Using a 3% Proton Density Fat Fraction as a Cut-Off Value Increases Sensitivity of Detection of Hepatic Steatosis, Based on Results From Histopathology Analysis
It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to d...
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Published in | Gastroenterology Vol. 153; no. 1; pp. 53 - 55.e7 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
01.07.2017
Elsevier BV |
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Abstract | It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of 1H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic, 1H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the 1H-MRS PDFF findings with SPCs (r = 0.92; P < .001). 1H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis. |
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AbstractList | It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (
H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of
H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic,
H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the
H-MRS PDFF findings with SPCs (r = 0.92; P < .001).
H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis. It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of 1H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic, 1H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the 1H-MRS PDFF findings with SPCs (r = 0.92; P < .001). 1H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis.It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of 1H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic, 1H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the 1H-MRS PDFF findings with SPCs (r = 0.92; P < .001). 1H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis. It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of 1H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic, 1H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the 1H-MRS PDFF findings with SPCs (r = 0.92; P < .001). 1H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis. It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (less thansuperscriptgreater than1less than/superscriptgreater thanH-MRS), instead of collecting and analyzing liver biopsies to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of less thansuperscriptgreater than1less than/superscriptgreater thanH-MRS PDFF in measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic, less thansuperscriptgreater than1less than/superscriptgreater thanH-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (6 months or more) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereological point counts (SPCs). We correlated less thansuperscriptgreater than1less than/superscriptgreater thanH-MRS PDFF findings with SPCs (r = 0.92; P less than.001). less thansuperscriptgreater than1less than/superscriptgreater thanH-MRS PDFF results correlated with histopathology results (ρ = 0.87; P less than.001), and SPCs correlated with histopathology results (ρ = 0.88; P less than.001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values below 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve non-invasive detection of steatosis. It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1 H-MRS), instead of collecting and analyzing liver biopsy specimens to detect steatosis. However, the current PDFF cut-off value (5%) used to define steatosis by magnetic resonance was derived from studies that did not use histopathology as the reference standard. We performed a prospective study to determine the accuracy of1 H-MRS PDFF in the measurement of steatosis using histopathology analysis as the standard. We collected clinical, serologic,1 H-MRS PDFF, and liver biopsy data from 94 adult patients with increased levels of liver enzymes (≥6 mo) referred to the Department of Gastroenterology and Hepatology at Linköping University Hospital in Sweden from 2007 through 2014. Steatosis was graded using the conventional histopathology method and fat content was quantified in biopsy samples using stereologic point counts (SPCs). We correlated the1 H-MRS PDFF findings with SPCs (r = 0.92; P < .001).1 H-MRS PDFF results correlated with histopathology results (ρ = 0.87; P < .001), and SPCs correlated with histopathology results (ρ = 0.88; P < .001). All 25 subjects with PDFF values of 5.0% or more had steatosis based on histopathology findings (100% specificity for PDFF). However, of 69 subjects with PDFF values less than 5.0% (negative result), 22 were determined to have steatosis based on histopathology findings (53% sensitivity for PDFF). Reducing the PDFF cut-off value to 3.0% identified patients with steatosis with 100% specificity and 79% sensitivity; a PDFF cut-off value of 2.0% identified patients with steatosis with 94% specificity and 87% sensitivity. These findings might be used to improve noninvasive detection of steatosis. |
