Predicted probability of end stage renal failure at 5 years for 4 Hypothetical Patient Profiles based on Model 2 and the 4-variable Kidney Failure Risk Equation.

An independent validation of the kidney failure risk equation in an Asian population

Kang, Min Woo
Tangri, Navdeep
Kim, Yong Chul
An, Jung Nam
Lee, Jeonghwan
Li, Lilin
Oh, Yun Kyu
Kim, Dong Ki
Joo, Kwon Wook
Kim, Yon Su
Lim, Chun Soo
Lee, Jung Pyo

July 2020

Predicting the risk of end-stage renal disease (ESRD) progression facilitates appropriate nephrology...

A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests.

Zacharias, Helena U
Altenbuchinger, Michael
Schultheiss, Ulla T
Raffler, Johannes
Kotsis, Fruzsina
Ghasemi, Sahar
Ali, Ibrahim
Kollerits, Barbara
Metzger, Marie
Steinbrenner, Inga
Sekula, Peggy
Massy, Ziad A
Combe, Christian
Kalra, Philip A
Kronenberg, Florian
Stengel, Bénédicte
Eckardt, Kai-Uwe
Köttgen, Anna
Schmid, Matthias
Gronwald, Wolfram
Oefner, Peter J

July 2021

International audienceStratification of chronic kidney disease (CKD) patients at risk for progressin...

Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

Min-Jeong Lee (1539964)
Joo-Han Park (5819240)
Yeo Rae Moon (5819243)
Soo-Yeon Jo (5819246)
Dukyong Yoon (469192)
Rae Woong Park (469195)
Jong Cheol Jeong (1372383)
Inwhee Park (469193)
Gyu-Tae Shin (5819249)
Heungsoo Kim (2515300)

October 2018

PurposeWe aimed to develop a model of chronic kidney disease (CKD) progression for predicting the pr...

Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD

Chiava L Ramspek
Marie Evans
Christoph Wanner Christiane Drechsler
Nicholas C Chesnaye
Maciel Szymczaka
Kitty J Jagger
Merel Van Diepen
EQUAL Study Investigators
Antonello Pani
Gianfranca Cabiddu

January 2021

Background Various prediction models have been developed to predict the risk of kidney failure in pa...

Calibration between predicted and observed risk of progression to end-stage kidney disease at five years follow-up in the NephroTest cohort.

Jan A. J. G. van den Brand (3607550)
Tjeerd M. H. Dijkstra (173108)
Jack Wetzels (4542142)
Bénédicte Stengel (2818411)
Marie Metzger (156894)
Peter J. Blankestijn (151363)
Hiddo J. Lambers Heerspink (284477)
Ron T. Gansevoort (111233)

May 2019

The black line indicates the average calibration line, the shaded area is the 95% confidence interva...

Kidney failure prediction models: A comprehensive external validation study in patients with advanced CKD

