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Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes

Identifieur interne : 005990 ( Main/Exploration ); précédent : 005989; suivant : 005991

Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes

Auteurs : Meg J. Jardine [Australie] ; Jun Hata [Australie] ; Mark Woodward [Australie] ; Vlado Perkovic [Australie] ; Toshiharu Ninomiya [Australie] ; Hisatomi Arima [Australie] ; Sophia Zoungas [Australie] ; Alan Cass [Australie] ; Anushka Patel [Australie] ; Michel Marre [France] ; Giuseppe Mancia [Italie] ; Carl E. Mogensen [Danemark] ; Neil Poulter [Royaume-Uni] ; John Chalmers [Australie]

Source :

RBID : Pascal:12-0424074

Descripteurs français

English descriptors

Abstract

Background: Tools are needed to predict which individuals with diabetes will develop kidney disease and its complications. Study Design: An observational analysis of a randomized controlled trial. Setting & Participants: The ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation) Study followed up 11,140 participants with type 2 diabetes for 5 years. Predictor: Readily available baseline demographic and clinical variables. Outcomes: (1) Major kidney-related events (doubling of serum creatinine to ≥2.26 mg/dL [≥200 μmol/L], renal replacement therapy, or renal death) in all participants, and (2) new-onset albuminuria in participants with baseline normoalbuminuria. Measurements: Cox proportional hazard regression models predicting the outcomes were used to generate risk scores. Discrimination of the risk prediction models was compared with that of models based on estimated glomerular filtration rate (eGFR) alone, urinary albumin-creatinine ratio (ACR) alone, and their combination. Results: Risk scores for major kidney-related events and new-onset albuminuria were derived from 7- and 8-variable models, respectively. Baseline eGFR and ACR were dominant although models based on the 2 factors, alone or combined, had less discrimination (P < 0.05) than the risk prediction models containing additional variables (risk prediction model C statistics of 0.847 [95% CI, 0.815-0.880] for major kidney-related events, and 0.647 [95% CI, 0.637-0.658] for new-onset albuminuria). Novel risk factors for new-onset albuminuria included Asian ethnicity and greater waist circumference, and for major kidney-related events, less education. The risk prediction models had acceptable calibration for both outcomes (modified Hosmer-Lemeshow test, P = 0.9 and P = 0.06, respectively). Limitations: The follow-up period was limited to 5 years. Results are applicable to people with type 2 diabetes at risk of vascular disease. Conclusions: Risk scores have been developed for early and late events in diabetic nephropathy. Although eGFR and urinary ACR are important components of the prediction models, the extra variables considered add significantly to discrimination and, in the case of new-onset albuminuria, are required to achieve satisfactory calibration.


