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Table 1 Characteristics of participants in the development and internal validation datasets

From: Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation

Variable

Development dataset (N = 1075)

Internal validation dataset (N = 877)

P value

Age (year)

55.6 ± 14.5

57.4 ± 13.4

0.008

Male sex, N (%)

616 (57.3)

517 (59.0)

0.5

Body mass index (kg/m2)

24.0 ± 3.6

24.5 ± 3.8

0.005

Serum creatinine (mg/dL)*

1.5 ± 1.3

1.3 ± 0.9

< 0.001

Serum cystatin C (mg/L)

1.5 ± 0.9

1.3 ± 0.7

< 0.001

Blood urea nitrogen (mg/dL)

20.9 ± 13.5

21.5 ± 11.7

0.3

Albumin (g/dL)

3.9 ± 0.5

3.9 ± 0.5

0.3

Uric acid (mg/dL)

6.7 ± 2.1

7.1 ± 2.2

< 0.001

Hemoglobin (g/L)

12.3 ± 2.3

12.7 ± 2.1

< 0.001

Measured GFR (mL/min/1.73 m2)

71.0 ± 27.4

68.8 ± 27.1

0.08

Measured GFR N(%)

0.04

 < 30 mL/min/1.73 m2

70 (6.5)

55 (6.3)

 

 30–59 mL/min/1.73 m2

310 (28.8)

304 (34.7)

 

 60–89 mL/min/1.73 m2

420 (39.1)

324 (36.9)

 

 ≥ 90 mL/min/1.73 m2

275 (25.6)

194 (22.1)

 
  1. Values for continuous variables were reported as mean ± standard deviation, and values for categorical variables as number (percentage). Conversion factor for units: serum creatinine in mg/dL to mmol/L × 88.4
  2. GFR glomerular filtration rate