Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT)
Background and purpose The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomograp...
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| Veröffentlicht in: | Clinical and experimental nephrology 2015-10, Vol.19 (5), p.974-981 |
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| creator | Isotani, Shuji Shimoyama, Hirofumi Yokota, Isao Noma, Yasuhiro Kitamura, Kousuke China, Toshiyuki Saito, Keisuke Hisasue, Shin-ichi Ide, Hisamitsu Muto, Satoru Yamaguchi, Raizo Ukimura, Osamu Gill, Inderbir S. Horie, Shigeo |
| description | Background and purpose
The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model.
Patients and methods
Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models.
Results
The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (
p
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| doi_str_mv | 10.1007/s10157-015-1082-6 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1768572012</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1722422726</sourcerecordid><originalsourceid>FETCH-LOGICAL-c554t-d163e287c39369bcaa9bfddedc99ffb2805dcf0c264b2e1e3106d28a12a8cf413</originalsourceid><addsrcrecordid>eNqNkctu1TAQhiMEoqXwAGyQJTZlEfA4F9tLdLhKBTZlHTn2uCdVEgdfTnXegwfGIQeEkJDYjEfjbz5L_oviKdCXQCl_FYBCw8tcSqCCle294hzqipecS3k_91XNSuANnBWPQrillArZyIfFGWtaELWszovvn90BR7J4NIOOg5vJ5EweOEs8zmokNs3bXNmInsy47D3q6KYjsd5NRKXcq4jmxB_cmCaM_kjuhrhfxW5Br-JwQDKlMQ4G47rviXbTktbFLHA3Xi37I7n89GZ3_eJx8cCqMeCT03lRfH339nr3obz68v7j7vVVqZumjqWBtkImuK5k1cpeKyV7awwaLaW1PRO0MdpSzdq6ZwhYAW0NEwqYEtrWUF0Ul5t38e5bwhC7aQgax1HN6FLogLei4YwC-w-UsZoxztqMPv8LvXXJ57_5SYHgEkSVKdgo7V0IHm23-GFS_tgB7dZ0uy3dLpduTbdbzc9O5tRPaH5v_IozA2wDQr6ab9D_8fQ_rT8Agjmy9Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1721879183</pqid></control><display><type>article</type><title>Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT)</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Isotani, Shuji ; Shimoyama, Hirofumi ; Yokota, Isao ; Noma, Yasuhiro ; Kitamura, Kousuke ; China, Toshiyuki ; Saito, Keisuke ; Hisasue, Shin-ichi ; Ide, Hisamitsu ; Muto, Satoru ; Yamaguchi, Raizo ; Ukimura, Osamu ; Gill, Inderbir S. ; Horie, Shigeo</creator><creatorcontrib>Isotani, Shuji ; Shimoyama, Hirofumi ; Yokota, Isao ; Noma, Yasuhiro ; Kitamura, Kousuke ; China, Toshiyuki ; Saito, Keisuke ; Hisasue, Shin-ichi ; Ide, Hisamitsu ; Muto, Satoru ; Yamaguchi, Raizo ; Ukimura, Osamu ; Gill, Inderbir S. ; Horie, Shigeo</creatorcontrib><description>Background and purpose
The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model.
Patients and methods
Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models.
Results
The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (
p
< 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (
r
= 0.58,
p
< 0.01) and %RPV preservation (
r
= 0.54,
p
< 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 − 0.55(age) − 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (
r
= 0.83;
p
< 0.001). The external validation cohort (
n
= 21) showed our model outperformed previously reported models.
