Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy
To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR]
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| Veröffentlicht in: | Journal of endourology 2016-01, Vol.30 (1), p.114-121 |
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| creator | Liss, Michael A DeConde, Robert Caovan, Dominique Hofler, Joseph Gabe, Michael Palazzi, Kerrin L Patel, Nishant D Lee, Hak J Ideker, Trey Van Poppel, Hendrik Karow, David Aertsen, Michael Casola, Giovanna Derweesh, Ithaar H |
| description | To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] |
| doi_str_mv | 10.1089/end.2015.0411 |
| format | Article |
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Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC).
We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89.
Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.</description><identifier>ISSN: 0892-7790</identifier><identifier>EISSN: 1557-900X</identifier><identifier>DOI: 10.1089/end.2015.0411</identifier><identifier>PMID: 26192380</identifier><language>eng</language><publisher>United States: Mary Ann Liebert, Inc</publisher><subject>Aged ; Carcinoma, Renal Cell - diagnostic imaging ; Carcinoma, Renal Cell - pathology ; Carcinoma, Renal Cell - surgery ; Decision Support Techniques ; Female ; General Research ; Glomerular Filtration Rate ; Humans ; Image Processing, Computer-Assisted ; Kidney - diagnostic imaging ; Kidney - surgery ; Kidney Neoplasms - diagnostic imaging ; Kidney Neoplasms - pathology ; Kidney Neoplasms - surgery ; Linear Models ; Male ; Middle Aged ; Nephrectomy - methods ; Nephrons ; Organ Size ; Organ Sparing Treatments - methods ; Postoperative Complications - epidemiology ; Radiography ; Renal Insufficiency, Chronic - epidemiology ; Retrospective Studies ; Tumor Burden</subject><ispartof>Journal of endourology, 2016-01, Vol.30 (1), p.114-121</ispartof><rights>Copyright 2016, Mary Ann Liebert, Inc. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-e958db2a74291ff6c687efd46f684e2e2615a6ad923b5f11e7b652480bf0e8b13</citedby><cites>FETCH-LOGICAL-c387t-e958db2a74291ff6c687efd46f684e2e2615a6ad923b5f11e7b652480bf0e8b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26192380$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liss, Michael A</creatorcontrib><creatorcontrib>DeConde, Robert</creatorcontrib><creatorcontrib>Caovan, Dominique</creatorcontrib><creatorcontrib>Hofler, Joseph</creatorcontrib><creatorcontrib>Gabe, Michael</creatorcontrib><creatorcontrib>Palazzi, Kerrin L</creatorcontrib><creatorcontrib>Patel, Nishant D</creatorcontrib><creatorcontrib>Lee, Hak J</creatorcontrib><creatorcontrib>Ideker, Trey</creatorcontrib><creatorcontrib>Van Poppel, Hendrik</creatorcontrib><creatorcontrib>Karow, David</creatorcontrib><creatorcontrib>Aertsen, Michael</creatorcontrib><creatorcontrib>Casola, Giovanna</creatorcontrib><creatorcontrib>Derweesh, Ithaar H</creatorcontrib><title>Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy</title><title>Journal of endourology</title><addtitle>J Endourol</addtitle><description>To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m(2)) 6 months from extirpative renal surgery (nephron-sparing surgery [NSS] or radical nephrectomy [RN]).
Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC).
We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89.
Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.</description><subject>Aged</subject><subject>Carcinoma, Renal Cell - diagnostic imaging</subject><subject>Carcinoma, Renal Cell - pathology</subject><subject>Carcinoma, Renal Cell - surgery</subject><subject>Decision Support Techniques</subject><subject>Female</subject><subject>General Research</subject><subject>Glomerular Filtration Rate</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Kidney - diagnostic imaging</subject><subject>Kidney - surgery</subject><subject>Kidney Neoplasms - diagnostic imaging</subject><subject>Kidney Neoplasms - pathology</subject><subject>Kidney Neoplasms - surgery</subject><subject>Linear Models</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Nephrectomy - methods</subject><subject>Nephrons</subject><subject>Organ Size</subject><subject>Organ Sparing Treatments - methods</subject><subject>Postoperative Complications - epidemiology</subject><subject>Radiography</subject><subject>Renal Insufficiency, Chronic - epidemiology</subject><subject>Retrospective Studies</subject><subject>Tumor Burden</subject><issn>0892-7790</issn><issn>1557-900X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkU9vFCEYh4nR2LV69Go4epktzB9gLiZ1tbZJU5u2Gm-EYV52MQOswNTsJ_Hrlm1roycS3oeH95cfQm8pWVIi-iPw47ImtFuSltJnaEG7jlc9IT-eo0WZ1xXnPTlAr1L6SQhtGG1eooOa0b5uBFmgP5cqgtebnVMT_h6m2UGOVuPjlCAlBz5jlbDClxFGq7O9BXwTwoRzwJ8gQ3TWA74CX16fzL4AweOP4MHYjIPBF7DdxOCr662K1q_x9RzXEHd4FVy5gRH_tnmDr1RxF8M9DToHt3uNXhg1JXjzeB6ibyefb1an1fnXL2er4_NKN4LnCvpOjEOteFv31BimmeBgxpYZJlqooQTtFFNjSTt0hlLgA-vqVpDBEBADbQ7Rhwfvdh4cjLoEjmqS22idijsZlJX_T7zdyHW4la1oKSFNEbx_FMTwa4aUpbNJwzQpD2FOknJGBKcd4wWtHlAdQ0oRzNM3lMh9mbKUKfdlyn2ZhX_3725P9N_2mjueG58V</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Liss, Michael