With the increasing number of oncology cases and a parallel surge in chemotherapeutic drugs for treatment, the treating physicians conducts nephrotoxicity evaluation to provide a personalized dosing strategy. Of the various tests available, glomerular filtration rate (GFR) under gamma camera with help of Gates method has gained importance, being a good index of overall kidney functions. In addition to this, there has been an alternate and old method for GFR estimation: plasma sampling. We at our Institution conducted both the methods for better evaluation of GFR in cancer patient management.

Comparison of Gates' camera based GFR based on kidney depth correction using Tonessen's method and CT based manual depth calculation with dual time point plasma sampling in cancer patients.

A retrospective study wherein patients' database were evaluated over a period of four months after approval from our Institutional Review Board. Thirty patients were included in the study. GFR was evaluated by two methods: Gates camera based and dual time plasma sampling method. Statistical analysis was done to help evaluate a correlation coefficient between the methods (Gates' method with and without CT based manual depth correction and dual time point plasma sampling).

Our study showed moderate correlation between Gates' camera based GFR and dual time plasma sampling method.

One need to understand the limitation of each method and see if the renal depth corrections can be done with the help of CT or lateral images of NM for near accurate GFR and in case of selecting dual plasma sampling, errors to be minimized in pipetting and sample counting. Hence, it will be better to use both the methods for coming to a conclusion.

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.

The regulation of body fluid balance is a key concern in health and disease and comprises three concepts. The first concept pertains to the relationship between total body water (TBW) and total effective solute and is expressed in terms of the tonicity of the body fluids. Disturbances in tonicity are the main factor responsible for changes in cell volume, which can critically affect brain cell function and survival. Solutes distributed almost exclusively in the extracellular compartment (mainly sodium salts) and in the intracellular compartment (mainly potassium salts) contribute to tonicity, while solutes distributed in TBW have no effect on tonicity. The second body fluid balance concept relates to the regulation and measurement of abnormalities of sodium salt balance and extracellular volume. Estimation of extracellular volume is more complex and error prone than measurement of TBW. A key function of extracellular volume, which is defined as the effective arterial blood volume (EABV), is to ensure adequate perfusion of cells and organs. Other factors, including cardiac output, total and regional capacity of both arteries and veins, Starling forces in the capillaries, and gravity also affect the EABV. Collectively, these factors interact closely with extracellular volume and some of them undergo substantial changes in certain acute and chronic severe illnesses. Their changes result not only in extracellular volume expansion, but in the need for a larger extracellular volume compared with that of healthy individuals. Assessing extracellular volume in severe illness is challenging because the estimates of this volume by commonly used methods are prone to large errors in many illnesses. In addition, the optimal extracellular volume may vary from illness to illness, is only partially based on volume measurements by traditional methods, and has not been determined for each illness. Further research is needed to determine optimal extracellular volume levels in several illnesses. For these reasons, extracellular volume in severe illness merits a separate third concept of body fluid balance.

In patients with fluid retention, the plasma clearance of ⁵¹ Cr-EDTA (Cl_exp obtained by multiexponential fit) may overestimate the glomerular filtration rate (GFR). The present study was undertaken to compare a gamma-variate plasma clearance (Cl_gv) with the urinary plasma clearance of ⁵¹ Cr-EDTA (Cl_u ) in patients with cirrhosis with and without fluid retention. A total of 81 patients with cirrhosis (22 without fluid retention, 59 with ascites) received a quantitative intravenous injection of ⁵¹ Cr-EDTA followed by plasma and quantitative urinary samples for 5 h. Cl_gv was determined from the injected dose relative to the plasma concentration-time area, obtained by a gamma-variate iterative fit. Cl_exp and Cl_u were determined by standard technique. In patients without fluid retention, Cl_gv , Cl_exp and Cl_u were closely similar. The difference between Cl_gv and Cl_u (Cl_gv - Cl_u  = ΔCl) was mean -0·6 ml min^-1  1·73 m^-2 . In patients with ascites, ΔCl was significantly higher (11·8 ml min^-1  1·73 m^-2 , P<0·0001), but this value was lower than Cl_exp - Cl_u (17·5 mL min^-1  1·73 m^-2 , P<0·01). ΔCl increased with lower values of GFR (P<0·001). In conclusion, in patients with fluid retention and ascites Cl_gv and Cl_exp overestimates GFR substantially, but the overestimation is smaller with Cl_gv . Although Cl_u may underestimate GFR slightly, patients with ascites should collect urine quantitatively to obtain a reliable measurement of GFR.

Using a best variable to scale glomerular filtration rate (GFR) is important for clinical practice. The variables, estimated by equations regressed from a healthy population, are usually used in scaling GFR of renal patients. However, because the predicted variables may deviate in renal patients, it is necessary to verify whether these variables can be used to reduce the variability of GFR of renal patients. This study was designed to use repeated measures analyses to identify the best variable for scaling GFR of renal patients.

Patients with non-obstructive renal diseases were enrolled in this study. The absolute GFRs of (99m)Tc-DTPA renography (gGFR) and plasma clearance (pGFR) were measured. The indices relating to between-subjects variability, such as Passing and Bablok regression, intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) were used to identify the best variable from body surface area (BSA), extracellular fluid volume (ECV), lean body mass (LBM), total body water (TBW), body mass index (BMI), and metabolic rate (MR).

