Evaluation of the nephroprotective strategy effectiveness in the late stages of CKD

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BACKGROUND: The effectiveness of conventional nephroprotection is reduced at the late stages of chronic kidney disease; the search for effective algorithms is hampered by accelerating decline in glomerular filtration rate. There are no generally accepted ways to evaluate the effectiveness of conventional nephroprotection.

AIM: To build a model for predicting the glomerular filtration decline rate to assess the effectiveness of the intensive follow-up.

MATERIALS AND METHODS: A representative group of regular follow-up (n = 540) was selected from the city database (n = 7696) to built-up the model that predicts glomerular filtration annual decline rate. This model is used to evaluate the effectiveness of intensive monitoring (n = 100) by the difference between predicted and actual glomerular filtration rate decline. The corresponding subgroup (n = 200) was used for direct comparison of hard and surrogate outcomes.

RESULTS: A year before dialysis is required, the glomerular filtration rate decline in intensive group was 5.98 ± 1.69 vs. the predicted 9.06 ± 0.59 ml/min/1.73 m2/year. The assessment of the intervention effectiveness has been used as a dependent variable in regression and categorical analysis. Significant components of the nephroprotection including phosphatemia decrease (0.25 mmol/l), hemoglobin increase (1 g/dl), effective administration of renin-angiotensin-aldosterone system blockers (proteinuria reduce by 0.1 g/l), systolic blood pressure decrease (5 mm Hg), calcemia deviations from the target decrease (0.1 mmol/l), acidosis correction (2 mmol/l), inflammation reduction and albumin increase (1.5 g/l) which were associated with the smaller glomerular filtration rate decrease rate by 15%. The intensive therapy group had the dialysis risk was 2.2 times smaller, and the death risk was 4 times smaller. The planned dialysis was ensured in the intensive therapy group; 67% chose peritoneal dialysis.

CONCLUSIONS: The prediction of glomerular filtration decline rate calculated by nonlinear model in comparison with the actual one can evaluate the nephroprotection effectiveness; it differs significantly from the conventional ones at the late stages of chronic kidney disease.

作者简介

Daria S. Sadovskaya

North-Western state medical university named after I.I.Mechnikov

Email: dssadovskaya@gmail.com
ORCID iD: 0000-0002-1903-2630
SPIN 代码: 1304-5441

Postgraduate student of the Department of Internal Medicine, Clinical Pharmacology and Nephrology

俄罗斯联邦, 191015, 41, Kirochnaya str., Saint Petersburg, Russia

Konstantin A. Vishnevsky

North-Western state medical university named after I.I.Mechnikov; City Mariinsky Hospital

Email: vishnevskii2022@mail.ru
ORCID iD: 0000-0001-6945-4711
SPIN 代码: 4417-0736
Scopus 作者 ID: 56841508800

PhD, Assistant of the Department of Internal Diseases, Clinical Pharmacology and Nephrology; head of dialysis unit

俄罗斯联邦, 41 Kirochnaya str., Saint Petersburg, 191015, Russia; 56 Liteyny Ave., Saint Petersburg, Russia, 191014

Irina Konakova

City Mariinsky hospital

Email: inkonakova@yandex.ru
ORCID iD: 0000-0003-4564-5809
SPIN 代码: 8560-9861

Deputy Chief Physician

俄罗斯联邦, 191014, 56 Liteyny Ave., Saint Petersburg, Russia

Olga R. Golubeva

City Mariinsky hospital; Saint Petersburg State Pediatric Medical University

Email: 12golubevaolga@gmail.com
ORCID iD: 0000-0003-2078-7747
SPIN 代码: 4866-1590

nephrologist of the Dialysis unit; assistant of the Department of Propaedeutics of Internal Diseases

俄罗斯联邦, 191014, 56 Liteyny Ave., Saint Petersburg, Russia; 194100, St. Petersburg, Litovskaya str., 2

Natalya V. Bakulina

North-West State Medical University named after I.I. Mechnikov

编辑信件的主要联系方式.
Email: nv_bakulina@mail.ru
ORCID iD: 0000-0003-4075-4096
SPIN 代码: 9503-8950
Scopus 作者 ID: 7201739080
Researcher ID: N-7299-2014
http://www.researcherid.com/rid/N-7299-2014

MD, Dr. Sci. (Med.); Head of the Department of Internal Diseases, Clinical Pharmacology and Nephrology, Vice-Rector for Science and Innovation

俄罗斯联邦, 191015, 41 Kirochnaya St., Saint Petersburg, Russia

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2. Fig. 1. The number of individual from/to certain levels of glomerular filtration rate (GFR) used to create a model for predicting the progression rate. A, Б, В, Г — groups of the patients followed up to GFR ranges of 10–14, 15–19, 20–24, 25–29 ml/min/1.72 m2, respectively; α, β, γ — followed from the GFR ranges of 44–40, 39–35, 34–30 ml/min/1.72 m2, respectively

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3. Fig. 2. Function for predicting estimated glomerular filtration rate decline with the current estimated glomerular filtration rate in patients with “standard” follow-up (n = 540). GFR — glomerular filtration rate; N — number of patients

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4. Fig. 3. A model of multiple regression analysis with a dependent variable “the effect of intensive monitoring on reducing estimated glomerular filtration rate decline”

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