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Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation.
J Transl Med. 2020 03 10; 18(1):120.JT

Abstract

BACKGROUND

The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population.

METHODS

The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age < 18, unstable kidney function, taking trimethoprim or cimetidine, or receiving dialysis were excluded. Among the finally enrolled 1952 participants, 1075 participants (55.07%) from Jan 2012 to Dec 2014 were assigned as the development data whereas 877 participants (44.93%) from Jan 2015 to Jun 2016 as the internal validation data. We in total developed 3 GFR estimation models: a 4-variable revised CKD-EPI (chronic kidney disease epidemiology collaboration) equation (standardized serum creatinine and cystatin C, age and gender), a 9-variable revised CKD-EPI equation (additional auxiliary variables: body mass index, blood urea nitrogen, albumin, uric acid and hemoglobin), and a 9-variable ANN model.

RESULTS

Compared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P < 0.001).

CONCLUSIONS

It is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Authors+Show Affiliations

SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University (SJTU), Room 4-225, Life Science Building, 800 Dongchuan Road, Shanghai, China.Cardiovascular Department, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University (SJTU), Room 4-225, Life Science Building, 800 Dongchuan Road, Shanghai, China. Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.Department of Nephrology, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong, China.SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University (SJTU), Room 4-225, Life Science Building, 800 Dongchuan Road, Shanghai, China. huilu@sjtu.edu.cn. MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China. huilu@sjtu.edu.cn. Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China. huilu@sjtu.edu.cn.Clinical data center of the Third Affiliated Hospital of Sun Yat sen University, Guangdong, China. naturestyle@163.com. Department of Nephrology, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong, China. naturestyle@163.com.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32156297

Citation

Li, Ningshan, et al. "Improving Accuracy of Estimating Glomerular Filtration Rate Using Artificial Neural Network: Model Development and Validation." Journal of Translational Medicine, vol. 18, no. 1, 2020, p. 120.
Li N, Huang H, Qian HZ, et al. Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation. J Transl Med. 2020;18(1):120.
Li, N., Huang, H., Qian, H. Z., Liu, P., Lu, H., & Liu, X. (2020). Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation. Journal of Translational Medicine, 18(1), 120. https://doi.org/10.1186/s12967-020-02287-y
Li N, et al. Improving Accuracy of Estimating Glomerular Filtration Rate Using Artificial Neural Network: Model Development and Validation. J Transl Med. 2020 03 10;18(1):120. PubMed PMID: 32156297.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation. AU - Li,Ningshan, AU - Huang,Hui, AU - Qian,Han-Zhu, AU - Liu,Peijia, AU - Lu,Hui, AU - Liu,Xun, Y1 - 2020/03/10/ PY - 2019/12/14/received PY - 2020/02/27/accepted PY - 2020/3/12/entrez PY - 2020/3/12/pubmed PY - 2020/3/12/medline KW - Artificial neural network KW - Chronic kidney disease KW - Estimating equation KW - Glomerular filtration rate SP - 120 EP - 120 JF - Journal of translational medicine JO - J Transl Med VL - 18 IS - 1 N2 - BACKGROUND: The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population. METHODS: The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age < 18, unstable kidney function, taking trimethoprim or cimetidine, or receiving dialysis were excluded. Among the finally enrolled 1952 participants, 1075 participants (55.07%) from Jan 2012 to Dec 2014 were assigned as the development data whereas 877 participants (44.93%) from Jan 2015 to Jun 2016 as the internal validation data. We in total developed 3 GFR estimation models: a 4-variable revised CKD-EPI (chronic kidney disease epidemiology collaboration) equation (standardized serum creatinine and cystatin C, age and gender), a 9-variable revised CKD-EPI equation (additional auxiliary variables: body mass index, blood urea nitrogen, albumin, uric acid and hemoglobin), and a 9-variable ANN model. RESULTS: Compared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P < 0.001). CONCLUSIONS: It is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model. SN - 1479-5876 UR - https://www.unboundmedicine.com/medline/citation/32156297/Improving_accuracy_of_estimating_glomerular_filtration_rate_using_artificial_neural_network:_model_development_and_validation L2 - https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02287-y DB - PRIME DP - Unbound Medicine ER -
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