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Original Investigation |

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

Navdeep Tangri, MD, PhD, FRCPC1,2; Morgan E. Grams, MD, PhD3; Andrew S. Levey, MD4; Josef Coresh, MD, PhD5; Lawrence J. Appel, MD3,5; Brad C. Astor, PhD, MPH6,7; Gabriel Chodick, PhD8; Allan J. Collins, MD9,10; Ognjenka Djurdjev, MSc11; C. Raina Elley, MBCHB, PhD12; Marie Evans, MD, PhD13; Amit X. Garg, MD, PhD14; Stein I. Hallan, MD, PhD15,16; Lesley A. Inker, MD, MS4; Sadayoshi Ito, MD, PhD17; Sun Ha Jee, PhD18; Csaba P. Kovesdy, MD19,20; Florian Kronenberg, MD21; Hiddo J. L. Heerspink, PharmD, PhD22; Angharad Marks, MBBCh, MRCP, MSc, PhD23; Girish N. Nadkarni, MD, MPH24; Sankar D. Navaneethan, MD, MPH25; Robert G. Nelson, MD, PhD26; Stephanie Titze, MD, MSc27; Mark J. Sarnak, MD, MS4; Benedicte Stengel, MD, PhD28,29; Mark Woodward, PhD5,30,31; Kunitoshi Iseki, MD, PhD32 ; for the CKD Prognosis Consortium
[+] Author Affiliations
1Department of Medicine, Seven Oaks General Hospital, University of Manitoba, Winnipeg, Canada
2Department of Community Health Sciences, Seven Oaks General Hospital, University of Manitoba, Winnipeg, Canada
3Johns Hopkins Medical Institutions, Baltimore, Maryland
4Division of Nephrology at Tufts Medical Center, Boston, Massachusetts
5Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
6Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison
7Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison
8Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
9Chronic Disease Research Group, Minneapolis Medical Research Foundation, Minneapolis, Minnesota
10Department of Medicine, University of Minnesota, Minneapolis
11Department of Measurement & Reporting, Provincial Health Service Authority, Vancouver, British Columbia, Canada
12Department of General Practice and Primary Health Care, School of Population Health, University of Auckland, Auckland, New Zealand
13Division of Renal Medicine, CLINTEC, Karolinska Institutet, Stockholm, Sweden
14Departments of Medicine and Epidemiology and Biostatistics, Western University, and Institute for Clinical Evaluative Sciences, Ontario, Canada
15Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science Technology, Trondheim
16Division of Nephrology, Department of Medicine, St Olav University Hospital, Trondheim, Norway
17Division of Nephrology, Endocrinology and Vascular Medicine, Department of Medicine, Tohoku University School of Medicine, Sendai, Japan
18Department of Epidemiology and Health Promotion, Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
19Memphis Veterans Affairs Medical Center, Memphis, Tennessee
20University of Tennessee Health Science Center, Memphis, Tennessee
21Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
22Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, the Netherlands
23Division of Applied Health Sciences, University of Aberdeen, and NHS Grampian, Foresterhill, Aberdeen, Scotland
24Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
25Division of Nephrology and Hypertension, Cleveland Clinic, Cleveland, Ohio
26National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
27Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany
28CESP, INSERM, Villejuif, France
29Université Paris-Saclay, Université Paris-Sud, UVSQ, Villejuif, France
30The George Institute for Global Health, Nuffield Department of Population Health, University of Oxford, Oxford, England
31The George Institute for Global Health, University of Sydney, Sydney, Australia
32Dialysis Unit, University Hospital of the Ryukyus, Nishihara, Okinawa, Japan
JAMA. 2016;315(2):164-174. doi:10.1001/jama.2015.18202.
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Importance  Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed.

Objective  To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis.

Data Sources  Thirty-one cohorts, including 721 357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014.

Study Selection  Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease.

Data Extraction and Synthesis  Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed.

Main Outcomes and Measures  Kidney failure (treatment by dialysis or kidney transplant).

Results  During a median follow-up of 4 years of 721 357 participants with CKD, 23 829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibration (the difference between observed and predicted risk) was adequate in North American cohorts, but the original risk equations overestimated risk in some non-North American cohorts. Addition of a calibration factor that lowered the baseline risk by 32.9% at 2 years and 16.5% at 5 years improved the calibration in 12 of 15 and 10 of 13 non-North American cohorts at 2 and 5 years, respectively (P = .04 and P = .02).

Conclusions and Relevance  Kidney failure risk equations developed in a Canadian population showed high discrimination and adequate calibration when validated in 31 multinational cohorts. However, in some regions the addition of a calibration factor may be necessary.

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Figure 1.
Discrimination Statistics (C Statistics) for Original 4-Variable Equation at 2 Years by Cohort

Due to a limited number of events, confidence intervals were wide in some studies and therefore capped at 1.00 (maximum value for C statistic). Size is proportional to the weight of the study in a random effects meta-analysis. Arrows indicate that the true values are beyond the range of the axis. The dashed line indicates the overall C statistic. Representative references and expanded acronyms for each cohort name are provided in eAppendix 3 in the Supplement.

aCohort with dipstick proteinuria.

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Figure 2.
Discrimination Statistics (C Statistics) for Original 4-Variable Equation at 5 Years by Cohort

Due to a limited number of events, confidence intervals were wide in some studies and therefore capped at 1.00 (maximum value for C statistic). Size is proportional to the weight of the study in a random effects meta-analysis. The dashed line indicates the overall C statistic. Representative references and expanded acronyms for each cohort name are provided in eAppendix 3 in the Supplement.

aCohort with dipstick proteinuria.

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Figure 3.
Discrimination Statistics (C Statistics) for Original 4-Variable and 8-Variable Equations at 2 and 5 Years by Subgroup

In the 4-variable equation analyses, 31 cohorts contributed to the 2-year analysis and 26 cohorts to the 5-year analysis. In the 8-variable equation analyses, 16 cohorts contributed to 2-year analysis and 11 cohorts contributed to the 5-year analysis.

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Figure 4.
Refit Baseline Hazard of Original 4-Variable Equation at 2 and 5 Years in Individual Cohorts Stratified by Region

Thin black line represents the centered baseline hazard for the original 4-variable kidney failure risk equation (age 70 years; male, 56%; eGFR, 36 mL/min/1.73 m2; urine albumin to creatinine ratio, 170 mg/g); the orange and blue horizontal lines represent the weighted mean refit baseline hazard within each region (North America and non-North America). The 25 cohorts included represent studies with available urine albumin to creatinine ratio. Studies with dipstick proteinuria were not included in the calculation. See Table 1 footnotes for expansion of cohort abbreviations.

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