Literature DB >> 23645476

Validation of the kidney failure risk equation in European CKD patients.

Mieke J Peeters1, Arjan D van Zuilen, Jan A J G van den Brand, Michiel L Bots, Peter J Blankestijn, Jack F M Wetzels.   

Abstract

BACKGROUND: Patients with chronic kidney disease (CKD) are at risk for progression to kidney failure. Using data of Canadian CKD patients, Tangri et al. recently developed models to predict the progression of CKD stages 3-5 to kidney failure within 5 years. We validated this kidney failure risk equation (KFRE) in European CKD patients.
METHODS: We selected non-transplanted patients with CKD stages 3-5 who participated in the MASTERPLAN study, a randomized controlled trial in patients with CKD. Kidney failure was defined as the initiation of chronic dialysis or kidney transplantation within 5 years. Patients who died before kidney failure were censored. Patients followed for <5 years, who did not develop kidney failure and did not die, were excluded. The 5-year kidney failure risk was predicted using three different models developed by Tangri et al. and compared with the actual kidney failure rate in MASTERPLAN. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), the net reclassification index (NRI) and by comparing the observed and predicted rates of kidney failure.
RESULTS: A total of 595 patients were included; 114 developed kidney failure. (Overall observed kidney failure risk in our cohort was 5% lower than in the Canadian validation cohort.) Discrimination of the eight-variable model [including age, sex, estimated glomerular filtration rate (eGFR), albuminuria, calcium, phosphate, bicarbonate, albumin] was similar to that of the four-variable model (including age, sex, eGFR, albuminuria) and the three-variable model (including age, sex, eGFR); ROC-AUCs were 0.89 [95% confidence interval (CI) 0.86-0.92], 0.88 (95% CI 0.85-0.91) and 0.88 (95% CI 0.85-0.92), respectively. Using the NRI, the eight-variable model slightly outperformed the four-variable model (NRI 6.5%) and the three-variable model (NRI 12.4%). The mean differences between the observed and predicted kidney failure risk were -4.0, -7.1 and -7.4% for the eight-, four-, and three-variable model, respectively.
CONCLUSIONS: The KFRE accurately predicted the progression to kidney failure in European CKD patients. Discrimination of the three models was similar. Calibration of the eight-variable model was slightly better than that of the simpler models. We question whether this outweighs its added complexity.

Entities:  

Keywords:  chronic kidney disease; kidney failure; risk prediction models; validation

Mesh:

Year:  2013        PMID: 23645476     DOI: 10.1093/ndt/gft063

Source DB:  PubMed          Journal:  Nephrol Dial Transplant        ISSN: 0931-0509            Impact factor:   5.992


  26 in total

1.  Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis.

Authors:  Navdeep Tangri; Morgan E Grams; Andrew S Levey; Josef Coresh; Lawrence J Appel; Brad C Astor; Gabriel Chodick; Allan J Collins; Ognjenka Djurdjev; C Raina Elley; Marie Evans; Amit X Garg; Stein I Hallan; Lesley A Inker; Sadayoshi Ito; Sun Ha Jee; Csaba P Kovesdy; Florian Kronenberg; Hiddo J Lambers Heerspink; Angharad Marks; Girish N Nadkarni; Sankar D Navaneethan; Robert G Nelson; Stephanie Titze; Mark J Sarnak; Benedicte Stengel; Mark Woodward; Kunitoshi Iseki
Journal:  JAMA       Date:  2016-01-12       Impact factor: 56.272

2.  Use of the Kidney Failure Risk Equation to Determine the Risk of Progression to End-stage Renal Disease in Children With Chronic Kidney Disease.

Authors:  Erica Winnicki; Charles E McCulloch; Mark M Mitsnefes; Susan L Furth; Bradley A Warady; Elaine Ku
Journal:  JAMA Pediatr       Date:  2018-02-01       Impact factor: 16.193

3.  External Validation of the Kidney Failure Risk Equation and Re-Calibration with Addition of Ultrasound Parameters.

Authors:  Claudia S Lennartz; John William Pickering; Sarah Seiler-Mußler; Lucie Bauer; Kathrin Untersteller; Insa E Emrich; Adam M Zawada; Jörg Radermacher; Navdeep Tangri; Danilo Fliser; Gunnar H Heine
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5.  Low performance of prognostic tools for predicting dialysis in elderly people with advanced CKD.

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6.  Shared decision-making in advanced kidney disease: a scoping review.

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Journal:  BMJ Open       Date:  2022-09-21       Impact factor: 3.006

7.  Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD.

Authors:  Gregory L Hundemer; Navdeep Tangri; Manish M Sood; Tim Ramsay; Ann Bugeja; Pierre A Brown; Edward G Clark; Mohan Biyani; Christine A White; Ayub Akbari
Journal:  Clin J Am Soc Nephrol       Date:  2020-09-14       Impact factor: 8.237

8.  ESRD and death after heart failure in CKD.

Authors:  Maneesh Sud; Navdeep Tangri; Melania Pintilie; Andrew S Levey; David M J Naimark
Journal:  J Am Soc Nephrol       Date:  2014-09-04       Impact factor: 10.121

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

Authors:  Chava L Ramspek; Marie Evans; Christoph Wanner; Christiane Drechsler; Nicholas C Chesnaye; Maciej Szymczak; Magdalena Krajewska; Claudia Torino; Gaetana Porto; Samantha Hayward; Fergus Caskey; Friedo W Dekker; Kitty J Jager; Merel van Diepen
Journal:  J Am Soc Nephrol       Date:  2021-03-08       Impact factor: 10.121

10.  CKD Awareness Among US Adults by Future Risk of Kidney Failure.

Authors:  Chi D Chu; Charles E McCulloch; Tanushree Banerjee; Meda E Pavkov; Nilka R Burrows; Brenda W Gillespie; Rajiv Saran; Michael G Shlipak; Neil R Powe; Delphine S Tuot
Journal:  Am J Kidney Dis       Date:  2020-04-15       Impact factor: 8.860

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