Renal Survival and Validation of Novel International Immunoglobulin A Nephropathy Prediction Tool in Latvian Population: Preliminary Data

Anna Jana Saulīte (Corresponding Author), Anna Popova, Renārs Erts, Kārlis Rācenis, Linda Kučāne, Aivars Pētersons, Arvydas Laurinavičius, Harijs Čerņevskis, Viktorija Kuzema

Research output: Contribution to journalArticlepeer-review

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Abstract

The aim of the study was to determine kidney survival and validate the novel international immunoglobulin A nephropathy (IgAN) prediction tool (PT) in the Latvian population. Adults with morphologically confirmed IgAN were included. Kidney survival was analysed with the Kaplan–Meier method. PT-assigned risk was compared with calculated risk by the Cox regression model. The
Kaplan–Meier analysis included 95 patients. The five-year kidney survival Q3 was 24 months. Women had longer median kidney-survival time (> 60 months) than men (58 months). Median kidney survival in participants with MEST T0 was longer than 60 months; T1 and T2 were 40 and 18 months, respectively. Median kidney survival in participants with diastolic blood pressure (DBP) < 99 mmHg was longer than 60 months, whereas in patients with DBP 100–109 and 110
mmHg, it was 40 and 24 months, respectively. Cox regression analysis included 68 patients. A moderate degree of correlation was found between predicted and observed five-year risk (p = 0.001). Gender, tubular atrophy/interstitial fibrosis, DBP are significant factors affecting kidney survival. Since there was statistically significant correlation and reliability between PT and follow-up analysis data, we conclude that PT could be applied for use in the Latvian population
Original languageEnglish
Pages (from-to)379-386
Number of pages8
JournalProceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.
Volume75
Issue number5
DOIs
Publication statusPublished - Oct 2021

Keywords*

  • end-stage kidney disease
  • immunoglobulin A nephropathy
  • kidney survival risk factors
  • novel IgAN prediction tool

Field of Science*

  • 3.2 Clinical medicine

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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