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. 2019 Jun 13;14(6):e0217570. doi: 10.1371/journal.pone.0217570

Table 3. Clinical consequences of using prediction models to guide antibiotic prescription.

Rotterdam, n = 248 Coventry, n = 301
Observed antibiotic prescription, n (%) 51 (21%) 105 (35%)
Predictions by Nijman's model
Threshold 10% Rotterdam Coventry
Number of children below threshold (low-risk group) 130 (52%) 193 (64%)
Expected antibiotic prescription when guided by threshold (benefit) 35 (14%) 49 (16%)
Expected under treatment when prescription was guided by threshold (harm)a 5 (2%) 15 (5%)
Threshold 15%
Number of children below threshold 167 (67%) 229 (76%)
Expected antibiotic prescription when guided by threshold 28 (11%) 36 (12%)
Expected under treatment when prescription was guided by thresholda 8 (3%) 22 (7%)
Predictions by Oostenbrink's model
Threshold 10% Rotterdam Coventry
Number of children below threshold 69 (28%) 94 (31%)
Expected antibiotic prescription when guided by threshold 44 (18%) 77 (26%)
Expected under treatment when prescription was guided by thresholda 0 (0%) 8 (3%)
Threshold 15%
Number of children below threshold 110 (44%) 178 (59%)
Expected antibiotic prescription when guided by threshold 35 (14%) 51 (17%)
Expected under treatment when prescription was guided by thresholda 2 (1%) 13 (4%)
Predictions by Irwin's model
Threshold 10% Rotterdam Coventry
Number of children below threshold 100 (40%) 155 (51%)
Expected antibiotic prescription when guided by threshold 38 (15%) 64 (21%)
Expected under treatment when prescription was guided by thresholda 5 (2%) 15 (5%)
Threshold 15%
Number of children below threshold 120 (48%) 198 (66%)
Expected antibiotic prescription when guided by threshold 33 (13%) 48 (16%)
Expected under treatment when prescription was guided by thresholda 8 (3%) 22 (7%)

a Number of children with a bacterial infection who were treated with antibiotics, but who were classified as low-risk according to the used prediction model and threshold