For the scaled indices related to between-subjects variability, ICC and CCC identified the same ranking sequence (BMI < LBMB(B; Boer) < LBMJ(J; James) < TBW < ECVB(B; Bird) < ECVS(S; Silva) < BSA < MR). In the Passing and Bablok regression, the ratio of residual standard deviation to pooled standard deviation (RSD/PSD) produced the same ranking sequence as that identified by ICC and CCC.

The estimated metabolic rate can explain most between-subjects variability of GFR, and seems to be the best variable for scaling GFR of renal patients.

Methods for determining extracellular fluid volume (ECFV) are important clinically for cats. Bromide dilution has been studied in cats to estimate ECFV. Markers of GFR also distribute in ECFV and can be used for its measurement.

The primary objective was to develop a method of determining ECFV from iohexol clearance in cats and evaluate agreement with that determined using bromide dilution. Additional objectives were to compare ECFV between azotemic and nonazotemic cats and evaluate appropriate methods of standardizing ECFV.

Client-owned cats with varying renal function.

Validation of ECFV determined from slope-intercept iohexol clearance was performed in 18 healthy nonazotemic cats. ECFV was then determined using the validated method and bromide dilution and agreement assessed. Appropriateness of standardization to body weight (BW) and body surface area (BSA) was evaluated.

Extracellular fluid volume determined from slope-intercept iohexol clearance and bromide dilution was 0.84 ± 0.32 L and 0.85 ± 0.19 L (mean ± SD), respectively. There were wide limits of agreement between the methods (-0.58 to 0.54 L) and therefore, agreement was considered to be poor. ECFV did not differ significantly between azotemic and nonazotemic cats (P = .177). BSA was found to be the best method for standardizing ECFV measurement in cats.

This study developed a method for determining ECFV from slope-intercept iohexol clearance which provides simultaneous assessment of renal function and an estimate of ECFV. ECFV does not differ between azotemic and nonazotemic cats, which suggests fluid volume loss or overload is not an important clinical feature in cats with mild chronic kidney disease.

Many equations have been developed to estimate various body fluid volumes from height and weight, but few have been developed for children. The aim of this study was to compare four height/weight formulae for estimating extracellular fluid volume (eECV) in children against measured extracellular fluid volume (mECV).

The mECV was obtained from plasma Cr-51-EDTA clearance data used for routine measurement of glomerular filtration rate (GFR) in two groups of children (n=182 and 69, respectively). eECV obtained using the formulae of Abraham et al. (Clin J Am Assoc Nephrol 6:741-747, 2011) and Friis-Hansen (Pediatrics 28:169-181, 1961) were compared with mECV in both patient groups. The formulae of Bird et al. (J Nucl Med 44:1037-1043, 2003) and of Peters (Nucl Med Commun 32:375-380, 2011) were originally based on groups 1 and 2, respectively, so the eECV from them was compared with the mECV in groups 2 and 1, respectively.

The eECV from the Friis-Hansen formula underestimated the mECV in larger children. Biases (mean differences between eECV and mECV) from the Bird (0.146 l) and Peters (0.029 l) formulae were not significantly different from zero, but those from the Abraham formula was higher than zero (0.694 and 0.588 l in groups 1 and 2; p<0.001). Precisions (standard deviations of the biases) of these three formulae were similar, ranging from 0.731 l (Peters) to 0.878 l (Abraham, group 2; p>0.1).

The formulae of Bird, Peters and Abraham have similar precisions. The higher bias of the Abraham formula is probably due to the higher values of mECV on which their formula was based. The Friis-Hansen formula no longer has a place.

Aim. The aim of this study was to investigate the influence of age, gender, obesity and scaling on glomerular filtration rate (GFR) and extracellular fluid volume (ECV) in healthy subjects.

This is a retrospective multi-centre study of 1878 healthy prospective kidney transplant donors (819 men) from 15 centres. Age and body mass index (BMI) were not significantly different between men and women. Slope-intercept GFR was measured (using Cr-51-EDTA in 14 centres; Tc-99m-DTPA in one) and scaled to body surface area (BSA) and lean body mass (LBM), both estimated from height and weight. GFR was also expressed as the slope rate constant, with one-compartment correction (GFR/ECV). ECV was measured as the ratio, GFR to GFR/ECV.

ECV was age independent but GFR declined with age, at a significantly faster rate in women than men. GFR/BSA was higher in men but GFR/ECV and GFR/LBM were higher in women. Young women (<30 years) had higher GFR than young men but the reverse was recorded in the elderly (>65 years). There was no difference in GFR between obese (BMI>30 kg/m2) and non-obese men. Obese women, however, had lower GFR than non-obese women and negative correlations were observed between GFR and both BMI and %fat. The decline in GFR with age was no faster in obese versus non-obese subjects. ECV/BSA was higher in men but ECV/LBM was higher in women. ECV/weight was almost gender independent, suggesting that fat-free mass in women contains more extracellular water. BSA is therefore a misleading scaling variable.

There are several significant differences in GFR and ECV between healthy men and women.