VARIABLES IMPACTING GFR ESTIMATION METHOD FOR DRUG DOSING IN CKD: ARTIFICIAL NEURAL NETWORK PREDICTION MODEL
Objective: This study aimed to measure concordance between different renal function estimates in terms of drug doses and determine the potential significant clinical differences.
Methods: Around one hundred and eighty patients (≥ 18 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 adjusted 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.
Results: 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 method 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%).
Conclusion: Among many variables that affect the optimal dosing method, the top three are probably CKD staging, weight, and the drug. The contrasting CKD stages from the different methods can be used to recognize patterns, identify and predict the best dosing tactics in CKD patients.
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