Ramspek C. L.
Evans M.
Wanner C.
Drechsler C.
Chesnaye N. C.
Szymczak M.
Krajewska M.
Torino C.
Porto G.
Hayward S.
Caskey F.
Dekker F. W.
Jager K. J.
van Diepen M.
Cupisti A.
Sagliocca A.
Ferraro A.
Musiala A.
Mele A.
Naticchia A.
Cosaro A.
Woodman A.
Ranghino A.
Stucchi A.
Jonsson A.
Schneider A.
Pignataro A.
Schrander A.
Torp A.
McKeever A.
Szymczak A.
Blom A. -L.
de Blasio A.
Pani A.
Tsalouichos A.
Ullah A.
McLaren B.
van Dam B.
Iwig B.
Antonio B.
Di Iorio B. R.
Rogland B.
Perras B.
Alessandra B.
Harron C.
Wallquist C.
Siegert C.
Barrett C.
Gaillard C.
Garofalo C.
Abaterusso C.
Beerenhout C.
O'Toole C.
Somma C.
Marx C.
Summersgill C.
Blaser C.
D'alessandro C.
Emde C.
Zullo C.
Pozzi C.
Geddes C.
Verburgh C.
Bergamo D.
Ciurlino D.
Motta D.
Glowski D.
McGlynn D.
Vargas D.
Krieter D.
Russo D.
Fuchs D.
Sands D.
Hoogeveen E.
Irmler E.
Dimeny E.
Favaro E.
Platen E.
Olczyk E.
Hoorn E.
Vigotti F.
Ansali F.
Conte F.
Cianciotta F.
Giacchino F.
Cappellaio F.
Pizzarelli F.
Sundelin F.
Uhlin F.
Greco G.
Roy G.
Porto G.
Bigatti G.
Marinangeli G.
Cabiddu G.
Hirst G.
Fumagalli G.
Caloro G.
Piccoli G.
Capasso G.
Gambaro G.
Tognarelli G.
Bonforte G.
Conte G.
Toscano G.
Del Rosso G.
Welander G.
Augustyniak-Bartosik H.
Boots H.
Schmidt-Gurtler H.
King H.
McNally H.
Schlee H.
Boom H.
Naujoks H.
Masri-Senghor H.
Murtagh H.
Rayner H.
Miskowiec-Wisniewska I.
Schlee I.
Capizzi I.
Hernandez I. B.
Baragetti I.
Manitius J.
Turner J.
Eijgenraam J. -W.
Kooman J.
Beige J.
Pondel J.
Wilcox J.
Berdeprado J.
Rothele J.
Wong J.
Rotmans J.
Banda J.
Mazur J.
Hahn K.
Jedrzejak K.
Nowanska K.
Blouin K.
Neumeier K.
Jones K.
Anding-Rost K.
Grontoft K. -C.
Oldrizzi L.
Haydock L.
Vogt L.
Wilkinson L.
Gesualdo L.
Schramm L.
Biancone L.
Nowak L.
Raasveld M.
Durlik M.
Magnano M.
Vervloet M.
Ricardi M.
Carmody M.
Di Bari M.
Laudato M.
Sirico M. L.
Stendahl M.
Svensson M.
Weetman M.
van Buren M.
Joinson M.
Ferraresi M.
Dutton M.
van Diepen M.
Matthews M.
Provenzano M.
Hopf M.
Malaguti M.
Wuttke N.
Morgan N.
Palmieri N.
Frischmuth N.
Bleakley N.
Murrone P.
Cockwell P.
Leurs P.
Roderick P.
Voskamp P.
Kashioulis P.
Ichtiaris P.
Blankestijn P.
Kirste P.
Schulz P.
Mason P.
Kalra P.
Cirillo P.
Dattolo P.
Acampora P.
Sajith R.
Nigro R.
Boero R.
Scarpioni R.
Sicoli R.
Malandra R.
Aign S.
Casar S.
van Esch S.
Chapman S.
Biribauer S.
Navjee S.
Crosbie S.
Brown S.
Tickle S.
Manan S.
Roser S.
Savoldi S.
Bertoli S.
Borrelli S.
Boorsma S.
Heidenreich S.
Melander S.
Maxia S.
Maffei S.
Mangano S.
Palm S.
Konings S.
Mathavakkannan S.
Schwedler S.
Delrieux S.
Renker S.
Schattel S.
Dorota S.
Cicchetti T.
Nieszporek T.
Stephan T.
Schmiedeke T.
Weinreich T.
Leimbach T.
Rappa T.
Almquist T.
Stovesand T.
Bahner U.
Jensen U.
Palazzo V.
de Simone W.
Seeger W.
Kuan Y.
Heleniak Z.
Aydin Z.

January 2021

Background Various prediction models have been developed to predict the risk of kidney failure in pa...

Probabilities of progression to CKD stage 5 in the next t years after inclusion into the cohort for a man with different ages, levels of mGFR and proteinuria at inclusion, estimated from the illness-death model for interval-censored data (IMID, model M4, solid line) and the standard survival model (model M1, dotted line).

Julie Boucquemont (673192)
Marie Metzger (156894)
Christian Combe (162087)
Bénédicte Stengel (2818411)
Karen Leffondre (5661718)

December 2014

<p>Probabilities of progression to CKD stage 5 in the next t years after inclusion into the cohort f...

Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate

Grams, Morgan E.
Sang, Yingying
Ballew, Shoshana H.
Carrero, Juan Jesus
Djurdjev, Ognjenka
Heerspink, Hiddo J. L.
Ho, Kevin
Ito, Sadayoshi
Marks, Angharad
Naimark, David
Nash, Danielle M.
Navaneethan, Sankar D.
Sarnak, Mark
Stengel, Benedicte
Visseren, Frank L. J.
Wang, Angela Yee-Moon
Koettgen, Anna
Levey, Andrew S.
Woodward, Mark
Eckardt, Kai-Uwe
Hemmelgarn, Brenda
Coresh, Josef

June 2018

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at ...

Improving precision in prediction: Using kidney failure risk equations as a potential adjunct to vascular access planning.

Inston, Nicholas
Lok, Charmaine E

January 2019

The timing of referral for creation of vascular access in a patient with declining kidney function i...