Conclusions
Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.</description><identifier>ISSN: 1342-1751</identifier><identifier>EISSN: 1437-7799</identifier><identifier>DOI: 10.1007/s10157-015-1082-6</identifier><identifier>PMID: 25618493</identifier><identifier>CODEN: CENPFV</identifier><language>eng</language><publisher>Tokyo: Springer Japan</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Automation ; Cohort Studies ; Female ; Glomerular Filtration Rate ; Humans ; Kidney - pathology ; Kidney Function Tests ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Multidetector Computed Tomography - methods ; Nephrectomy ; Nephrology ; Organ Size ; Original Article ; Postoperative Period ; Predictive Value of Tests ; Urology</subject><ispartof>Clinical and experimental nephrology, 2015-10, Vol.19 (5), p.974-981</ispartof><rights>Japanese Society of Nephrology 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c554t-d163e287c39369bcaa9bfddedc99ffb2805dcf0c264b2e1e3106d28a12a8cf413</citedby><cites>FETCH-LOGICAL-c554t-d163e287c39369bcaa9bfddedc99ffb2805dcf0c264b2e1e3106d28a12a8cf413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10157-015-1082-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10157-015-1082-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25618493$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Isotani, Shuji</creatorcontrib><creatorcontrib>Shimoyama, Hirofumi</creatorcontrib><creatorcontrib>Yokota, Isao</creatorcontrib><creatorcontrib>Noma, Yasuhiro</creatorcontrib><creatorcontrib>Kitamura, Kousuke</creatorcontrib><creatorcontrib>China, Toshiyuki</creatorcontrib><creatorcontrib>Saito, Keisuke</creatorcontrib><creatorcontrib>Hisasue, Shin-ichi</creatorcontrib><creatorcontrib>Ide, Hisamitsu</creatorcontrib><creatorcontrib>Muto, Satoru</creatorcontrib><creatorcontrib>Yamaguchi, Raizo</creatorcontrib><creatorcontrib>Ukimura, Osamu</creatorcontrib><creatorcontrib>Gill, Inderbir S.</creatorcontrib><creatorcontrib>Horie, Shigeo</creatorcontrib><title>Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT)</title><title>Clinical and experimental nephrology</title><addtitle>Clin Exp Nephrol</addtitle><addtitle>Clin Exp Nephrol</addtitle><description>Background and purpose
The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model.
Patients and methods
Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models.
Results
The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (
p
< 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (
r
= 0.58,
p
< 0.01) and %RPV preservation (
r
= 0.54,
p
< 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 − 0.55(age) − 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (
r
= 0.83;
p
< 0.001). The external validation cohort (
n
= 21) showed our model outperformed previously reported models.
Conclusions
Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Automation</subject><subject>Cohort Studies</subject><subject>Female</subject><subject>Glomerular Filtration Rate</subject><subject>Humans</subject><subject>Kidney - pathology</subject><subject>Kidney Function Tests</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multidetector Computed Tomography - methods</subject><subject>Nephrectomy</subject><subject>Nephrology</subject><subject>Organ Size</subject><subject>Original Article</subject><subject>Postoperative Period</subject><subject>Predictive Value of Tests</subject><subject>Urology</subject><issn>1342-1751</issn><issn>1437-7799</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNqNkctu1TAQhiMEoqXwAGyQJTZlEfA4F9tLdLhKBTZlHTn2uCdVEgdfTnXegwfGIQeEkJDYjEfjbz5L_oviKdCXQCl_FYBCw8tcSqCCle294hzqipecS3k_91XNSuANnBWPQrillArZyIfFGWtaELWszovvn90BR7J4NIOOg5vJ5EweOEs8zmokNs3bXNmInsy47D3q6KYjsd5NRKXcq4jmxB_cmCaM_kjuhrhfxW5Br-JwQDKlMQ4G47rviXbTktbFLHA3Xi37I7n89GZ3_eJx8cCqMeCT03lRfH339nr3obz68v7j7vVVqZumjqWBtkImuK5k1cpeKyV7awwaLaW1PRO0MdpSzdq6ZwhYAW0NEwqYEtrWUF0Ul5t38e5bwhC7aQgax1HN6FLogLei4YwC-w-UsZoxztqMPv8LvXXJ57_5SYHgEkSVKdgo7V0IHm23-GFS_tgB7dZ0uy3dLpduTbdbzc9O5tRPaH5v_IozA2wDQr6ab9D_8fQ_rT8Agjmy9Q</recordid><startdate>20151001</startdate><enddate>20151001</enddate><creator>Isotani, Shuji</creator><creator>Shimoyama, Hirofumi</creator><creator>Yokota, Isao</creator><creator>Noma, Yasuhiro</creator><creator>Kitamura, Kousuke</creator><creator>China, Toshiyuki</creator><creator>Saito, Keisuke</creator><creator>Hisasue, Shin-ichi</creator><creator>Ide, Hisamitsu</creator><creator>Muto, Satoru</creator><creator>Yamaguchi, Raizo</creator><creator>Ukimura, Osamu</creator><creator>Gill, Inderbir S.