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Table 3 Performance of the new regression equations and ANN models in the external validation data set

From: Development and validation of new glomerular filtration rate predicting models for Chinese patients with type 2 diabetes

 

Bias (ml/min/1.73 m2)

Precision (ml/min/1.73 m2)

Accuracy (%)

CKD-EPI

−6.00

24.20

80.0

Japanese equation 1

−20.48*

22.43*

54.3*

Japanese equation 2

−30.67* 

23.55*

29.5*

New equation 1

−2.49

22.50*

84.8

ANN1

−2.70

21.37*

87.6

New equation 2

−2.88

22.07*

84.8

ANN2

−4.87

20.70*

83.8

New equation 3

−3.97

21.22*

80.0

ANN3

−5.97

20.49*

88.6‡

New equation 4

−2.23

21.11*

84.8

ANN4

−3.08

19.96*

84.8

New equation 5

−3.97

21.46*

80.0

ANN5

−6.81

22.93*

85.7

New equation 6

−3.19

21.64*

83.8

ANN6

−5.31

25.87*

88.6

New equation 7

−2.91

21.35*

81.9

ANN7

−5.97

22.51*

87.6

New equation 8

−4.48

21.89*

81.9

ANN8

−5.73

22.30*

87.6

  1. CKD-EPI equation Chronic Kidney Disease Epidemiology Collaboration, ANN Artificial neural network
  2. * P < 0.001, ‡P < 0.05 comparing with the CKD-EPI equation
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Journal of Translational Medicine

ISSN: 1479-5876

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