AustralieFrV1, PascalFrancis, Corpus, bibRecord, 000F07

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

Identifieur interne : 000F07 ( PascalFrancis/Corpus ); précédent : 000F06; suivant : 000F08

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

Auteurs : Meg J. Jardine ; Jun Hata ; Mark Woodward ; Vlado Perkovic ; Toshiharu Ninomiya ; Hisatomi Arima ; Sophia Zoungas ; Alan Cass ; Anushka Patel ; Michel Marre ; Giuseppe Mancia ; Carl E. Mogensen ; Neil Poulter ; John Chalmers

Source :

American journal of kidney diseases [ 0272-6386 ] ; 2012.

RBID : Pascal:12-0424074

Descripteurs français

Pascal (Inist)
- Diabète de type 2, Facteur prédictif, Prédiction, Néphropathie diabétique, Rein, Pronostic, Néphropathie chronique, Homme, Insuffisance rénale, Modèle, Analyse risque, Néphrologie, Urologie.

English descriptors

KwdEn :
- Chronic kidney disease, Diabetic nephropathy, Human, Kidney, Models, Nephrology, Prediction, Predictive factor, Prognosis, Renal failure, Risk analysis, Type 2 diabetes, Urology.

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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C01	`01`		`ENG`	@0 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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Format Inist (serveur)

NO :	PASCAL 12-0424074 INIST
ET :	Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes
AU :	JARDINE (Meg J.); HATA (Jun); WOODWARD (Mark); PERKOVIC (Vlado); NINOMIYA (Toshiharu); ARIMA (Hisatomi); ZOUNGAS (Sophia); CASS (Alan); PATEL (Anushka); MARRE (Michel); MANCIA (Giuseppe); MOGENSEN (Carl E.); POULTER (Neil); CHALMERS (John)
AF :	The George Institute for Global Health/Australie (1 aut., 2 aut., 3 aut., 4 aut., 5 aut., 6 aut., 7 aut., 8 aut., 9 aut., 14 aut.); Concord Repatriation General Hospital/Sydney/Australie (1 aut.); School of Public Health, Monash University/Melbourne/Australie (7 aut.); Service d'Endocrinologie Diabétologie Nutrition, Groupe Hospitalier Bichat-Claude Bernard/Paris/France (10 aut.); Department of Clinical Medicine and Prevention, University of Milano-Bicocca/Milan/Italie (11 aut.); Medical Department M, Aarhus University Hospital, Aarhus Sygehus/Aarhus C/Danemark (12 aut.); International Centre for Circulatory Health, National Heart and Lung Institute, Imperial College London/London/Royaume-Uni (13 aut.)
DT :	Publication en série; Niveau analytique
SO :	American journal of kidney diseases; ISSN 0272-6386; Etats-Unis; Da. 2012; Vol. 60; No. 5; Pp. 770-778; Bibl. 45 ref.
LA :	Anglais
EA :	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.
CC :	002B14E01; 002B21E01A
FD :	Diabète de type 2; Facteur prédictif; Prédiction; Néphropathie diabétique; Rein; Pronostic; Néphropathie chronique; Homme; Insuffisance rénale; Modèle; Analyse risque; Néphrologie; Urologie
FG :	Appareil urinaire; Endocrinopathie; Maladie métabolique; Pathologie de l'appareil urinaire; Pathologie du rein; Association morbide
ED :	Type 2 diabetes; Predictive factor; Prediction; Diabetic nephropathy; Kidney; Prognosis; Chronic kidney disease; Human; Renal failure; Models; Risk analysis; Nephrology; Urology
EG :	Urinary system; Endocrinopathy; Metabolic diseases; Urinary system disease; Kidney disease; Concomitant disease
SD :	Diabetes de tipo 2; Factor predictivo; Predicción; Nefropatía diabética; Riñón; Pronóstico; Nefropatía crónica; Hombre; Insuficiencia renal; Modelo; Análisis riesgo; Nefrología; Urología
LO :	INIST-19098.354000505374560280
ID :	12-0424074

