Longitudinal Changes and Predictive Value of the Renal Resistive Index in the General Population
Year: 2025
Type: article
Study design: Observational Study
Abstract: AIM: Haemodynamic imaging markers like the renal-resistive index (RRI) provide insights into cardiovascular-renal interactions. However, longitudinal epidemiological data on the RRI's natural history, correlates and predictive value are lacking. We investigated factors associated with longitudinal RRI changes (ΔRRI) and its predictive value for cardiovascular events in the community. METHODS: In this prospective observational community-based cohort study, 594 adults underwent renal ultrasonography for RRI assessment and were followed for major cardiovascular disease incidence for 9.1 ± 2.3 years. A subset of 135 (22.7%) had a second RRI measurement after 8.1 ± 1.6 years. Stepwise regression identified clinical correlates of ΔRRI, while multivariable Cox models assessed the association between major cardiovascular events and baseline RRI. RESULTS: Over 8 years, mean RRI increased from 0.60 ± 0.05 to 0.63 ± 0.06 (p < 0.001). Greater ΔRRI was independently associated with older age, higher pulse pressure and diabetes mellitus at baseline and with greater increase in pulse pressure during follow-up. Baseline RRI was associated with major cardiovascular events in univariate analysis, but this association was mostly explained by age and was no longer significant after adjustment for confounders (p = 0.51). Neither the highest RRI tertile (HR: 1.22 (0.51 to 2.94), p = 0.65) nor an RRI above 0.70 (HR: 1.39 (0.70 to 2.76), p = 0.35) predicted major cardiovascular events after full adjustment. CONCLUSION: This study provides the first longitudinal description of the RRI's natural history in a general adult population. RRI increased stronger over time with ageing, higher pulsatility and diabetes mellitus. However, its limited predictive value for cardiovascular events disfavours its use for risk stratification in the community.
Source: Nephrology
DOI: 10.1111/nep.70068
Institutions: KU Leuven, Universitair Ziekenhuis Leuven
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1683220021, Integrative personalized computer modeling for better cardiovascular health assessment, G4009809703, Machine learning platform for better cardiovascular health assessment and risk stratification, Reclassification of kidney transplant rejection and the role of innate allorecognition., G4718095871
OpenAlex ID: W4411928156
