Renal length z-score for the detection of dysfunction in children with solitary functioning kidney

Kidney volume normative values in Central European children aged 0-19 years: a multicenter study

BACKGROUND AND OBJECITVES

The currently available kidney volume normative values in children are restricted to small populations from single-centre studies not assessing kidney function and including none or only a small number of adolescents. This study aimed to obtain ultrasound-based kidney volume normative values derived from a large European White/Caucasian paediatric population with normal kidney function.

METHODS

After recruitment of 1427 children aged 0-19 years, 1396 individuals with no history of kidney disease and normal estimated glomerular filtration rate were selected for the sonographic evaluation of kidney volume. Kidney volume was correlated with age, height, weight, body surface area and body mass index. Kidney volume curves and tables related to anthropometric parameters were generated using the LMS method. Kidney volume predictors were evaluated using multivariate regression analysis with collinearity checks.

RESULTS

No clinically significant differences in kidney volume in relation to height were found between males and females, between supine and prone position and between left and right kidneys. Males had, however, larger age-related kidney volumes than females in most age categories. For the prediction of kidney volume, the highest coefficient correlation was observed for body surface area (r = 0.94), followed by weight (r = 0.92), height (r = 0.91), age (r = 0.91), and body mass index (r = 0.67; p < 0.001 for all).

CONCLUSIONS

This study presents LMS-percentile curves and tables for kidney volume which can be used as reference values for children aged 0-19 years.

A Pilot Comparative Study between Creatinine- and Cystatin-C-Based Equations to Estimate GFR and Kidney Ultrasound Percentiles in Children with Congenital Anomalies of the Kidney and Urinary Tract

Congenital anomalies affecting the kidneys present significant challenges in pediatric nephrology, needing precise methods for assessing renal function and guiding therapeutic intervention. Bedside Schwartz formula with the cystatin-C-based Full Age Spectrum formula and Chronic Kidney Disease in Children (CKiD) U 25 formula used in estimating glomerular filtration rate (eGFR) and also to assess if the eGFR in association with kidney length percentiles can be a monitoring parameter for the progression of chronic kidney disease in children with congenital anomalies of the kidney and urinary tract (CAKUT). A total of 64 pediatric patients (median age at diagnostic was 12 months with an interquartile range of 2 to 60) were diagnosed with congenital anomalies in the kidney and urinary tract between June 2018 and May 2023 at "Louis Turcanu" Emergency Hospital for Children in Timisoara, Romania. Baseline characteristics, CAKUT types, associated pathologies, CKD staging, and eGFR using creatinine and cystatin C were analyzed. The mean age at the moment of examination was 116.50 months; (65, 180). Chronic kidney disease staging revealed a predominance of patients in CKD stages G1 and A1. Analysis of eGFR methods revealed a small mean difference between eGFR estimated by creatinine and cystatin C, with a moderate-strong positive correlation observed between the eGFR and ultrasound parameters. Using cystatin-C-based formulas for eGFR, in conjunction with ultrasound measurements, may offer reliable insights into renal function in pediatric patients with congenital anomalies affecting the kidney and urinary tract. However, the economic aspect must be taken into consideration because cystatin C determination is approximately eight times more expensive than that of creatinine. An interdisciplinary approach is crucial for managing patients with CAKUT.

Predicting outcomes in children with congenital anomalies of the kidney and urinary tract

BACKGROUND

Congenital anomalies of the kidney and urinary tract (CAKUT) are the most frequent causes of childhood chronic kidney disease (CKD). Using a large CAKUT cohort, we sought to identify the predictors of CKD and to develop a prediction model that informs a risk-stratified clinical pathway.

METHODS

This was a retrospective cohort study including cases with multicystic dysplastic kidneys (MCDK), unilateral kidney agenesis (UKA), kidney hypoplasia (KH), and posterior urethral valves (PUV). We identified risk factors for CKD (estimated glomerular filtration rate [eGFR] <60 ml/min/1.73 m2) and tested their performance in an adjusted multivariate binary regression model. Prediction probability scores for CKD were used to separate cases likely to develop complications from those not needing specialist follow-up.

