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We therefore aimed to develop an accurate GFR-estimating tool for this age group.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Adults aged\u2009\u2265\u200965 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid\u00a0(<jats:sup>99m<\/jats:sup>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-Malm\u00f6 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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study included 1,222 older adults. The mean age of both the training cohort (n\u2009=\u2009978) and the test cohort (n\u2009=\u2009244) was 72\u2009\u00b1\u20096 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>, which was smaller than that of LMR (4.59 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.02). The median bias between BPNN and each of CKD-EPI (2.19 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.31), EKFC (-1.41 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.26), BIS1 (0.64 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.99), and MDRD (1.11 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>; p\u2009=\u20090.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>) and the greatest accuracy P30 among all equations (78.28%). At measured GFR\u2009&lt;\u200945 ml\/min\/1.73 m<jats:sup>2<\/jats:sup>, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46\u00a0ml\/min\/1.73 m<jats:sup>2<\/jats:sup>). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55\u22122.78] and 0.24 [-2.58\u22121.61], respectively), smaller than any other equation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12877-023-04027-5","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T06:02:09Z","timestamp":1684908129000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A back propagation neural network approach to estimate the glomerular filtration rate in an older population"],"prefix":"10.1186","volume":"23","author":[{"given":"Shimin","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yetong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenge","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"issue":"4s","key":"4027_CR1","first-page":"1","volume":"100","author":"Kidney Disease","year":"2021","unstructured":"Kidney Disease. 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