Validation of the kidney failure risk equation in European CKD patients

Peeters, M.J.
Zuilen, A.D. van
Brand, A. van den
Bots, M.L.
Blankestijn, P.J.
Wetzels, J.F.M.
Vervoort, G.M.M.
et al.

January 2013

BACKGROUND: Patients with chronic kidney disease (CKD) are at risk for progression to kidney failure...

Predicting Kidney Failure from Longitudinal Kidney Function Trajectory: A Comparison of Models

Brand, dr. A.J.G. van den (Radboud University)

April 2019

Minimal datasets used to train and validate models described in Predicting Kidney Failure from Longi...

Logistic regression models for the prediction of end stage renal disease at two years, with measures of model performance and the incremental value of adding kappa/creatinine ratio (KCR) or lambda/creatinine ratio (LCR) to a baseline model containing the kidney failure risk equation (KFRE).

Anthony Fenton (3343094)
Mark D. Jesky (535502)
Rachel Webster (3249432)
Stephanie J. Stringer (535501)
Punit Yadav (535503)
Iain Chapple (5194430)
Indranil Dasgupta (3343091)
Stephen J. Harding (5194427)
Charles J. Ferro (319168)
Paul Cockwell (345149)

May 2018

<p>Logistic regression models for the prediction of end stage renal disease at two years, with measu...

An independent validation of the kidney failure risk equation in an Asian population

Kang, Min Woo
Tangri, Navdeep
Kim, Yong Chul
An, Jung Nam
Lee, Jeonghwan
Li, Lilin
Oh, Yun Kyu
Kim, Dong Ki
Joo, Kwon Wook
Kim, Yon Su
Lim, Chun Soo
Lee, Jung Pyo

July 2020

Predicting the risk of end-stage renal disease (ESRD) progression facilitates appropriate nephrology...

A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests.

Zacharias, Helena U
Altenbuchinger, Michael
Schultheiss, Ulla T
Raffler, Johannes
Kotsis, Fruzsina
Ghasemi, Sahar
Ali, Ibrahim
Kollerits, Barbara
Metzger, Marie
Steinbrenner, Inga
Sekula, Peggy
Massy, Ziad A
Combe, Christian
Kalra, Philip A
Kronenberg, Florian
Stengel, Bénédicte
Eckardt, Kai-Uwe
Köttgen, Anna
Schmid, Matthias
Gronwald, Wolfram
Oefner, Peter J

July 2021

International audienceStratification of chronic kidney disease (CKD) patients at risk for progressin...

Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

Min-Jeong Lee (1539964)
Joo-Han Park (5819240)
Yeo Rae Moon (5819243)
Soo-Yeon Jo (5819246)
Dukyong Yoon (469192)
Rae Woong Park (469195)
Jong Cheol Jeong (1372383)
Inwhee Park (469193)
Gyu-Tae Shin (5819249)
Heungsoo Kim (2515300)

October 2018

PurposeWe aimed to develop a model of chronic kidney disease (CKD) progression for predicting the pr...

Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD

Chiava L Ramspek
Marie Evans
Christoph Wanner Christiane Drechsler
Nicholas C Chesnaye
Maciel Szymczaka
Kitty J Jagger
Merel Van Diepen
EQUAL Study Investigators
Antonello Pani
Gianfranca Cabiddu

January 2021

Background Various prediction models have been developed to predict the risk of kidney failure in pa...

Calibration between predicted and observed risk of progression to end-stage kidney disease at five years follow-up in the NephroTest cohort.

Jan A. J. G. van den Brand (3607550)
Tjeerd M. H. Dijkstra (173108)
Jack Wetzels (4542142)
Bénédicte Stengel (2818411)
Marie Metzger (156894)
Peter J. Blankestijn (151363)
Hiddo J. Lambers Heerspink (284477)
Ron T. Gansevoort (111233)

May 2019

The black line indicates the average calibration line, the shaded area is the 95% confidence interva...