A</creator><creator>DeConde, Robert</creator><creator>Caovan, Dominique</creator><creator>Hofler, Joseph</creator><creator>Gabe, Michael</creator><creator>Palazzi, Kerrin L</creator><creator>Patel, Nishant D</creator><creator>Lee, Hak J</creator><creator>Ideker, Trey</creator><creator>Van Poppel, Hendrik</creator><creator>Karow, David</creator><creator>Aertsen, Michael</creator><creator>Casola, Giovanna</creator><creator>Derweesh, Ithaar H</creator><general>Mary Ann Liebert, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201601</creationdate><title>Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy</title><author>Liss, Michael A ; DeConde, Robert ; Caovan, Dominique ; Hofler, Joseph ; Gabe, Michael ; Palazzi, Kerrin L ; Patel, Nishant D ; Lee, Hak J ; Ideker, Trey ; Van Poppel, Hendrik ; Karow, David ; Aertsen, Michael ; Casola, Giovanna ; Derweesh, Ithaar H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-e958db2a74291ff6c687efd46f684e2e2615a6ad923b5f11e7b652480bf0e8b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Aged</topic><topic>Carcinoma, Renal Cell - diagnostic imaging</topic><topic>Carcinoma, Renal Cell - pathology</topic><topic>Carcinoma, Renal Cell - surgery</topic><topic>Decision Support Techniques</topic><topic>Female</topic><topic>General Research</topic><topic>Glomerular Filtration Rate</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Kidney - diagnostic imaging</topic><topic>Kidney - surgery</topic><topic>Kidney Neoplasms - diagnostic imaging</topic><topic>Kidney Neoplasms - pathology</topic><topic>Kidney Neoplasms - surgery</topic><topic>Linear Models</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Nephrectomy - methods</topic><topic>Nephrons</topic><topic>Organ Size</topic><topic>Organ Sparing Treatments - methods</topic><topic>Postoperative Complications - epidemiology</topic><topic>Radiography</topic><topic>Renal Insufficiency, Chronic - epidemiology</topic><topic>Retrospective Studies</topic><topic>Tumor Burden</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liss, Michael A</creatorcontrib><creatorcontrib>DeConde, Robert</creatorcontrib><creatorcontrib>Caovan, Dominique</creatorcontrib><creatorcontrib>Hofler, Joseph</creatorcontrib><creatorcontrib>Gabe, Michael</creatorcontrib><creatorcontrib>Palazzi, Kerrin L</creatorcontrib><creatorcontrib>Patel, Nishant D</creatorcontrib><creatorcontrib>Lee, Hak J</creatorcontrib><creatorcontrib>Ideker, Trey</creatorcontrib><creatorcontrib>Van Poppel, Hendrik</creatorcontrib><creatorcontrib>Karow, David</creatorcontrib><creatorcontrib>Aertsen, Michael</creatorcontrib><creatorcontrib>Casola, Giovanna</creatorcontrib><creatorcontrib>Derweesh, Ithaar H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of endourology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liss, Michael A</au><au>DeConde, Robert</au><au>Caovan, Dominique</au><au>Hofler, Joseph</au><au>Gabe, Michael</au><au>Palazzi, Kerrin L</au><au>Patel, Nishant D</au><au>Lee, Hak J</au><au>Ideker, Trey</au><au>Van Poppel, Hendrik</au><au>Karow, David</au><au>Aertsen, Michael</au><au>Casola, Giovanna</au><au>Derweesh, Ithaar H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy</atitle><jtitle>Journal of endourology</jtitle><addtitle>J Endourol</addtitle><date>2016-01</date><risdate>2016</risdate><volume>30</volume><issue>1</issue><spage>114</spage><epage>121</epage><pages>114-121</pages><issn>0892-7790</issn><eissn>1557-900X</eissn><abstract>To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m(2)) 6 months from extirpative renal surgery (nephron-sparing surgery [NSS] or radical nephrectomy [RN]).
Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC).
We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89.
Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.</abstract><cop>United States</cop><pub>Mary Ann Liebert, Inc</pub><pmid>26192380</pmid><doi>10.1089/end.2015.0411</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Aged Carcinoma, Renal Cell - diagnostic imaging Carcinoma, Renal Cell - pathology Carcinoma, Renal Cell - surgery Decision Support Techniques Female General Research Glomerular Filtration Rate Humans Image Processing, Computer-Assisted Kidney - diagnostic imaging Kidney - surgery Kidney Neoplasms - diagnostic imaging Kidney Neoplasms - pathology Kidney Neoplasms - surgery Linear Models Male Middle Aged Nephrectomy - methods Nephrons Organ Size Organ Sparing Treatments - methods Postoperative Complications - epidemiology Radiography Renal Insufficiency, Chronic - epidemiology Retrospective Studies Tumor Burden |
| title | Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy |
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