Evaluation of the nephroprotective strategy effectiveness in the late stages of chronic kidney disease

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Abstract

Background. Classical nephroprotection reduces its effectiveness at the late CKD stages; the search for effective algorithms is hampered by accelerating decline in GFR, therefore there are no generally accepted ways to evaluate the effectiveness.

Aim: to build a model for predicting the GFR decline rate in order to assess the effectiveness of the intensive follow-up.

Methods. A representative group of regular follow-up (N=540) was allocated from the city database (N=7696) to built-up the polynomial model that predicts GFR annual decline. We used the model to evaluate the intensive monitoring effectiveness (N=100) by the difference between predicted and actual rates of GFR decline. We also selected well matched subgroup of 200 patients for direct comparison of hard and surrogate outcomes.

Results. During last year before need in dialysis, the rate of GFR decline in intensive group was 5.98±1.69 vs. the predicted 9.06±0.59ml/min/1.73 m²/year. We used that assessment of the intervention effectiveness as dependent variable in regression and categorical analysis. The significant components of the nephroprotection: phosphatemia decrease (0.25 mmol/l), hemoglobin increase (1 g/dl), effective administration of RAAS blockers (to reduce proteinuria by 0.1 g/l), systolic blood pressure decrease (5 mmHg), calcemia deviations decrease from the target (0.1 mmol/l), acidosis correction (2 mmol/l), inflammation reduction and albumin increase (1.5 g/l) -were associated with the smaller GFR decrease rate by 15%. In intensive group, the dialysis risk was 2.2 times lower, the death risk was 4 times. The only planned dialysis start was ensured in intensive group, 67% chose peritoneal dialysis.

Conclusions. The prediction of GFR decline rate calculated by nonlinear model in comparison with the actual one can evaluate the nephroprotection effectiveness; it differs significantly from the classical ones at the CKD late stages.

About the authors

Daria S. Sadovskaya

North-Western State Medical University named after I.I. Mechnikov; City Mariinsky Hospital

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

MD, PhD student

Russian Federation, 41 Kirochnaya St., Saint Petersburg, 191015; Saint Petersburg

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-code: 4417-0736
Scopus Author ID: 56841508800

MD, Cand. Sci. (Med.)

Russian Federation, 41 Kirochnaya St., Saint Petersburg, 191015; Saint Petersburg

Irina N. Konakova

City Mariinsky hospital

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

MD

Russian Federation, Saint Petersburg

Olga R. Golubeva

City Mariinsky hospital; Saint Petersburg State Pediatric Medical University

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

MD

Russian Federation, Saint Petersburg; St. Petersburg

Natalya V. Bakulina

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

Author for correspondence.
Email: nv_bakulina@mail.ru
ORCID iD: 0000-0003-4075-4096
SPIN-code: 9503-8950
Scopus Author ID: 7201739080
ResearcherId: N-7299-2014
http://www.researcherid.com/rid/N-7299-2014

MD, Dr. Sci. (Med.), Professor

Russian Federation, 41 Kirochnaya St., Saint Petersburg, 191015

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Supplementary files

Supplementary Files
Action
1. JATS XML
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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