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. 2020 Aug 19;34(1):165–172. doi: 10.1007/s40620-020-00818-w

Table 2.

Multivariable binary logistic regression models for rapid kidney function decline

Rapid kidney

Function decline

Basic model

Age years

Gender

Smoking

HbA1c rel. %

SBP mmHg

LDL mg/dL

logCRP mg/dL

Adjusted R2 = 03.2% AUC = 0.602
Model 1

+ eGFR mL/min/1.73m2

+ logUACR mg/g

Adjusted R2 = 09.9%

AUC = 0.670

Model 2

+ eGFR mL/min/1.73m2

+ logUACR mg/g

+ VPO1 ng/mL

Adjusted R2 = 15.6%

AUC = 0.722

Inclusion of renal parameters (model 1), as well as of VPO1 (model 2) improved rapid decline risk discrimination beyond parameters enumerated in the basic model.

Statistical analyses included the multivariable binary logistic regression analyses and the adjusted pseudo-R2 measure Nagelkerke; constructing a ROC curve, the area under the curve was computed.

AUC area under the curve, eGFR estimated glomerular filtration rate, HbA1c glycated hemoglobin A1c, LDL low-density lipoprotein, logCRP logarithmised C-reactive protein, logUACR logarithmised urine albumin-to-creatinine ratio, R2 coefficient of determination, ROC receiver operating characteristic, SBP systolic blood pressure