Key result
An AI-based rule extraction algorithm using UACR identified cut-off values of 6.1 and 71.00 mg/g Cr to detect early diabetic kidney disease, achieving a test accuracy of 77.56% and an AUC of 75%.
Why the study?
The study aimed to determine the lowest cut-off value for albuminuria levels to detect diabetic kidney disease using the urinary albumin-to-creatinine ratio.
Can an artificial intelligence-based rule extraction approach identify lower albuminuria (UACR) cut-off values to detect early diabetic kidney disease in adults with diabetes or prediabetes?
Cross-Sectional (n=1,466)
Can an artificial intelligence-based rule extraction approach identify lower albuminuria (UACR) cut-off values to detect early diabetic kidney disease in adults with diabetes or prediabetes?
An AI-based rule extraction approach identified lower UACR cut-off values (6.1 mg/g Cr) that may help detect diabetic kidney disease earlier than traditional microalbuminuria thresholds.
Should not yet change UACR thresholds for DKD screening; leaves open whether lower cutoffs enable earlier detection in prediabetes.
The aim of the present study was to determine the lowest cut-off value for albuminuria levels, which can be used to detect diabetic kidney disease (DKD) using the urinary albumin-to-creatinine ratio (UACR). National Health and Nutrition Examination Survey (NHANES) data for US adults were used, and participants were classified as having diabetes or prediabetes based on a self-report and physiological measures. The study dataset comprised 942 diabetes and 524 prediabetes samples. This study clarified the significance of the lower albuminuria (UACR) levels, which can detect DKD, using an artificial intelligence-based rule extraction approach. The diagnostic rules (15 concrete rules) for both samples were extracted using a recursive-rule eXtraction (Re-RX) algorithm with continuous attributes (continuous Re-RX) to discriminate between prediabetes and diabetes datasets. Continuous Re-RX showed high test accuracy (77.56%) and a large area under the receiver operating characteristics curve (75%), which derived the two cut-off values (6.1 mg/g Cr and 71.00 mg/g Cr) for the lower albuminuria level in the UACR to detect early development of DKD. The early cut-off values for normoalbuminuria (NA) and microalbuminuria (MA) will be determined to help detect CKD and DKD, and to detect diabetes before MA develop and to prevent diabetic complications.
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Yoichi Hayashi (2019) conducted a cross-sectional in Diabetic kidney disease (n=1,466). Urinary albumin-to-creatinine ratio (UACR) was evaluated on Detection of early development of diabetic kidney disease. An AI-based rule extraction algorithm using UACR identified cut-off values of 6.1 and 71.00 mg/g Cr to detect early diabetic kidney disease, achieving a test accuracy of 77.56% and an AUC of 75%.
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