Comparison of a neural network approach with five traditional methods for predicting creatinine clearance in patients with human immunodeficiency virus infection.
OBJECTIVE
To compare the results of an artificial neural network approach with those of five published creatinine clearance (Cl(cr)) prediction equations and with the measured (true) Cl(cr) in patients infected with the human immunodeficiency virus (HIV).


METHODS
Six-month prospective study.


METHODS
Two university medical centers.


METHODS
Sixty-five HIV-infected patients: 18 relatively healthy outpatients and 47 inpatients.


METHODS
All subjects had urine collected for 24 hours to determine Cl(cr).


RESULTS
The 16 input variables were age, ideal body weight, actual body weight, body surface area, height, and the following blood chemistries: sodium, potassium, aspartate aminotransferase, alanine aminotransferase, red blood cell count, platelet count, white blood cell count, glucose, serum creatinine, blood urea nitrogen, and albumin. The only output variable was Cl(cr). A training set of 55 subjects was used to develop the relationship between input variables and the output variable. The trained neural network was then used to predict Cl(cr) of a validation set of 10 subjects. Mean differences between predicted Cl(cr) and actual Cl(cr) (bias) were 4.1, 28.7, 29.4, 26.0, 31.8, and 55.8 ml/min/1.73 m2 for the artificial neural network, Cockcroft and Gault, Jelliffe 1, Jelliffe 2, Mawer et al, and Hull et al methods, respectively.


CONCLUSIONS
The accuracy of predicting Cl(cr) in subjects with HIV infection by the artificial neural network is superior to that of the five equations that are currently used in clinical settings.
Herman
R A
RA
College of Pharmacy, University of Iowa, Iowa City, USA.
Noormohamed
S
S
Hirankarn
S
S
Shelton
M J
MJ
Huang
E
E
Morse
G D
GD
Hewitt
R G
RG
Stapleton
J T
JT
D000328:Adult; D003404:Creatinine / Q000652:urine*; D005260:Female; D015658:HIV Infections / Q000652:urine*; D006801:Humans; D007484:Iowa; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011446:Prospective Studies
D003404:Creatinine