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<name sortKey="Marre, Michel" sort="Marre, Michel" uniqKey="Marre M" first="Michel" last="Marre">Michel Marre</name>
<affiliation wicri:level="3">
<inist:fA14 i1="04">
<s1>Service d'Endocrinologie Diabétologie Nutrition, Groupe Hospitalier Bichat-Claude Bernard</s1>
<s2>Paris</s2>
<s3>FRA</s3>
<sZ>10 aut.</sZ>
</inist:fA14>
<country>France</country>
<placeName>
<region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Paris</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Mancia, Giuseppe" sort="Mancia, Giuseppe" uniqKey="Mancia G" first="Giuseppe" last="Mancia">Giuseppe Mancia</name>
<affiliation wicri:level="3">
<inist:fA14 i1="05">
<s1>Department of Clinical Medicine and Prevention, University of Milano-Bicocca</s1>
<s2>Milan</s2>
<s3>ITA</s3>
<sZ>11 aut.</sZ>
</inist:fA14>
<country>Italie</country>
<placeName>
<settlement type="city">Milan</settlement>
<region nuts="2">Lombardie</region>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Mogensen, Carl E" sort="Mogensen, Carl E" uniqKey="Mogensen C" first="Carl E." last="Mogensen">Carl E. Mogensen</name>
<affiliation wicri:level="1">
<inist:fA14 i1="06">
<s1>Medical Department M, Aarhus University Hospital, Aarhus Sygehus</s1>
<s2>Aarhus C</s2>
<s3>DNK</s3>
<sZ>12 aut.</sZ>
</inist:fA14>
<country>Danemark</country>
<wicri:noRegion>Aarhus C</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Poulter, Neil" sort="Poulter, Neil" uniqKey="Poulter N" first="Neil" last="Poulter">Neil Poulter</name>
<affiliation wicri:level="3">
<inist:fA14 i1="07">
<s1>International Centre for Circulatory Health, National Heart and Lung Institute, Imperial College London</s1>
<s2>London</s2>
<s3>GBR</s3>
<sZ>13 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<placeName>
<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Chalmers, John" sort="Chalmers, John" uniqKey="Chalmers J" first="John" last="Chalmers">John Chalmers</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>The George Institute for Global Health</s1>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>5 aut.</sZ>
<sZ>6 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
<sZ>9 aut.</sZ>
<sZ>14 aut.</sZ>
</inist:fA14>
<country>Australie</country>
<wicri:noRegion>The George Institute for Global Health</wicri:noRegion>
</affiliation>
</author>
</analytic>
<series>
<title level="j" type="main">American journal of kidney diseases</title>
<title level="j" type="abbreviated">Am. j. kidney dis.</title>
<idno type="ISSN">0272-6386</idno>
<imprint>
<date when="2012">2012</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt>
<title level="j" type="main">American journal of kidney diseases</title>
<title level="j" type="abbreviated">Am. j. kidney dis.</title>
<idno type="ISSN">0272-6386</idno>
</seriesStmt>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Chronic kidney disease</term>
<term>Diabetic nephropathy</term>
<term>Human</term>
<term>Kidney</term>
<term>Models</term>
<term>Nephrology</term>
<term>Prediction</term>
<term>Predictive factor</term>
<term>Prognosis</term>
<term>Renal failure</term>
<term>Risk analysis</term>
<term>Type 2 diabetes</term>
<term>Urology</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Diabète de type 2</term>
<term>Facteur prédictif</term>
<term>Prédiction</term>
<term>Néphropathie diabétique</term>
<term>Rein</term>
<term>Pronostic</term>
<term>Néphropathie chronique</term>
<term>Homme</term>
<term>Insuffisance rénale</term>
<term>Modèle</term>
<term>Analyse risque</term>
<term>Néphrologie</term>
<term>Urologie</term>
</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr">
<term>Homme</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">Background: Tools are needed to predict which individuals with diabetes will develop kidney disease and its complications. Study Design: An observational analysis of a randomized controlled trial. Setting & Participants: The ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation) Study followed up 11,140 participants with type 2 diabetes for 5 years. Predictor: Readily available baseline demographic and clinical variables. Outcomes: (1) Major kidney-related events (doubling of serum creatinine to ≥2.26 mg/dL [≥200 μmol/L], renal replacement therapy, or renal death) in all participants, and (2) new-onset albuminuria in participants with baseline normoalbuminuria. Measurements: Cox