RESULTS

We identified 452 eligible cases of CAKUT with 22% developing CKD. Strongest associations with CKD included primary diagnosis (OR 3.5, 95% CI 2.6-4.6), preterm delivery (OR 2.3, 95% CI 1.2-4.4), non-kidney anomalies (OR 1.8, 95% CI 1.1-3), first eGFR<90 (OR 8.9, 95% CI 4.4-18.1), small kidney size (OR 9, 95% CI 4.9-16.6), and additional kidney anomalies (OR 1.6, 95% CI 1.2-2.8). PUV (OR 4.7, 95% CI 1.5-15.3), first eGFR <90 (OR 4.4, 95% CI 2-9.7), and kidney length to body length ratio <7.9 (OR 4.2, 95% CI 1.9-9.2) were independent predictors of CKD. The regression model had a prediction accuracy of 80% and a prediction probability c-statistic of 0.81.

CONCLUSION

Using a large combined CAKUT cohort we identified risk factors for CKD. Our prediction model provides the first steps towards a risk-stratified clinical pathway. A higher resolution version of the Graphical abstract is available as Supplementary information.

Artificial Intelligence in Pediatric Nephrology-A Call for Action

Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.

Body surface area-based kidney length percentiles misdiagnose small kidneys in children with overweight/obesity

BACKGROUND

We evaluated the diagnostic performance of height-, age- and body surface area (BSA)-based kidney length (KL) percentiles in the identification of at least one small kidney (KL < 3^rd) and in the prediction of reduced estimated glomerular filtration rate (eGFR) and/or elevated blood pressure (BP) in children with and without overweight (OW)/obesity(OB).

METHODS

In this cross-sectional study, 744 apparently healthy children (mean age 8.3 years) were recruited in a primary care setting. Clinical data were collected, and serum creatinine and KL were measured. Height-, age- and BSA-based percentiles of KL were calculated and the association of at least one small kidney per subject with reduced eGFR and/or elevated BP was explored by logistic regression.

RESULTS

Two hundred fifty-seven out of seven hundred forty-four (34.5%) subjects were OW/OB and 127 (17.1%) had reduced eGFR or elevated BP. In separate analyses in children with OW/OB, the KL percentiles calculated on the basis of BSA were lower compared with height- and age-based KL percentiles. Consequently, the prevalence of a small kidney was significantly higher when evaluating percentiles of KL based on BSA compared with other percentiles. In logistic regression analysis, a small kidney was significantly associated with reduced eGFR and/or elevated BP only when using height-based KL percentiles. The KL percentiles according to BSA for the ideal weight (iBSA) showed similar performance compared with height-based percentiles. No differences in the diagnostic performance of different percentiles were found in children with normal weight.

CONCLUSIONS

BSA-based percentiles underestimate KL in children with OW/OB. In these subjects, the use of height-based or iBSA-based percentiles should be preferred. A higher resolution version of the Graphical abstract is available as Supplementary information.

Outcomes of solitary functioning kidneys-renal agenesis is different than multicystic dysplastic kidney disease

BACKGROUND

Multicystic dysplastic kidney (MCDK) disease and unilateral renal agenesis (URA) are well-known causes of a solitary functioning kidney (SFK) and are associated with long-term kidney injury. The aims of this study were to characterize the natural history of SFK at our center, define the risk factors associated with chronic kidney injury, and identify distinguishing features between URA and MCDK that predict outcome.

METHODS

This was a retrospective cohort study of 230 SFK patients. We compared MCDK (n=160) and URA (n=70) according to clinical features at diagnosis and kidney outcomes over follow-up. Univariate and multivariate binary regression analysis was used to determine independent risk factors for chronic kidney injury, defined as the composite outcome of hypertension, proteinuria, or chronic kidney disease (eGFR <60 mL/min/1.73m²).

RESULTS

URA had a higher prevalence of comorbid genetic syndromes (15 vs. 6%, p=0.04), non-renal anomalies (39 vs. 11%, p<0.001), and congenital anomalies of the kidney and urinary tract (CAKUT) (51 vs. 26%, p<0.001) than MCDK. Over follow-up, URA experienced more hypertension (19 vs. 3%, p=0.002), proteinuria (12 vs. 3%, p=0.03), and the composite outcome (19 vs. 6%, p=0.003) than MCDK. Independent risk factors for chronic kidney injury included CAKUT (OR 5.01, p=0.002) and URA (OR 2.71, p=0.04).

CONCLUSIONS

In our population, URA was more likely to have associated syndromes or anomalies, and to have worse outcomes over time than MCDK. URA diagnosis was an independent risk factor for chronic kidney injury. Our results will be used to develop a standardized clinical pathway for SFK management. A higher resolution version of the Graphical abstract is available as Supplementary information.