Author | Ignatova, Simone Ekstedt, Mattias Kechagias, Stergios Forsgren, Mikael F. Norén, Bengt Nasr, Patrik Dahlström, Nils Leinhard, Olof Dahlqvist Cedersund, Gunnar Lundberg, Peter |
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Cites_doi | 10.1016/j.jhep.2015.11.004 10.1002/jmri.21809 10.1002/jmri.22580 10.1152/ajpendo.00064.2004 10.1016/0730-725X(94)92543-7 10.1002/nbm.1622 10.1001/archinte.158.16.1789 10.1148/radiol.12120896 10.1002/hep.20701 10.1007/s00330-015-3724-1 10.1007/BF02668096 10.1002/hep.28012 10.1053/j.gastro.2005.03.084 10.1148/radiol.14140754 10.1038/modpathol.3800370 10.1002/hep.27368 10.1002/jmri.22583 10.1097/SLA.0b013e3181bcd6dd 10.1053/j.gastro.2015.04.043 10.1016/j.ejrad.2007.06.004 10.1006/jmre.1997.1244 10.1002/jmri.1880050311 10.1002/jmri.23741 |
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Keywords | 1H-MRS PDFF NAFLD Diagnostic Tests MRI Nonalcoholic Fatty Liver Disease SPC NASH magnetic resonance imaging stereologic point counting proton density fat fraction proton magnetic resonance spectroscopy |
Language | English |
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References | Franzén (bib2) 2005; 18 Szczepaniak (bib5) 2005; 288 Tang (bib7) 2015; 274 Bannas, Kramer (bib4) 2015; 62 Ekstedt, Hagström (bib9) 2015; 61 Angulo (bib10) 2015; 149 Kleiner (bib1) 2005; 41 Tang (bib6) 2013; 267 (bib8) 2016; 64 Reeder (bib3) 2011; 34 Rehm (bib11) 2015; 25 Kleiner (10.1053/j.gastro.2017.03.005_bib1) 2005; 41 Hamilton (10.1053/j.gastro.2017.03.005_bib27) 2009; 30 Vanhamme (10.1053/j.gastro.2017.03.005_bib15) 1997; 129 Bush (10.1053/j.gastro.2017.03.005_bib12) 1998; 158 10.1053/j.gastro.2017.03.005_bib13 Ratziu (10.1053/j.gastro.2017.03.005_bib28) 2005; 128 Franzén (10.1053/j.gastro.2017.03.005_bib2) 2005; 18 Angulo (10.1053/j.gastro.2017.03.005_bib10) 2015; 149 Rehm (10.1053/j.gastro.2017.03.005_bib11) 2015; 25 El-Badry (10.1053/j.gastro.2017.03.005_bib24) 2009; 250 Reeder (10.1053/j.gastro.2017.03.005_bib3) 2011; 34 Szczepaniak (10.1053/j.gastro.2017.03.005_bib5) 2005; 288 Ekstedt (10.1053/j.gastro.2017.03.005_bib9) 2015; 61 Longo (10.1053/j.gastro.2017.03.005_bib19) 1995; 5 Bannas (10.1053/j.gastro.2017.03.005_bib4) 2015; 62 Thomsen (10.1053/j.gastro.2017.03.005_bib17) 1994; 12 Kleiner (10.1053/j.gastro.2017.03.005_bib20) 2005; 41 (10.1053/j.gastro.2017.03.005_bib8) 2016; 64 Turlin (10.1053/j.gastro.2017.03.005_bib22) 1998; 15 Tang (10.1053/j.gastro.2017.03.005_bib7) 2015; 274 Franzén (10.1053/j.gastro.2017.03.005_bib21) 2005; 18 Hamilton (10.1053/j.gastro.2017.03.005_bib16) 2011; 24 Norén (10.1053/j.gastro.2017.03.005_bib23) 2008; 66 Tang (10.1053/j.gastro.2017.03.005_bib6) 2013; 267 Lee (10.1053/j.gastro.2017.03.005_bib18) 2011; 33 Bannas (10.1053/j.gastro.2017.03.005_bib26) 2015; 62 Naressi (10.1053/j.gastro.2017.03.005_bib14) 2001; 12 Reeder (10.1053/j.gastro.2017.03.005_bib25) 2012; 36 |
References_xml | – volume: 62 start-page: 1444 year: 2015 end-page: 1455 ident: bib4 publication-title: Hepatology – volume: 25 start-page: 2921 year: 2015 end-page: 2930 ident: bib11 publication-title: Eur Radiol – volume: 274 start-page: 416 year: 2015 end-page: 425 ident: bib7 publication-title: Radiology – volume: 288 start-page: E462 year: 2005 end-page: E468 ident: bib5 publication-title: Am J Physiol Endocrinol Metab – volume: 18 start-page: 912 year: 2005 end-page: 916 ident: bib2 publication-title: Mod Pathol – volume: 267 start-page: 422 year: 2013 end-page: 431 ident: bib6 publication-title: Radiology – volume: 149 start-page: 389 year: 2015 end-page: 397 ident: bib10 publication-title: Gastroenterology – volume: 41 start-page: 1313 year: 2005 end-page: 1321 ident: bib1 publication-title: Hepatology – volume: 61 start-page: 1547 year: 2015 end-page: 1554 ident: bib9 publication-title: Hepatology – volume: 34 start-page: 729 year: 2011 end-page: 749 ident: bib3 publication-title: J Magn Reson Imaging – volume: 64 start-page: 1388 year: 2016 end-page: 1402 ident: bib8 publication-title: J Hepatol – volume: 64 start-page: 1388 year: 2016 ident: 10.1053/j.gastro.2017.03.005_bib8 publication-title: J Hepatol doi: 10.1016/j.jhep.2015.11.004 – volume: 30 start-page: 145 year: 2009 ident: 10.1053/j.gastro.2017.03.005_bib27 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.21809 – volume: 34 start-page: 729 year: 2011 ident: 10.1053/j.gastro.2017.03.005_bib3 