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Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis

Identifieur interne : 001A82 ( Main/Exploration ); précédent : 001A81; suivant : 001A83

Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis

Auteurs : Navdeep Tangri ; Morgan E. Grams ; Andrew S. Levey ; Josef Coresh ; Lawrence Appel ; Brad C. Astor ; Gabriel Chodick ; Allan J. Collins ; Ognjenka Djurdjev ; C. Raina Elley ; Marie Evans ; Amit X. Garg ; Stein I. Hallan ; Lesley 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

Source :

RBID : PMC:4752167

Abstract

Importance

Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations (KFREs) were previously developed and validated in two 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 KFREs 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 3–5 in over 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 KFREs, cohort-specific hazard ratios were estimated, and combined in meta-analysis to form new “pooled” KFREs. Original and pooled equation performance was compared, and the need for regional calibration factors was assessed.

Main Outcome and Measure

Kidney failure (treatment by dialysis or kidney transplantation).

Results

During a median follow-up of 4 years, 23,829 cases of kidney failure were observed. The original KFREs 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 and 0.88 (95% CI 0.86–0.90) at 5 years); 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 KFREs 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 12/15 and 10/13 non-North American cohorts at 2 and 5 years, respectively (p=0.04 and p=0.02).

Conclusions and Relevance

KFREs 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.


Url:
DOI: 10.1001/jama.2015.18202
PubMed: 26757465
PubMed Central: 4752167


Affiliations:


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<name sortKey="Tangri, Navdeep" sort="Tangri, Navdeep" uniqKey="Tangri N" first="Navdeep" last="Tangri">Navdeep Tangri</name>
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<name sortKey="Navaneethan, Sankar D" sort="Navaneethan, Sankar D" uniqKey="Navaneethan S" first="Sankar D." last="Navaneethan">Sankar D. Navaneethan</name>
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<front>
<div type="abstract" xml:lang="en">
<sec id="S1">
<title>Importance</title>
<p id="P1">Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations (KFREs) were previously developed and validated in two Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed.</p>
</sec>
<sec id="S2">
<title>Objective</title>
<p id="P2">To evaluate the accuracy of the KFREs across different geographic regions and patient populations through individual-participant data meta-analysis.</p>
</sec>
<sec id="S3">
<title>Data Sources</title>
<p id="P3">Thirty-one cohorts, including 721,357 participants with CKD Stages 3–5 in over 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014.</p>
</sec>
<sec id="S4">
<title>Study Selection</title>
<p id="P4">Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease.</p>
</sec>
<sec id="S5">
<title>Data Extraction and Synthesis</title>
<p id="P5">Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original KFREs, cohort-specific hazard ratios were estimated, and combined in meta-analysis to form new “pooled” KFREs. Original and pooled equation performance was compared, and the need for regional calibration factors was assessed.</p>
</sec>
<sec id="S6">
<title>Main Outcome and Measure</title>
<p id="P6">Kidney failure (treatment by dialysis or kidney transplantation).</p>
</sec>
<sec id="S7">
<title>Results</title>
<p id="P7">During a median follow-up of 4 years, 23,829 cases of kidney failure were observed. The original KFREs 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 and 0.88 (95% CI 0.86–0.90) at 5 years); 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 KFREs 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 12/15 and 10/13 non-North American cohorts at 2 and 5 years, respectively (p=0.04 and p=0.02).</p>
</sec>
<sec id="S8">
<title>Conclusions and Relevance</title>
<p id="P8">KFREs 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.</p>
</sec>
</div>
</front>
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<name sortKey="Inker, Lesley" sort="Inker, Lesley" uniqKey="Inker L" first="Lesley" last="Inker">Lesley Inker</name>
<name sortKey="Iseki, Kunitoshi" sort="Iseki, Kunitoshi" uniqKey="Iseki K" first="Kunitoshi" last="Iseki">Kunitoshi Iseki</name>
<name sortKey="Ito, Sadayoshi" sort="Ito, Sadayoshi" uniqKey="Ito S" first="Sadayoshi" last="Ito">Sadayoshi Ito</name>
<name sortKey="Jee, Sun Ha" sort="Jee, Sun Ha" uniqKey="Jee S" first="Sun Ha" last="Jee">Sun Ha Jee</name>
<name sortKey="Kovesdy, Csaba P" sort="Kovesdy, Csaba P" uniqKey="Kovesdy C" first="Csaba P." last="Kovesdy">Csaba P. Kovesdy</name>
<name sortKey="Kronenberg, Florian" sort="Kronenberg, Florian" uniqKey="Kronenberg F" first="Florian" last="Kronenberg">Florian Kronenberg</name>
<name sortKey="Lambers Heerspink, Hiddo J" sort="Lambers Heerspink, Hiddo J" uniqKey="Lambers Heerspink H" first="Hiddo J." last="Lambers Heerspink">Hiddo J. Lambers Heerspink</name>
<name sortKey="Levey, Andrew S" sort="Levey, Andrew S" uniqKey="Levey A" first="Andrew S." last="Levey">Andrew S. Levey</name>
<name sortKey="Marks, Angharad" sort="Marks, Angharad" uniqKey="Marks A" first="Angharad" last="Marks">Angharad Marks</name>
<name sortKey="Nadkarni, Girish N" sort="Nadkarni, Girish N" uniqKey="Nadkarni G" first="Girish N." last="Nadkarni">Girish N. Nadkarni</name>
<name sortKey="Navaneethan, Sankar D" sort="Navaneethan, Sankar D" uniqKey="Navaneethan S" first="Sankar D." last="Navaneethan">Sankar D. Navaneethan</name>
<name sortKey="Nelson, Robert G" sort="Nelson, Robert G" uniqKey="Nelson R" first="Robert G." last="Nelson">Robert G. Nelson</name>
<name sortKey="Sarnak, Mark J" sort="Sarnak, Mark J" uniqKey="Sarnak M" first="Mark J." last="Sarnak">Mark J. Sarnak</name>
<name sortKey="Stengel, Benedicte" sort="Stengel, Benedicte" uniqKey="Stengel B" first="Benedicte" last="Stengel">Benedicte Stengel</name>
<name sortKey="Tangri, Navdeep" sort="Tangri, Navdeep" uniqKey="Tangri N" first="Navdeep" last="Tangri">Navdeep Tangri</name>
<name sortKey="Titze, Stephanie" sort="Titze, Stephanie" uniqKey="Titze S" first="Stephanie" last="Titze">Stephanie Titze</name>
<name sortKey="Woodward, Mark" sort="Woodward, Mark" uniqKey="Woodward M" first="Mark" last="Woodward">Mark Woodward</name>
</noCountry>
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