Objective:
This study aimed to measure concordance between different renal function estimates in terms of drug doses and determine the p
otential
significant clinical differences.
Method
s:
Around
one hundred and eighty patients (
≥ 1
8 y
) with chronic kidney disease (CKD) were eligible for inclusion in this study. A paired
-
proportion cohort design was utilized using an artificial intelligence model. CKD patients refined into those who have drugs adju
sted for renal function.
For superiority of Cockcroft
-Gault (CG) vs. modified diet in renal disease (MDRD) guided with references for concordance or discordance of the two
equations and determined the dosing tiers of each drug. Validated artificial neural networks (ANN) was one outcome of interest. Variable impacts and
performed reassignments were compared to evaluate the factors that affect the accuracy in estimating the kidney function for
a better drug dosing.
Result
s:
The best ANN model classified most
cases to CG as the best dosing method (79 vs. 72). The probability was 85% and the top performance
was slightly above 93%. Creatinine levels and CKD staging were the most important factors in determining the best dosing meth
od
of CG versus
MDRD. Ideal and actual body weights were second (24%). Whereas drug class or the specific drug was an important third factor
(14%).
Conclusio
n:
Among many variables that affect the optimal dosing method, the top three are probably CKD staging, w
eight, and the drug. The
contrasting CKD stages from the different methods can be used to recognize patterns, identify and predict the best dosing tac
tics in CKD patients.