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Risk Prediction for Early CKD in Type 2 Diabetes.
Clin J Am Soc Nephrol. 2015 Aug 07; 10(8):1371-9.CJ

Abstract

BACKGROUND AND OBJECTIVES

Quantitative data for prediction of incidence and progression of early CKD are scarce in individuals with type 2 diabetes. Therefore, two risk prediction models were developed for incidence and progression of CKD after 5.5 years and the relative effect of predictors were ascertained.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Baseline and prospective follow-up data of two randomized clinical trials, ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) and Outcome Reduction with Initial Glargine Intervention (ORIGIN), were used as development and independent validation cohorts, respectively. Individuals aged ≥55 years with type 2 diabetes and normo- or microalbuminuria at baseline were included. Incidence or progression of CKD after 5.5 years was defined as new micro- or macroalbuminuria, doubling of creatinine, or ESRD. The competing risk of death was considered as an additional outcome state in the multinomial logistic models.

RESULTS

Of the 6766 ONTARGET participants with diabetes, 1079 (15.9%) experienced incidence or progression of CKD, and 1032 (15.3%) died. The well calibrated, parsimonious laboratory prediction model incorporating only baseline albuminuria, eGFR, sex, and age exhibited an externally validated c-statistic of 0.68 and an R(2) value of 10.6%. Albuminuria, modeled to depict the difference between baseline urinary albumin/creatinine ratio and the threshold for micro- or macroalbuminuria, was mostly responsible for the predictive performance. Inclusion of clinical predictors, such as glucose control, diabetes duration, number of prescribed antihypertensive drugs, previous vascular events, or vascular comorbidities, increased the externally validated c-statistic and R(2) value only to 0.69 and 12.1%, respectively. Explained variation was largely driven by renal and not clinical predictors.

CONCLUSIONS

Albuminuria and eGFR were the most important factors to predict onset and progression of early CKD in individuals with type 2 diabetes. However, their predictive ability is modest. Inclusion of demographic, clinical, and other laboratory predictors barely improved predictive performance.

Links

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Authors+Show Affiliations

Dunkler D
Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada; Department of Nephrology, Universitaetsklinikum Erlangen, Erlangen, Germany; Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria; Daniela.Dunkler@phri.ca Rainer.Oberbauer@meduniwien.ac.at.
Gao P
Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada;
Lee SF
Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada;
Heinze G
Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria;
Clase CM
McMaster University, Hamilton, Ontario, Canada;
Tobe S
Sunnybrook Health Sciences Center, Toronto, Ontario, Canada;
Teo KK
Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada; McMaster University, Hamilton, Ontario, Canada;
Gerstein H
Population Health Research Institute, Hamilton Health Sciences/McMaster University, Hamilton, Ontario, Canada;
Mann JF
Department of Nephrology, Universitaetsklinikum Erlangen, Erlangen, Germany; Schwabing General Hospital and KfH Kidney Center, Munich, Germany;
Oberbauer R
Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria; Hospital Elisabethinen Linz, Linz, Austria; and Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria Daniela.Dunkler@phri.ca Rainer.Oberbauer@meduniwien.ac.at.
ONTARGET and ORIGIN Investigators
No affiliation info available

MeSH

AgedAlbuminuriaBiomarkersCreatinineDecision Support TechniquesDiabetes Mellitus, Type 2Diabetic NephropathiesDisease ProgressionFemaleGlomerular Filtration RateHumansIncidenceKidneyLogistic ModelsMaleMiddle AgedMultivariate AnalysisOdds RatioPredictive Value of TestsRenal Insufficiency, ChronicReproducibility of ResultsRisk AssessmentRisk FactorsTime Factors

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

26175542

Citation

Dunkler, Daniela, et al. "Risk Prediction for Early CKD in Type 2 Diabetes." Clinical Journal of the American Society of Nephrology : CJASN, vol. 10, no. 8, 2015, pp. 1371-9.
Dunkler D, Gao P, Lee SF, et al. Risk Prediction for Early CKD in Type 2 Diabetes. Clin J Am Soc Nephrol. 2015;10(8):1371-9.
Dunkler, D., Gao, P., Lee, S. F., Heinze, G., Clase, C. M., Tobe, S., Teo, K. K., Gerstein, H., Mann, J. F., & Oberbauer, R. (2015). Risk Prediction for Early CKD in Type 2 Diabetes. Clinical Journal of the American Society of Nephrology : CJASN, 10(8), 1371-9. https://doi.org/10.2215/CJN.10321014
Dunkler D, et al. Risk Prediction for Early CKD in Type 2 Diabetes. Clin J Am Soc Nephrol. 2015 Aug 7;10(8):1371-9. PubMed PMID: 26175542.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Risk Prediction for Early CKD in Type 2 Diabetes. AU - Dunkler,Daniela, AU - Gao,Peggy, AU - Lee,Shun Fu, AU - Heinze,Georg, AU - Clase,Catherine M, AU - Tobe,Sheldon, AU - Teo,Koon K, AU - Gerstein,Hertzel, AU - Mann,Johannes F E, AU - Oberbauer,Rainer, AU - ,, Y1 - 2015/07/14/ PY - 2014/10/16/received PY - 2015/5/4/accepted PY - 2015/7/16/entrez PY - 2015/7/16/pubmed PY - 2016/5/11/medline KW - diabetes mellitus KW - glomerular filtration rate KW - proteinuria SP - 1371 EP - 9 JF - Clinical journal of the American Society of Nephrology : CJASN JO - Clin J Am Soc Nephrol VL - 10 IS - 8 N2 - BACKGROUND AND OBJECTIVES: Quantitative data for prediction of incidence and progression of early CKD are scarce in individuals with type 2 diabetes. Therefore, two risk prediction models were developed for incidence and progression of CKD after 5.5 years and the relative effect of predictors were ascertained. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Baseline and prospective follow-up data of two randomized clinical trials, ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) and Outcome Reduction with Initial Glargine Intervention (ORIGIN), were used as development and independent validation cohorts, respectively. Individuals aged ≥55 years with type 2 diabetes and normo- or microalbuminuria at baseline were included. Incidence or progression of CKD after 5.5 years was defined as new micro- or macroalbuminuria, doubling of creatinine, or ESRD. The competing risk of death was considered as an additional outcome state in the multinomial logistic models. RESULTS: Of the 6766 ONTARGET participants with diabetes, 1079 (15.9%) experienced incidence or progression of CKD, and 1032 (15.3%) died. The well calibrated, parsimonious laboratory prediction model incorporating only baseline albuminuria, eGFR, sex, and age exhibited an externally validated c-statistic of 0.68 and an R(2) value of 10.6%. Albuminuria, modeled to depict the difference between baseline urinary albumin/creatinine ratio and the threshold for micro- or macroalbuminuria, was mostly responsible for the predictive performance. Inclusion of clinical predictors, such as glucose control, diabetes duration, number of prescribed antihypertensive drugs, previous vascular events, or vascular comorbidities, increased the externally validated c-statistic and R(2) value only to 0.69 and 12.1%, respectively. Explained variation was largely driven by renal and not clinical predictors. CONCLUSIONS: Albuminuria and eGFR were the most important factors to predict onset and progression of early CKD in individuals with type 2 diabetes. However, their predictive ability is modest. Inclusion of demographic, clinical, and other laboratory predictors barely improved predictive performance. SN - 1555-905X UR - https://www.unboundmedicine.com/medline/citation/26175542/Risk_Prediction_for_Early_CKD_in_Type_2_Diabetes_ DB - PRIME DP - Unbound Medicine ER -