Renal resistive index in IgA nephropathy and renal scleroderma vasculopathy

Changes in renal microcirculation in patients with nephrotic and nephritic syndrome: The role of resistive index

BACKGROUND

Renal Resistive Index (RRI) is an important and non-invasive parameter of renal damage and it is associated with abnormal microcirculation or to a parenchymal injury. The aim of our study was to compare the RRI in a cohort of patients with renal diseases categorized in three groups: nephrotic syndrome (NS), acute nephritic syndrome (ANS) and patients with urinary abnormalities (UA).

METHODS

Four hundred eighty-two patients with median age of 48 years (IQR 34-62) with indications for kidney disease were included in the study. Biochemical analyses, clinical assessment with detection of NS, ANS and UA and comorbidities were reported. Renal Doppler ultrasound with RRI was evaluated in all patients at the time of enrolment.

RESULTS

NS was present in 81 (16.8 %) patients while ANS in 81 (16.8 %) and UA in 228 (47.3 %) patients. Patients with ANS showed significant higher RRI compared to both patients with NS [0.71 (IQR 0.67-0.78) vs 0.68 (0.63-0.73), p < 0.001] and UA [0.71 (0.67-0.78) vs 0.65 (0.61-0.71), p < 0.001]; RRI was higher in NS patients than in patients with UA [0.68 (0.63-0.73) vs 0.65 (0.61-0.71), p < 0.001]. Patients with ANS had significantly lower median estimated glomerular filtration rate (eGFR) compared respectively to NS and UA patients [19.7 ml/min vs 54.8 ml/min and vs 72.3 ml/min, p < 0.001], while renal length was significantly higher in patients with NS compared to both patients with ANS and UA [111.88 mm vs 101.98 mm and vs 106.15, p < 0.001]. Patients with ANS had more frequently hematuria and RRI ≥ 0.70 (p < 0.001) compared to both patients with NS and patients with UA. The multiple regression analysis, weighted for age, showed that RRI inversely correlates with eGFR (β coefficient = -0.430, p < 0.001).

CONCLUSIONS

Higher and pathological RRI were found in ANS than NS and UA. Renal resistive index in ANS reflects changes in intrarenal perfusion and microvascular dysfunction related to disease characteristics.

Noninvasive evaluation of IgA nephropathy fibrosis using Doppler ultrasound

In this study, we aimed to explore the clinical value of routine color ultrasound parameters in the evaluation of tubular atrophy and interstitial fibrosis (TA/IF) in IgA nephropathy (IgAN). We enrolled 725 patients with IgAN who underwent renal biopsy at the First Affiliated Hospital of Anhui Medical University between January 2019 and May 2022. Examinations were performed to measure the routine ultrasound renal parameters and renal biopsy was done within next three days. Univariate and multivariate analyses were used to determine the correlates and the independent predictors of TA/IF. Simultaneously, a nomogram based on risk indicators was created to predict TA/IF. Univariate and multivariate analyses showed that sex (p < 0.001, OR = 2.538, 95%CI: 1.739–3.734), renal length (p < 0.001, OR = 0.927, 95%CI: 0.905–0.95), resistive index of main renal artery (p = 0.037, OR = 1.891, 95%CI: 1.027–3.426), peak systolic velocity of segmental renal artery (p = 0.58, OR = 0.975, 95%CI: 0.399–0.841), and cortex echogenicity (p < 0.001, OR = 3.448, 95%CI: 2.382–5.018) were independent predictors of TA/IF in IgAN nomograms, with a good C-index of 0.765 (95%CI = 0.727–0.803). Analyses of the calibration charts show that nomograms have good performance and clinical applicability. In our study, renal color ultrasound parameters correlated well with TA/IF in IgAN. By establishing a conventional color ultrasound prediction model, we can accurately gauge the extent of TA/IF in patients with IgAN for clinical applications.

Assessment of renal microcirculation in biopsy-proven tubulointerstitial nephritis in patients with and without glomerular disease: the role of resistive index

BACKGROUND

Renal resistive index (RRI) measured by Doppler sonography is a marker of microvascular status and it is associated with changes in renal function. Aim of the study was to assess RRI in biopsy-proven tubulointerstitial nephritis (TIN) in patients with and without glomerular disease.

METHODS

132 consecutive patients underwent to native renal biopsy with diagnosis of isolated TIN or in association with glomerulonephritis. Estimated glomerular filtration rate (eGFR), 24-hour urinary protein excretion and renal ecocolorDoppler ultrasonography with RRI assessment were performed at time of enrollment.

RESULTS

Patients with isolated-TIN had significantly higher RRI than both patients with non-immunoglobulin A glomerulonephritis (non-IgA-TIN) [0.73 (0.68-0.77) vs 0.64 (0.60-0.67), p < 0.001] and patients with IgA nephropathy (IgAN) [0.73 (0.68-0.77) vs 0.66 (0.60-0.71), p < 0.01]. Patients with isolated-TIN had mainly RRI ≥ 0.70 (n = 15, 65.2%) with the respect to patients with non-IgA-TIN (n = 7, 12.3%) and patients with IgAN (n = 17, 32.7%). A negative linear correlation was found between RRI and hemoglobin (r = 0.233, p < 0.01) and between RRI and eGFR (r = 0.537, p < 0.001).

CONCLUSION

Tubulointerstitial damage is the most accurate histological lesion that correlates with eGFR and renal impairment. RRI can be a useful parameter to detect tubulointerstitial lesions.

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

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p=0.000 < 0.05. Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p=0.128 > 0.05). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2^nd day, 4^th day, and 6^th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 (p < 0.05). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.