Kidney failure prediction models: A comprehensive external validation study in patients with advanced CKD

Ramspek C. L.
Evans M.
Wanner C.
Drechsler C.
Chesnaye N. C.
Szymczak M.
Krajewska M.
Torino C.
Porto G.
Hayward S.
Caskey F.
Dekker F. W.
Jager K. J.
van Diepen M.
Cupisti A.
Sagliocca A.
Ferraro A.
Musiala A.
Mele A.
Naticchia A.
Cosaro A.
Woodman A.
Ranghino A.
Stucchi A.
Jonsson A.
Schneider A.
Pignataro A.
Schrander A.
Torp A.
McKeever A.
Szymczak A.
Blom A. -L.
de Blasio A.
Pani A.
Tsalouichos A.
Ullah A.
McLaren B.
van Dam B.
Iwig B.
Antonio B.
Di Iorio B. R.
Rogland B.
Perras B.
Alessandra B.
Harron C.
Wallquist C.
Siegert C.
Barrett C.
Gaillard C.
Garofalo C.
Abaterusso C.
Beerenhout C.
O'Toole C.
Somma C.
Marx C.
Summersgill C.
Blaser C.
D'alessandro C.
Emde C.
Zullo C.
Pozzi C.
Geddes C.
Verburgh C.
Bergamo D.
Ciurlino D.
Motta D.
Glowski D.
McGlynn D.
Vargas D.
Krieter D.
Russo D.
Fuchs D.
Sands D.
Hoogeveen E.
Irmler E.
Dimeny E.
Favaro E.
Platen E.
Olczyk E.
Hoorn E.
Vigotti F.
Ansali F.
Conte F.
Cianciotta F.
Giacchino F.
Cappellaio F.
Pizzarelli F.
Sundelin F.
Uhlin F.
Greco G.
Roy G.
Porto G.
Bigatti G.
Marinangeli G.
Cabiddu G.
Hirst G.
Fumagalli G.
Caloro G.
Piccoli G.
Capasso G.
Gambaro G.
Tognarelli G.
Bonforte G.
Conte G.
Toscano G.
Del Rosso G.
Welander G.
Augustyniak-Bartosik H.
Boots H.
Schmidt-Gurtler H.
King H.
McNally H.
Schlee H.
Boom H.
Naujoks H.
Masri-Senghor H.
Murtagh H.
Rayner H.
Miskowiec-Wisniewska I.
Schlee I.
Capizzi I.
Hernandez I. B.
Baragetti I.
Manitius J.
Turner J.
Eijgenraam J. -W.
Kooman J.
Beige J.
Pondel J.
Wilcox J.
Berdeprado J.
Rothele J.
Wong J.
Rotmans J.
Banda J.
Mazur J.
Hahn K.
Jedrzejak K.
Nowanska K.
Blouin K.
Neumeier K.
Jones K.
Anding-Rost K.
Grontoft K. -C.
Oldrizzi L.
Haydock L.
Vogt L.
Wilkinson L.
Gesualdo L.
Schramm L.
Biancone L.
Nowak L.
Raasveld M.
Durlik M.
Magnano M.
Vervloet M.
Ricardi M.
Carmody M.
Di Bari M.
Laudato M.
Sirico M. L.
Stendahl M.
Svensson M.
Weetman M.
van Buren M.
Joinson M.
Ferraresi M.
Dutton M.
van Diepen M.
Matthews M.
Provenzano M.
Hopf M.
Malaguti M.
Wuttke N.
Morgan N.
Palmieri N.
Frischmuth N.
Bleakley N.
Murrone P.
Cockwell P.
Leurs P.
Roderick P.
Voskamp P.
Kashioulis P.
Ichtiaris P.
Blankestijn P.
Kirste P.
Schulz P.
Mason P.
Kalra P.
Cirillo P.
Dattolo P.
Acampora P.
Sajith R.
Nigro R.
Boero R.
Scarpioni R.
Sicoli R.
Malandra R.
Aign S.
Casar S.
van Esch S.
Chapman S.
Biribauer S.
Navjee S.
Crosbie S.
Brown S.
Tickle S.
Manan S.
Roser S.
Savoldi S.
Bertoli S.
Borrelli S.
Boorsma S.
Heidenreich S.
Melander S.
Maxia S.
Maffei S.
Mangano S.
Palm S.
Konings S.
Mathavakkannan S.
Schwedler S.
Delrieux S.
Renker S.
Schattel S.
Dorota S.
Cicchetti T.
Nieszporek T.
Stephan T.
Schmiedeke T.
Weinreich T.
Leimbach T.
Rappa T.
Almquist T.
Stovesand T.
Bahner U.
Jensen U.
Palazzo V.
de Simone W.
Seeger W.
Kuan Y.
Heleniak Z.
Aydin Z.

January 2021

Background Various prediction models have been developed to predict the risk of kidney failure in pa...

Probabilities of progression to CKD stage 5 in the next t years after inclusion into the cohort for a man with different ages, levels of mGFR and proteinuria at inclusion, estimated from the illness-death model for interval-censored data (IMID, model M4, solid line) and the standard survival model (model M1, dotted line).