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Pascal:12-0424074

Le document en format XML

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<affiliation><inist:fA14 i1="04"><s1>Service d'Endocrinologie Diabétologie Nutrition, Groupe Hospitalier Bichat-Claude Bernard</s1>
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<affiliation><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>
</affiliation>
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<affiliation><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>
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<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>
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<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>
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<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>
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<sZ>11 aut.</sZ>
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<s2>Aarhus C</s2>
<s3>DNK</s3>
<sZ>12 aut.</sZ>
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<fC01 i1="01" l="ENG"><s0>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.</s0>
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<s5>40</s5>
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<s5>40</s5>
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<fC07 i1="05" i2="X" l="FRE"><s0>Pathologie du rein</s0>
<s5>41</s5>
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<fC07 i1="05" i2="X" l="ENG"><s0>Kidney disease</s0>
<s5>41</s5>
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<s5>41</s5>
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<s5>42</s5>
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<s5>42</s5>
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<s5>42</s5>
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<server><NO>PASCAL 12-0424074 INIST</NO>
<ET>Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes</ET>
<AU>JARDINE (Meg J.); HATA (Jun); WOODWARD (Mark); PERKOVIC (Vlado); NINOMIYA (Toshiharu); ARIMA (Hisatomi); ZOUNGAS (Sophia); CASS (Alan); PATEL (Anushka); MARRE (Michel); MANCIA (Giuseppe); MOGENSEN (Carl E.); POULTER (Neil); CHALMERS (John)</AU>
<AF>The George Institute for Global Health/Australie (1 aut., 2 aut., 3 aut., 4 aut., 5 aut., 6 aut., 7 aut., 8 aut., 9 aut., 14 aut.); Concord Repatriation General Hospital/Sydney/Australie (1 aut.); School of Public Health, Monash University/Melbourne/Australie (7 aut.); Service d'Endocrinologie Diabétologie Nutrition, Groupe Hospitalier Bichat-Claude Bernard/Paris/France (10 aut.); Department of Clinical Medicine and Prevention, University of Milano-Bicocca/Milan/Italie (11 aut.); Medical Department M, Aarhus University Hospital, Aarhus Sygehus/Aarhus C/Danemark (12 aut.); International Centre for Circulatory Health, National Heart and Lung Institute, Imperial College London/London/Royaume-Uni (13 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>American journal of kidney diseases; ISSN 0272-6386; Etats-Unis; Da. 2012; Vol. 60; No. 5; Pp. 770-778; Bibl. 45 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>002B14E01; 002B21E01A</CC>
<FD>Diabète de type 2; Facteur prédictif; Prédiction; Néphropathie diabétique; Rein; Pronostic; Néphropathie chronique; Homme; Insuffisance rénale; Modèle; Analyse risque; Néphrologie; Urologie</FD>
<FG>Appareil urinaire; Endocrinopathie; Maladie métabolique; Pathologie de l'appareil urinaire; Pathologie du rein; Association morbide</FG>
<ED>Type 2 diabetes; Predictive factor; Prediction; Diabetic nephropathy; Kidney; Prognosis; Chronic kidney disease; Human; Renal failure; Models; Risk analysis; Nephrology; Urology</ED>
<EG>Urinary system; Endocrinopathy; Metabolic diseases; Urinary system disease; Kidney disease; Concomitant disease</EG>
<SD>Diabetes de tipo 2; Factor predictivo; Predicción; Nefropatía diabética; Riñón; Pronóstico; Nefropatía crónica; Hombre; Insuficiencia renal; Modelo; Análisis riesgo; Nefrología; Urología</SD>
<LO>INIST-19098.354000505374560280</LO>
<ID>12-0424074</ID>
</server>
</inist>
</record>

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

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

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