A back propagation neural network approach to estimate the glomerular filtration rate in an older population

• Published in: BMC geriatrics Vol. 23; no. 1; pp. 322 - 10
• Main Authors: Jiang, Shimin, Li, Yetong, Jiao, Yuanyuan, Zhang, Danyang, Wang, Ying, Li, Wenge
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 24.05.2023; BioMed Central; BMC
• Subjects: Accuracy; Age groups; Aged; Analysis; Back propagation; Back propagation neural network; Bias; Care and treatment; Creatinine; Deep learning; Diabetes; Diagnosis; Elderly; Epidemiology; Estimation equation; Evaluation; Female; Geriatrics; Glomerular Filtration Rate; Humans; Independent variables; Kidney; Kidney diseases; Machine learning; Male; Neural networks; Neural Networks, Computer; Neurons; Older people; Pentetic Acid; Personal health; Renal Insufficiency, Chronic - diagnostic imaging; Serum creatinine; Software; Technetium; China; Back propagation neural network; Estimation equation; Elderly; Serum creatinine; Glomerular filtration rate
• ISSN: 1471-2318; 1471-2318
• DOI: 10.1186/s12877-023-04027-5

The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid ( Tc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m , which was smaller than that of LMR (4.59 ml/min/1.73 m ; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m ; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m ; p = 0.31), EKFC (-1.41 ml/min/1.73 m ; p = 0.26), BIS1 (0.64 ml/min/1.73 m ; p = 0.99), and MDRD (1.11 ml/min/1.73 m ; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m ) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m , the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m ). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation. The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.