Literature DB >> 33035555

Renal resistive index in IgA nephropathy and renal scleroderma vasculopathy.

Rosario Cianci1, Antonietta Gigante1, Domenico Bagordo1, Giovanni Pintus1, Antonello Giovannetti1, Silvia Lai1, Sandro Mazzaferro1, Edoardo Rosato2.   

Abstract

BACKGROUND: Renal Ultra-Sound (US) and Doppler US provide measurements which reflect changes in renal and systemic haemodynamic. The renal resistive index (RRI), obtained through the Doppler spectrum analysis of renal small arteries, is altered in several pathologic conditions. Glomerulonephritis cause minor RRI changes, while renal scleroderma vasculopathy (RSV) leads to significant RRI modifications. The aim of our study was to assess RRI in IgA nephropathy (IgAN) and RSV in a retrospective observational study and to investigate determinants of the RRI in these groups of patients.
METHODS: We enrolled 61 IgAN patients [23 female, median age 41 (33-58) years] and 80 SSc patients [71 female, median age 52 (43-60) years]. RRI was evaluated in all patients at the time of enrolment. Laboratory tests and clinical assessment were evaluated in all patients.
RESULTS: IgAN patients showed lower RRI values than RSV patients [0.70 (0.65-0.73) vs 0.66 (0.62-0.72), p < 0.01], while no significant difference in longitudinal length was observed. Median age was significantly lower in IgAN patients than in RSV patients [41 (33-58) vs 52 (43-60), p < 0.05] while IgAN patients showed a higher prevalence of high blood pressure than RSV patients (39.3% vs 13.8%, p < 0.01). The multiple regression analysis, weighted for age, showed that RRI inversely correlates with estimated glomerular filtration rate (β coefficient = -0.524, p < 0.0001).
CONCLUSION: Higher RRI were found in RSV patients than IgAN patients. IgAN is characterized mainly by glomerular injury, not leading to major RRI changes.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  IgA nephropathy; Renal resistive index; Renal scleroderma vasculopathy; Renal ultrasound; Systemic sclerosis

Mesh:

Year:  2020        PMID: 33035555     DOI: 10.1016/j.mvr.2020.104095

Source DB:  PubMed          Journal:  Microvasc Res        ISSN: 0026-2862            Impact factor:   3.514


  1 in total

1.  Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image.

Authors:  Fu Ying; Shuhua Chen; Guojun Pan; Zemin He
Journal:  J Healthc Eng       Date:  2021-07-20       Impact factor: 2.682

  1 in total

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