Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?
We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers. Data were derived from the el...
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| Veröffentlicht in: | PloS one 2018-10, Vol.13 (10), p.e0204586-e0204586 |
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| creator | Lee, Min-Jeong Park, Joo-Han Moon, Yeo Rae Jo, Soo-Yeon Yoon, Dukyong Park, Rae Woong Jeong, Jong Cheol Park, Inwhee Shin, Gyu-Tae Kim, Heungsoo |
| description | We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers.
Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min-1·1.73 m-2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets.
We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321).
We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT. |
| doi_str_mv | 10.1371/journal.pone.0204586 |
| format | Article |
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Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min-1·1.73 m-2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets.
We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321).
We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0204586</identifier><identifier>PMID: 30286208</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Biology and Life Sciences ; Calcium ; Calcium (blood) ; Chronic kidney failure ; Comorbidity ; Development and progression ; Diabetes ; Diabetes mellitus ; Diagnosis ; Diagnostic systems ; Disease Progression ; Electronic Health Records ; Electronic medical records ; Epidemiology ; Epidermal growth factor receptors ; Female ; Glaucoma ; Glomerular filtration rate ; Health care ; Health informatics ; Hemoglobin ; Hospitals ; Humans ; Integrals ; Kidney diseases ; Kidneys ; Laboratories ; Male ; Mathematical models ; Medical records ; Medicine ; Medicine and Health Sciences ; Middle Aged ; Missing data ; Models, Biological ; Nephrology ; Patients ; Peritoneal dialysis ; Phosphorus ; Physical Sciences ; Polycystic kidney ; Population ; Population studies ; Potassium ; Prediction models ; Prognosis ; Proteins ; Random Allocation ; Renal Insufficiency, Chronic - diagnosis ; Renal Insufficiency, Chronic - physiopathology ; Renal Insufficiency, Chronic - therapy ; Renal Replacement Therapy ; Research and Analysis Methods ; Retrospective Studies ; Risk ; Risk Assessment - methods ; Serum albumin ; Sex ; Test sets ; Therapy ; Time Factors ; Training ; Urea ; Urine ; Young Adult</subject><ispartof>PloS one, 2018-10, Vol.13 (10), p.e0204586-e0204586</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Lee et al 2018 Lee et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-41c3c2d0071306b880e5861befeff5d1f81a99a01299c0ad69c86e9c188b2d943</citedby><cites>FETCH-LOGICAL-c692t-41c3c2d0071306b880e5861befeff5d1f81a99a01299c0ad69c86e9c188b2d943</cites><orcidid>0000-0002-2611-7333 ; 0000-0002-9380-7457</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171856/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171856/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30286208$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Barretti, Pasqual</contributor><creatorcontrib>Lee, Min-Jeong</creatorcontrib><creatorcontrib>Park, Joo-Han</creatorcontrib><creatorcontrib>Moon, Yeo Rae</creatorcontrib><creatorcontrib>Jo, Soo-Yeon</creatorcontrib><creatorcontrib>Yoon, Dukyong</creatorcontrib><creatorcontrib>Park, Rae Woong</creatorcontrib><creatorcontrib>Jeong, Jong Cheol</creatorcontrib><creatorcontrib>Park, Inwhee</creatorcontrib><creatorcontrib>Shin, Gyu-Tae</creatorcontrib><creatorcontrib>Kim, Heungsoo</creatorcontrib><title>Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers.
Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min-1·1.73 m-2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets.
We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321).
We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biology and Life Sciences</subject><subject>Calcium</subject><subject>Calcium (blood)</subject><subject>Chronic kidney failure</subject><subject>Comorbidity</subject><subject>Development and progression</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Disease Progression</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Epidemiology</subject><subject>Epidermal growth factor receptors</subject><subject>Female</subject><subject>Glaucoma</subject><subject>Glomerular filtration rate</subject><subject>Health care</subject><subject>Health informatics</subject><subject>Hemoglobin</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Integrals</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>Laboratories</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical records</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Missing data</subject><subject>Models, Biological</subject><subject>Nephrology</subject><subject>Patients</subject><subject>Peritoneal dialysis</subject><subject>Phosphorus</subject><subject>Physical Sciences</subject><subject>Polycystic kidney</subject><subject>Population</subject><subject>Population studies</subject><subject>Potassium</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Proteins</subject><subject>Random Allocation</subject><subject>Renal Insufficiency, Chronic - diagnosis</subject><subject>Renal Insufficiency, Chronic - physiopathology</subject><subject>Renal Insufficiency, Chronic - therapy</subject><subject>Renal Replacement Therapy</subject><subject>Research and Analysis Methods</subject><subject>Retrospective Studies</subject><subject>Risk</subject><subject>Risk Assessment - methods</subject><subject>Serum albumin</subject><subject>Sex</subject><subject>Test sets</subject><subject>Therapy</subject><subject>Time