</creator><creator>Horie, Shigeo</creator><general>Springer Japan</general><general>Springer Nature B.V</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>3V.</scope><scope>7QP</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20151001</creationdate><title>Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT)</title><author>Isotani, Shuji ; Shimoyama, Hirofumi ; Yokota, Isao ; Noma, Yasuhiro ; Kitamura, Kousuke ; China, Toshiyuki ; Saito, Keisuke ; Hisasue, Shin-ichi ; Ide, Hisamitsu ; Muto, Satoru ; Yamaguchi, Raizo ; Ukimura, Osamu ; Gill, Inderbir S. ; Horie, Shigeo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c554t-d163e287c39369bcaa9bfddedc99ffb2805dcf0c264b2e1e3106d28a12a8cf413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Automation</topic><topic>Cohort Studies</topic><topic>Female</topic><topic>Glomerular Filtration Rate</topic><topic>Humans</topic><topic>Kidney - pathology</topic><topic>Kidney Function Tests</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multidetector Computed Tomography - methods</topic><topic>Nephrectomy</topic><topic>Nephrology</topic><topic>Organ Size</topic><topic>Original Article</topic><topic>Postoperative Period</topic><topic>Predictive Value of Tests</topic><topic>Urology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Isotani, Shuji</creatorcontrib><creatorcontrib>Shimoyama, Hirofumi</creatorcontrib><creatorcontrib>Yokota, Isao</creatorcontrib><creatorcontrib>Noma, Yasuhiro</creatorcontrib><creatorcontrib>Kitamura, Kousuke</creatorcontrib><creatorcontrib>China, Toshiyuki</creatorcontrib><creatorcontrib>Saito, Keisuke</creatorcontrib><creatorcontrib>Hisasue, Shin-ichi</creatorcontrib><creatorcontrib>Ide, Hisamitsu</creatorcontrib><creatorcontrib>Muto, Satoru</creatorcontrib><creatorcontrib>Yamaguchi, Raizo</creatorcontrib><creatorcontrib>Ukimura, Osamu</creatorcontrib><creatorcontrib>Gill, Inderbir S.</creatorcontrib><creatorcontrib>Horie, Shigeo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical 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>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Clinical and experimental nephrology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Isotani, Shuji</au><au>Shimoyama, Hirofumi</au><au>Yokota, Isao</au><au>Noma, Yasuhiro</au><au>Kitamura, Kousuke</au><au>China, Toshiyuki</au><au>Saito, Keisuke</au><au>Hisasue, Shin-ichi</au><au>Ide, Hisamitsu</au><au>Muto, Satoru</au><au>Yamaguchi, Raizo</au><au>Ukimura, Osamu</au><au>Gill, Inderbir S.</au><au>Horie, Shigeo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT)</atitle><jtitle>Clinical and experimental nephrology</jtitle><stitle>Clin Exp Nephrol</stitle><addtitle>Clin Exp Nephrol</addtitle><date>2015-10-01</date><risdate>2015</risdate><volume>19</volume><issue>5</issue><spage>974</spage><epage>981</epage><pages>974-981</pages><issn>1342-1751</issn><eissn>1437-7799</eissn><coden>CENPFV</coden><abstract>Background and purpose
The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model.
Patients and methods
Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models.
Results
The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (
p
< 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (
r
= 0.58,
p
< 0.01) and %RPV preservation (
r
= 0.54,
p
< 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 − 0.55(age) − 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (
r
= 0.83;
p
< 0.001). The external validation cohort (
n
= 21) showed our model outperformed previously reported models.
Conclusions
Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.</abstract><cop>Tokyo</cop><pub>Springer Japan</pub><pmid>25618493</pmid><doi>10.1007/s10157-015-1082-6</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1342-1751 |
| ispartof | Clinical and experimental nephrology, 2015-10, Vol.19 (5), p.974-981 |
| issn | 1342-1751 1437-7799 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1768572012 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Adult Aged Aged, 80 and over Automation Cohort Studies Female Glomerular Filtration Rate Humans Kidney - pathology Kidney Function Tests Male Medicine Medicine & Public Health Middle Aged Multidetector Computed Tomography - methods Nephrectomy Nephrology Organ Size Original Article Postoperative Period Predictive Value of Tests Urology |
| title | Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT) |
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