proportional hazard regression models predicting the outcomes were used to generate risk scores. Discrimination of the risk prediction models was compared with that of models based on estimated glomerular filtration rate (eGFR) alone, urinary albumin-creatinine ratio (ACR) alone, and their combination. Results: Risk scores for major kidney-related events and new-onset albuminuria were derived from 7- and 8-variable models, respectively. Baseline eGFR and ACR were dominant although models based on the 2 factors, alone or combined, had less discrimination (P < 0.05) than the risk prediction models containing additional variables (risk prediction model C statistics of 0.847 [95% CI, 0.815-0.880] for major kidney-related events, and 0.647 [95% CI, 0.637-0.658] for new-onset albuminuria). Novel risk factors for new-onset albuminuria included Asian ethnicity and greater waist circumference, and for major kidney-related events, less education. The risk prediction models had acceptable calibration for both outcomes (modified Hosmer-Lemeshow test, P = 0.9 and P = 0.06, respectively). Limitations: The follow-up period was limited to 5 years. Results are applicable to people with type 2 diabetes at risk of vascular disease. Conclusions: Risk scores have been developed for early and late events in diabetic nephropathy. Although eGFR and urinary ACR are important components of the prediction models, the extra variables considered add significantly to discrimination and, in the case of new-onset albuminuria, are required to achieve satisfactory calibration.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Australie</li>
<li>Danemark</li>
<li>France</li>
<li>Italie</li>
<li>Royaume-Uni</li>
</country>
<region>
<li>Angleterre</li>
<li>Grand Londres</li>
<li>Lombardie</li>
<li>Nouvelle-Galles du Sud</li>
<li>Victoria (État)</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Londres</li>
<li>Melbourne</li>
<li>Milan</li>
<li>Paris</li>
<li>Sydney</li>
</settlement>
</list>
<tree>
<country name="Australie">
<noRegion>
<name sortKey="Jardine, Meg J" sort="Jardine, Meg J" uniqKey="Jardine M" first="Meg J." last="Jardine">Meg J. Jardine</name>
</noRegion>
<name sortKey="Arima, Hisatomi" sort="Arima, Hisatomi" uniqKey="Arima H" first="Hisatomi" last="Arima">Hisatomi Arima</name>
<name sortKey="Cass, Alan" sort="Cass, Alan" uniqKey="Cass A" first="Alan" last="Cass">Alan Cass</name>
<name sortKey="Chalmers, John" sort="Chalmers, John" uniqKey="Chalmers J" first="John" last="Chalmers">John Chalmers</name>
<name sortKey="Hata, Jun" sort="Hata, Jun" uniqKey="Hata J" first="Jun" last="Hata">Jun Hata</name>
<name sortKey="Jardine, Meg J" sort="Jardine, Meg J" uniqKey="Jardine M" first="Meg J." last="Jardine">Meg J. Jardine</name>
<name sortKey="Ninomiya, Toshiharu" sort="Ninomiya, Toshiharu" uniqKey="Ninomiya T" first="Toshiharu" last="Ninomiya">Toshiharu Ninomiya</name>
<name sortKey="Patel, Anushka" sort="Patel, Anushka" uniqKey="Patel A" first="Anushka" last="Patel">Anushka Patel</name>
<name sortKey="Perkovic, Vlado" sort="Perkovic, Vlado" uniqKey="Perkovic V" first="Vlado" last="Perkovic">Vlado Perkovic</name>
<name sortKey="Woodward, Mark" sort="Woodward, Mark" uniqKey="Woodward M" first="Mark" last="Woodward">Mark Woodward</name>
<name sortKey="Zoungas, Sophia" sort="Zoungas, Sophia" uniqKey="Zoungas S" first="Sophia" last="Zoungas">Sophia Zoungas</name>
<name sortKey="Zoungas, Sophia" sort="Zoungas, Sophia" uniqKey="Zoungas S" first="Sophia" last="Zoungas">Sophia Zoungas</name>
</country>
<country name="France">
<region name="Île-de-France">
<name sortKey="Marre, Michel" sort="Marre, Michel" uniqKey="Marre M" first="Michel" last="Marre">Michel Marre</name>
</region>
</country>
<country name="Italie">
<region name="Lombardie">
<name sortKey="Mancia, Giuseppe" sort="Mancia, Giuseppe" uniqKey="Mancia G" first="Giuseppe" last="Mancia">Giuseppe Mancia</name>
</region>
</country>
<country name="Danemark">
<noRegion>
<name sortKey="Mogensen, Carl E" sort="Mogensen, Carl E" uniqKey="Mogensen C" first="Carl E." last="Mogensen">Carl E. Mogensen</name>
</noRegion>
</country>
<country name="Royaume-Uni">
<region name="Angleterre">
<name sortKey="Poulter, Neil" sort="Poulter, Neil" uniqKey="Poulter N" first="Neil" last="Poulter">Neil Poulter</name>
</region>
</country>
</tree>
</affiliations>
</record>

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