Table 3 Logistic regression predictive models results by using changes in biomarker levels from baseline to week 4 and clinical response at week 14. Model included training and test datasets combined

CRP-only model ^a (n = 308)

True/Predicted

NR

R

Accuracy

NR

35

115

Specificity = 23%

R

16

142

Sensitivity = 90%

Predictive value

NPV = 68%

PPV = 55%

Threshold = -0.35

Biomarker model ^a (n = 308)

True/Predicted

NR

R

Accuracy

NR

62

88

Specificity = 41%

R

16

142

Sensitivity = 90%

Predictive value

NPV = 79%

PPV = 61%

Threshold = -0.35

Predictors from biomarker model^b

Weight

Odds ratio

Multivariate P value

Hyaluronic acid

0.60

1.82

0.0001

Apolipoprotein C3

-0.98

0.38

0.003

Rheumatoid factor positive at baseline

-0.91

0.4

0.014

Basic fibroblast growth factor

0.72

2.05

0.023

Interleukin-16

0.83

2.29

0.031

Interleukin-6 serum

0.21

1.23

0.049

C-reactive protein

0.17

1.19

0.062

ACR 50

CRP-only model ^a (n = 308)

True/Predicted

NR

R

Accuracy

NR

108

116

Specificity = 48%

R

17

67

Sensitivity = 80%

Predictive Value

NPV = 86%

PPV = 36%

Threshold = -1.3

Biomarker model ^a (n = 308)

True/Predicted

NR

R

Accuracy

NR

137

87

Specificity = 61%

R

17

67

Sensitivity = 80%

Predictive value

NPV = 89%

PPV = 44%

Threshold = -1.2

Predictors from biomarker model^b

Weight

Odds ratio

Multivariate P value

Hyaluronic acid

0.53

1.70

0.001

Haptoglobin

0.79

2.2

0.001

Apolipoprotein C3

-0.94

0.39

0.007

Interleukin-16

1

2.72

0.014

STJC at baseline

0.17

1.19

0.015

Anti-CCP antibodies

-0.82

0.44

0.044