Stepwise model

1,213 (83.6)

46

93.9

16.8

3.8

98.7

  Best BMA model

1,451 (100)

49

100

0

3.4

-

  BMA model

1,437 (99.0)

49

100

1.0

3.4

100

  Simple model

1,201 (82.8)

47

95.9

17.7

3.9

99.2

Predicted probability ≥ 3%

      

  Stepwise model

813 (56.0)

37

75.5

44.7

4.6

98.1

  Best BMA model

528 (36.4)

34

69.4

64.8

6.4

98.4

  BMA model

1,137 (78.4)

44

89.8

22.0

3.9

98.4

  Simple model

776 (53.5)

39

79.6

47.4

5.0

98.5

Predicted probability ≥ 4%

      

  Stepwise model

482 (33.2)

30

61.2

67.8

6.2

98.0

  Best BMA model

528 (36.4)

34

69.4

64.8

6.4

98.4

  BMA model

463 (31.9)

31

63.3

69.2

6.7

98.2

  Simple model

479 (33.0)

33

67.3

68.2

6.9

98.4

Predicted probability ≥ 5%

      

  Stepwise model

354 (24.4)

26

53.1

76.6

7.3

97.9

  Best BMA model

167 (11.5)

19

38.8

89.4

11.4

97.7

  BMA model

133 (9.2)

16

32.7

91.7

12.0

97.5

  Simple model

336 (23.2)

27

55.1

78.0

8.0

98.0

Predicted probability ≥ 6%

      

  Stepwise model

229 (15.8)

18

36.7

85.0

7.9

97.5

  Best BMA model

126 (8.7)

17

34.7

92.2

13.5

97.6

  BMA model

64 (4.4)

6

12.2

95.9

9.4

96.9

  Simple model

220 (15.2)

20

40.8

85.7

9.1

97.6

  1. Note. The table shows the results when a random sample of 50% of the full cohort was used as the derivation dataset with the remaining data used as the validation dataset. MRSA, methicillin-resistant Staphylococcus aureus; NPV, negative predictive value; PPV, positive predictive value.