Julie Boucquemont (673192)
Marie Metzger (156894)
Christian Combe (162087)
Bénédicte Stengel (2818411)
Karen Leffondre (5661718)

December 2014

<p>Probabilities of progression to CKD stage 5 in the next t years after inclusion into the cohort f...

Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate

Grams, Morgan E.
Sang, Yingying
Ballew, Shoshana H.
Carrero, Juan Jesus
Djurdjev, Ognjenka
Heerspink, Hiddo J. L.
Ho, Kevin
Ito, Sadayoshi
Marks, Angharad
Naimark, David
Nash, Danielle M.
Navaneethan, Sankar D.
Sarnak, Mark
Stengel, Benedicte
Visseren, Frank L. J.
Wang, Angela Yee-Moon
Koettgen, Anna
Levey, Andrew S.
Woodward, Mark
Eckardt, Kai-Uwe
Hemmelgarn, Brenda
Coresh, Josef

June 2018

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at ...

Improving precision in prediction: Using kidney failure risk equations as a potential adjunct to vascular access planning.

Inston, Nicholas
Lok, Charmaine E

January 2019

The timing of referral for creation of vascular access in a patient with declining kidney function i...

Validation of the kidney failure risk equation in European CKD patients

Peeters, M.J.
Zuilen, A.D. van
Brand, A. van den
Bots, M.L.
Blankestijn, P.J.
Wetzels, J.F.M.
Vervoort, G.M.M.
et al.

January 2013

BACKGROUND: Patients with chronic kidney disease (CKD) are at risk for progression to kidney failure...

Predicting Kidney Failure from Longitudinal Kidney Function Trajectory: A Comparison of Models

Brand, dr. A.J.G. van den (Radboud University)

April 2019

Minimal datasets used to train and validate models described in Predicting Kidney Failure from Longi...

Logistic regression models for the prediction of end stage renal disease at two years, with measures of model performance and the incremental value of adding kappa/creatinine ratio (KCR) or lambda/creatinine ratio (LCR) to a baseline model containing the kidney failure risk equation (KFRE).

Anthony Fenton (3343094)
Mark D. Jesky (535502)
Rachel Webster (3249432)
Stephanie J. Stringer (535501)
Punit Yadav (535503)
Iain Chapple (5194430)
Indranil Dasgupta (3343091)
Stephen J. Harding (5194427)
Charles J. Ferro (319168)
Paul Cockwell (345149)

May 2018

<p>Logistic regression models for the prediction of end stage renal disease at two years, with measu...

An independent validation of the kidney failure risk equation in an Asian population

Kang, Min Woo
Tangri, Navdeep
Kim, Yong Chul
An, Jung Nam
Lee, Jeonghwan
Li, Lilin
Oh, Yun Kyu
Kim, Dong Ki
Joo, Kwon Wook
Kim, Yon Su
Lim, Chun Soo
Lee, Jung Pyo

July 2020

Predicting the risk of end-stage renal disease (ESRD) progression facilitates appropriate nephrology...

A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests.

Zacharias, Helena U
Altenbuchinger, Michael
Schultheiss, Ulla T
Raffler, Johannes
Kotsis, Fruzsina
Ghasemi, Sahar
Ali, Ibrahim
Kollerits, Barbara
Metzger, Marie
Steinbrenner, Inga
Sekula, Peggy
Massy, Ziad A
Combe, Christian
Kalra, Philip A
Kronenberg, Florian
Stengel, Bénédicte
Eckardt, Kai-Uwe
Köttgen, Anna
Schmid, Matthias
Gronwald, Wolfram
Oefner, Peter J

July 2021

International audienceStratification of chronic kidney disease (CKD) patients at risk for progressin...

Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

Min-Jeong Lee (1539964)
Joo-Han Park (5819240)
Yeo Rae Moon (5819243)
Soo-Yeon Jo (5819246)
Dukyong Yoon (469192)
Rae Woong Park (469195)
Jong Cheol Jeong (1372383)
Inwhee Park (469193)
Gyu-Tae Shin (5819249)
Heungsoo Kim (2515300)

October 2018

PurposeWe aimed to develop a model of chronic kidney disease (CKD) progression for predicting the pr...

Predicted probability of end stage renal failure at 5 years for 4 Hypothetical Patient Profiles based on Model 2 and the 4-variable Kidney Failure Risk Equation.

Abstract

Extracted data

Predicted probability of end stage renal failure at 5 years for 4 Hypothetical Patient Profiles based on Model 2 and the 4-variable Kidney Failure Risk Equation.

Abstract

Extracted data

Related items

Related items