Kidney Disease Classification Research Articles

Kidney Disease Classification

Kidney diseases, such as tumors, cysts, and stones, are serious health conditions that affect millions globally. Medical imaging, particularly Computed-Tomography (CT) scans, plays an important role in diagnosing these conditions. Although, manual analysis of CT images by radiologist can be tiresome, fallible, and affected by human variability. This paper introduces a deep learning approach that employs Convolutional Neural Networks (CNN) for the automated classification of kidney CT scan images into four distinct categories : normal, cysts, tumors, and stones. The system is designed to boost diagnostic accuracy, reduce human error, and expedite clinical decision making. Through data preprocessing, CNN architecture design, training, and evaluation, the proposed model achieved a classification accuracy of 92%. This study highlights the significance of CNNs in medical image analysis anddtheir implicit for real- time deployment in clinical surroundings. Additionally, we compare our CNN-based approach with other advanced architectures like Vision Transformers, demonstrating CNNs' robustness and efficiency in this domain.

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease

An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.

Immunofluorescence Staining for IgG Subclass: Cause for Discrepancy in the Detection of IgG1

Immunofluorescence (IF) staining for IgG subclasses plays an important role in the classification of kidney disease. However, widely used IgG subclass-specific antibodies are now commercially unavailable. Thus, we compared alternative antibodies for performing IgG subclass staining. A total of 21 cases were stained by 3 different methods: direct IF using fluorescein isothiocyanate (FITC)-conjugated polyclonal antibodies against IgG1-4 (commercially unavailable method), direct IF using FITC-conjugated monoclonal antibodies (clones HP-6091, 6014, 6050, and 6025), indirect IF using monoclonal antibodies (clones HP-6069, 6002, 6050, and 6025), and FITC-conjugated polyclonal secondary antibody. For cases with discrepancy in IgG1 staining, additional direct IF using FITC-conjugated monoclonal antibody (clone 4E3) was performed. Of 21 cases, 11 (52%) had no staining for IgG1 by direct IF using the clone HP-6091 despite≥1+ staining by the direct IF using polyclonal antibodies. Similarly, direct IF for IgG1 using the clone 4E3 had negative result in all 10 cases with available tissue. However, indirect IF for IgG1 using the clone HP-6069 had similar staining intensity (within 1 order of magnitude) as direct IF using the polyclonal antibodies (10 of 10). Results of IF for IgG2, IgG3, and IgG4 were similar in most cases. The choice of antibodies influences the result of IgG subclass staining, especially for anti-IgG1 antibodies, in which 2 monoclonal antibodies (HP6091 and 4E3) appear less sensitive. Although this may be due to unaccounted variables and requires confirmation, our results may partially explain the difference in IgG1 staining in the literature and underscore the need for careful validation.

Design and methodology of the PRIMETIME 1 cohort study: PRecIsion MEdicine based on kidney TIssue Molecular interrogation in diabetic nEphropathy.

Clinical features of diabetic kidney disease alone cannot differentiate between the histopathology that defines diabetic nephropathy (DN) and non-diabetic nephropathy (NDN). A kidney biopsy is necessary to make the definitive diagnosis of DN. However, there is no consensus on when to perform a kidney biopsy in individuals with diabetes and kidney disease. Furthermore, the implications of NDN versus DN for management, morbidity and kidney prognosis are unclear. To address the gap in knowledge, we aimed to create a national retrospective cohort of people with diabetes and a performed kidney biopsy. Adults diagnosed with diabetes in Denmark between 1996 and 2020 who had a kidney biopsy performed were included. The cohort was established by linking a nationwide diabetes registry with the Danish Pathology Registry. Data from 11 national registries and databases were compiled. The type of kidney disease was classified using a three-step analysis of Systematized Nomenclature of Medicine codes reported in relation to the histopathological examinations of kidney tissue. The final cohort and classification of kidney disease was as follows: out of 485 989 individuals with diabetes 2586 were included, 2259 of whom had type 2 diabetes. We were able to classify 599 (26.5%) with DN, 703 (31.1%) with NDN and 165 (7.3%) with mixed disease in individuals with type 2 diabetes. In individuals with type 1 diabetes, 132 (40.4%) had DN, 73 (22.3%) NDN and 39 (11.9%) mixed disease. The remaining could not be classified or had normal histology. The overall median (Q1-Q3) follow-up time was 3.8 (1.6-7.2) years. This cohort is a novel platform based on high-quality registry data for important longitudinal studies of the impact of kidney disease diagnosis on prognosis. With regular updates of data from the Danish registries, the presented follow-up will increase over time and is only limited by emigration or death.

