Chronic kidney disease risk calculator: new possibilities for predicting pathology in patients with diabetes mellitus

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Abstract

Background. Chronic kidney disease (CKD) is a socially significant pathology associated with an increased risk of mortality and progression to terminal stages, requiring expensive treatment with renal replacement therapy, which determines the priority of preventive strategies, especially in patients with diabetes mellitus (DM), who are at high risk of Kidney's injury.

Aim. To analyze the predictors of a decrease in glomerular filtration rate (GFR) less than 60 ml/min/1.73 m2 and develop an applied CKD prediction calculator in patients with type 1 and type 2 diabetes.

Materials and methods. The object of the study is a depersonalized database of the Federal Registry of DM, implemented under the auspices of the Endocrinology Research Centre. The study was based on a retrospective analysis of patients with type 1 and type 2 diabetes with different GFR dynamics over a 5-year period of 2014–2018. 68 911 patients were included (type 1 DM – 7919 and type 2 DM – 60 992). Stepwise logistic regression analysis was used to predict the risk of developing CKD.

Results. Sets of the most significant predictors of CKD development were established, which included 6 factors in type 1 DM: female sex, age, body mass index, the presence of myocardial infarction, diabetic coma and retinopathy, and 11 factors in type 2 DM: female sex, age, body mass index, glycated hemoglobin, baseline GFR and total cholesterol, presence of diabetic retinopathy, neuropathy, stroke, amputations and oncology in anamnesis.

Conclusion. An applied interactive CKD prognosis calculator has been developed that allows assessing the individual risk of developing pathology in patients with type 1 and type 2 diabetes based on the parameters available in routine clinical practice. The calculator has been introduced into the system of the Federal Registry of DM, which significantly expands the possibilities of diagnosing and monitoring CKD in DM.

About the authors

Olga K. Vikulova

Endocrinology Research Centre

Email: olga-vikulova-1973@yandex.ru
ORCID iD: 0000-0003-0571-8882
SPIN-code: 9790-2665

D. Sci. (Med.)

Russian Federation, Moscow

Alina R. Elfimova

Endocrinology Research Centre

Email: 9803005@mail.ru
ORCID iD: 0000-0001-6935-3187

cybernetic doctor

Russian Federation, Moscow

Anna V. Zheleznyakova

Endocrinology Research Centre

Email: azhelez@gmail.com
ORCID iD: 0000-0002-9524-0124
SPIN-code: 8102-1779

Cand. Sci. (Med.)

Russian Federation, Moscow

Mikhail A. Isakov

Endocrinology Research Centre

Author for correspondence.
Email: m.isakov@aston-health.com
ORCID iD: 0000-0001-9760-1117
SPIN-code: 5870-8933

Cand. Sci. (Biol.)

Russian Federation, Moscow

Minara S. Shamkhalova

Endocrinology Research Centre

Email: shamkhalova@mail.ru
ORCID iD: 0000-0002-3433-0142
SPIN-code: 4942-5481

D. Sci. (Med.)

Russian Federation, Moscow

Marina V. Shestakova

Endocrinology Research Centre

Email: shestakova.mv@gmail.com
ORCID iD: 0000-0002-5057-127X
SPIN-code: 7584-7015

D. Sci. (Med.), Prof., Acad. RAS

Russian Federation, Moscow

Natalia G. Mokrysheva

Endocrinology Research Centre

Email: mokrisheva.natalia@endocrincentr.ru
ORCID iD: 0000-0002-9717-9742
SPIN-code: 5624-3875

D. Sci. (Med.), Prof., Corr. Memb. RAS

Russian Federation, Moscow

References

Supplementary files

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1. JATS XML
2. Figure 1. CKD predictor analysis study design.

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3. Figure 2. The prevalence of CKD in patients with DM in age groups (children, adolescents, adults) in dynamics in 2013–2018

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4. Figure 3. CKD Prognosis Calculator implemented in the FDRF.

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