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.22580 – volume: 288 start-page: E462 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib5 publication-title: Am J Physiol Endocrinol Metab doi: 10.1152/ajpendo.00064.2004 – volume: 12 start-page: 487 year: 1994 ident: 10.1053/j.gastro.2017.03.005_bib17 publication-title: Magn Reson Imaging doi: 10.1016/0730-725X(94)92543-7 – volume: 15 start-page: 237 year: 1998 ident: 10.1053/j.gastro.2017.03.005_bib22 publication-title: Semin Diagn Pathol – volume: 24 start-page: 784 year: 2011 ident: 10.1053/j.gastro.2017.03.005_bib16 publication-title: NMR Biomed doi: 10.1002/nbm.1622 – volume: 158 start-page: 1789 year: 1998 ident: 10.1053/j.gastro.2017.03.005_bib12 publication-title: Arch Intern Med doi: 10.1001/archinte.158.16.1789 – volume: 267 start-page: 422 year: 2013 ident: 10.1053/j.gastro.2017.03.005_bib6 publication-title: Radiology doi: 10.1148/radiol.12120896 – volume: 41 start-page: 1313 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib1 publication-title: Hepatology doi: 10.1002/hep.20701 – volume: 25 start-page: 2921 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib11 publication-title: Eur Radiol doi: 10.1007/s00330-015-3724-1 – ident: 10.1053/j.gastro.2017.03.005_bib13 – volume: 41 start-page: 1313 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib20 publication-title: Hepatology doi: 10.1002/hep.20701 – volume: 12 start-page: 141 year: 2001 ident: 10.1053/j.gastro.2017.03.005_bib14 publication-title: MAGMA doi: 10.1007/BF02668096 – volume: 62 start-page: 1444 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib26 publication-title: Hepatology doi: 10.1002/hep.28012 – volume: 128 start-page: 1898 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib28 publication-title: Gastroenterology doi: 10.1053/j.gastro.2005.03.084 – volume: 274 start-page: 416 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib7 publication-title: Radiology doi: 10.1148/radiol.14140754 – volume: 18 start-page: 912 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib21 publication-title: Mod Pathol doi: 10.1038/modpathol.3800370 – volume: 61 start-page: 1547 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib9 publication-title: Hepatology doi: 10.1002/hep.27368 – volume: 33 start-page: 1390 year: 2011 ident: 10.1053/j.gastro.2017.03.005_bib18 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.22583 – volume: 250 start-page: 691 year: 2009 ident: 10.1053/j.gastro.2017.03.005_bib24 publication-title: Ann Surg doi: 10.1097/SLA.0b013e3181bcd6dd – volume: 149 start-page: 389 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib10 publication-title: Gastroenterology doi: 10.1053/j.gastro.2015.04.043 – volume: 66 start-page: 313 year: 2008 ident: 10.1053/j.gastro.2017.03.005_bib23 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2007.06.004 – volume: 129 start-page: 35 year: 1997 ident: 10.1053/j.gastro.2017.03.005_bib15 publication-title: J Magn Reson doi: 10.1006/jmre.1997.1244 – volume: 5 start-page: 281 year: 1995 ident: 10.1053/j.gastro.2017.03.005_bib19 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.1880050311 – volume: 36 start-page: 1011 year: 2012 ident: 10.1053/j.gastro.2017.03.005_bib25 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.23741 – volume: 18 start-page: 912 year: 2005 ident: 10.1053/j.gastro.2017.03.005_bib2 publication-title: Mod Pathol doi: 10.1038/modpathol.3800370 – volume: 62 start-page: 1444 year: 2015 ident: 10.1053/j.gastro.2017.03.005_bib4 publication-title: Hepatology doi: 10.1002/hep.28012 |
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Snippet | It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy... It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (1... It is possible to estimate hepatic triglyceride content by calculating the proton density fat fraction (PDFF), using proton magnetic resonance spectroscopy (... |
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SubjectTerms | Adiposity Adult Aged Biopsy Diagnostic Tests Female Gastroenterology and Hepatology Humans Liver Liver - pathology Magnetic Resonance Spectroscopy Male Middle Aged NASH Non-alcoholic Fatty Liver Disease Non-alcoholic Fatty Liver Disease - diagnosis Non-alcoholic Fatty Liver Disease - pathology Nonalcoholic Fatty Liver Disease Prospective Studies Reference Values Sensitivity and Specificity Triglycerides Triglycerides - analysis |
Title | Using a 3% Proton Density Fat Fraction as a Cut-Off Value Increases Sensitivity of Detection of Hepatic Steatosis, Based on Results From Histopathology Analysis |
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