Factors</subject><subject>Training</subject><subject>Urea</subject><subject>Urine</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12LEzEUhgdR3LX6D0QDguhFazIfmeRGWYofhYUFv25DJjnTSZ0ms0nG2iv_uqntLq3shQzMhJPnvGfOm5wse0rwjBQ1ebNyo7eynw3OwgznuKwYvZedE17kU5rj4v7R-ix7FMIK46pglD7MzgqcsxRn59nvubRoA2jwoI2KaNOBRdGhEKWPyEOqkN5DLxWswUYUO_By2CJj0SCjSaGANiZ2SHXeWaPQD6MtbJE2AWQANAZjl4iitbOxC8i1SPUmcUlWyyjfPc4etLIP8OTwnWTfPrz_Ov80vbz6uJhfXE4V5XmclkQVKtcY16TAtGEMQ2qXNNBC21aatIxIziUmOecKS025YhS4Iow1ueZlMcme73WH3gVx8C6InBBakpLxOhGLPaGdXInBm7X0W-GkEX8Dzi9FssSoHgQQ0jSNZrrSdUklkQXPocaac8KIApW03h6qjc0atEo2edmfiJ7uWNOJpfspKKkJq2gSeHUQ8O56hBDF2gQFfS8tuHH_36zkuOAJffEPend3B2opUwPGti7VVTtRcVFVNWVFmaydZLM7qPRoWBuVLlprUvwk4fVJQmIi_IpLOYYgFl8-_z979f2UfXnEdiD7dHtcP0bjbDgFyz2ovAvBQ3trMsFiNyc3bojdnIjDnKS0Z8cHdJt0MxjFHzRZDmU</recordid><startdate>20181004</startdate><enddate>20181004</enddate><creator>Lee, 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we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?</title><author>Lee, Min-Jeong ; Park, Joo-Han ; Moon, Yeo Rae ; Jo, Soo-Yeon ; Yoon, Dukyong ; Park, Rae Woong ; Jeong, Jong Cheol ; Park, Inwhee ; Shin, Gyu-Tae ; Kim, Heungsoo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-41c3c2d0071306b880e5861befeff5d1f81a99a01299c0ad69c86e9c188b2d943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biology and Life Sciences</topic><topic>Calcium</topic><topic>Calcium (blood)</topic><topic>Chronic kidney failure</topic><topic>Comorbidity</topic><topic>Development and progression</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Disease Progression</topic><topic>Electronic Health Records</topic><topic>Electronic medical records</topic><topic>Epidemiology</topic><topic>Epidermal growth factor receptors</topic><topic>Female</topic><topic>Glaucoma</topic><topic>Glomerular filtration rate</topic><topic>Health care</topic><topic>Health informatics</topic><topic>Hemoglobin</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Integrals</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>Laboratories</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medical records</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Missing data</topic><topic>Models, Biological</topic><topic>Nephrology</topic><topic>Patients</topic><topic>Peritoneal dialysis</topic><topic>Phosphorus</topic><topic>Physical Sciences</topic><topic>Polycystic kidney</topic><topic>Population</topic><topic>Population studies</topic><topic>Potassium</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Proteins</topic><topic>Random Allocation</topic><topic>Renal Insufficiency, Chronic - diagnosis</topic><topic>Renal Insufficiency, Chronic - physiopathology</topic><topic>Renal Insufficiency, Chronic - therapy</topic><topic>Renal Replacement Therapy</topic><topic>Research and Analysis Methods</topic><topic>Retrospective Studies</topic><topic>Risk</topic><topic>Risk Assessment - methods</topic><topic>Serum albumin</topic><topic>Sex</topic><topic>Test sets</topic><topic>Therapy</topic><topic>Time Factors</topic><topic>Training</topic><topic>Urea</topic><topic>Urine</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Min-Jeong</creatorcontrib><creatorcontrib>Park, Joo-Han</creatorcontrib><creatorcontrib>Moon, Yeo Rae</creatorcontrib><creatorcontrib>Jo, Soo-Yeon</creatorcontrib><creatorcontrib>Yoon, Dukyong</creatorcontrib><creatorcontrib>Park, Rae 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and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Min-Jeong</au><au>Park, Joo-Han</au><au>Moon, Yeo Rae</au><au>Jo, Soo-Yeon</au><au>Yoon, Dukyong</au><au>Park, Rae Woong</au><au>Jeong, Jong Cheol</au><au>Park, Inwhee</au><au>Shin, Gyu-Tae</au><au>Kim, Heungsoo</au><au>Barretti, Pasqual</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-10-04</date><risdate>2018</risdate><volume>13</volume><issue>10</issue><spage>e0204586</spage><epage>e0204586</epage><pages>e0204586-e0204586</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers.
Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min-1·1.73 m-2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets.
We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321).
We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30286208</pmid><doi>10.1371/journal.pone.0204586</doi><tpages>e0204586</tpages><orcidid>https://orcid.org/0000-0002-2611-7333</orcidid><orcidid>https://orcid.org/0000-0002-9380-7457</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2018-10, Vol.13 (10), p.e0204586-e0204586 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2116414897 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Aged Aged, 80 and over Biology and Life Sciences Calcium Calcium (blood) Chronic kidney failure Comorbidity Development and progression Diabetes Diabetes mellitus Diagnosis Diagnostic systems Disease Progression Electronic Health Records Electronic medical records Epidemiology Epidermal growth factor receptors Female Glaucoma Glomerular filtration rate Health care Health informatics Hemoglobin Hospitals Humans Integrals Kidney diseases Kidneys Laboratories Male Mathematical models Medical records Medicine Medicine and Health Sciences Middle Aged Missing data Models, Biological Nephrology Patients Peritoneal dialysis Phosphorus Physical Sciences Polycystic kidney Population Population studies Potassium Prediction models Prognosis Proteins Random Allocation Renal Insufficiency, Chronic - diagnosis Renal Insufficiency, Chronic - physiopathology Renal Insufficiency, Chronic - therapy Renal Replacement Therapy Research and Analysis Methods Retrospective Studies Risk Risk Assessment - methods Serum albumin Sex Test sets Therapy Time Factors Training Urea Urine Young Adult |
| title | Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data? |
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