#6853 KIDNEY ELASTOMETRY, KIDNEY MISMATCH INDEX AND CHRONICITY INDEX: NEW METHODS TO INTERROGATE THE DISEASED KIDNEY

Abstract Background and Aims Standard ultrasound mainly offers information about structural kidney alterations which may provide dates regarding the mass of the filtrating kidney (e.g. the nephron number), or obstructive alterations. Furthermore, the parenchymal Doppler ultrasound offers the resistance index, which is highly correlated to the plasma creatinine levels. However, in conditions of isolated proteinuria with normal kidney function these two classical methods do not reveal any alteration. We have hypothesized that kidney stiffness and a mismatch between creatinine levels and parenchymal resistance indices may offer additional features which may help the nephrologist in the classification of kidney diseases. Method In this respect, we have performed a transversal study on 468 patients with available resistance index of both the kidney and the spleen, clinical variables (including hemoglobin, creatinine, cholesterol, proteinuria, hypertension, diabetes) and shearwave elastometry, which gives an estimate of parenchimal stiffness. In order to identify patients with a mismatch between creatinine and renal resistance index we have introduced the kidney mismatch index (KMI) derived from the ratio between the renal resistance index and the creatinine levels. In order to identify chronic patients, we argued that in acute settings the creatinine levels (and hence the resistance index) are altered whereas the kidney size is normal: therefore the ratio of resistance index over the kidney size was tested as a quantitative marker of chronicity. Results Results confirm a strong correlation between kidney resistance index and creatinine (Pearson coeff. 0.318, p<0.01). Interestingly, this index is also significantly dependent on age and cholesterol levels using a multiple regression model, after adjusting for creatinine levels. On the contrary, the resistance index of the spleen did not correlate with creatinine or eGFR, whereas it correlated with cholesterol levels. Overall, creatinine levels could be predicted by the kidney size (longitudinal diameter, LD) and by the kidney resistance index (RI) using the following formula: creatinine = 1.8 -0.175*LD + 2.601* RI Conversely, sharewave stifness is only correlated to proteinuria (Pearson coeff. 0.497, p = 0.008) but not with creatinine.Finally, the kidney mismatch index (resistance index dissociated from creatinine levels) does not correlate with proteinuria and seems to identify particular subsets of patients with abnormal microcircle dynamics despite normal filtration rate, and should be tested in future studies as a prognostic factor. Conclusion In conclusion, kidney elastometry and kidney mismatch indices may prove additional sources of valuable informations that can be gathered from ultrasound methods which allow the identification of a diseased kidneys even in presence of normal architecture and normal resistance index.

Vaccine Attitudes and COVID-19 Vaccine Intention Among Parents of Children With Kidney Disease or Primary Hypertension

Rationale & ObjectiveChildren with kidney disease and primary hypertension may be more vulnerable to COVID-19. We examined COVID-19 vaccine hesitancy among parents of children with chronic kidney disease or hypertension.Study DesignSequential explanatory mixed-methods design; survey followed by in-depth interviews.Setting & ParticipantsParents of children aged <18 years with kidney disease or primary hypertension within a large pediatric practice.ExposureParental attitudes toward general childhood and influenza vaccines assessed by the Vaccine Hesitancy Scale. Kidney disease classification, demographic and socioeconomic factors, experiences with COVID-19, COVID-19 mitigation activities and self-efficacy, and sources of vaccine information.OutcomeWillingness to vaccinate child against COVID-19.Analytical ApproachAnalysis of variance (ANOVA) test to compare parental attitudes toward general childhood and influenza vaccination with attitudes toward COVID-19 vaccination. Multinomial logistic regression to assess predictors of willingness to vaccinate against COVID-19. Thematic analysis of interview data to characterize influences on parental attitudes.ResultsOf the participants, 207 parents completed the survey (39% of approached): 75 (36%) were willing, 80 (39%) unsure, and 52 (25%) unwilling to vaccinate their child against COVID-19. Hesitancy toward general childhood and influenza vaccines was highest among the unwilling group (P < 0.001). More highly educated parents more likely to be willing to vaccinate their children, while Black race was associated with being more likely to be unwilling. Rushed COVID-19 vaccine development as well as fear of serious and unknown long-term side effects were themes that differed across the parental groups that were willing, unsure, or unwilling to vaccinate their children. Although doctors and health care teams are trusted sources of vaccine information, perceptions of benefit versus harm and experiences with doctors differed among these 3 groups. The need for additional information on COVID-19 vaccines was greatest among those unwilling or unsure about vaccinating.LimitationsGeneralizability may be limited.ConclusionsTwo-thirds of parents of children with kidney disease or hypertension were unsure or unwilling to vaccinate their child against COVID-19. Higher hesitancy toward routine childhood and influenza vaccination was associated with hesitancy toward COVID-19 vaccines. Enhanced communication of vaccine information relevant to kidney patients in an accessible manner should be examined as a means to reduce vaccine hesitancy.Plain-Language SummaryChildren with kidney disease or hypertension may do worse with COVID-19. As there are now effective vaccines to protect children from COVID-19, we wanted to find out what parents think about COVID-19 vaccines and what influences their attitudes. We surveyed and then interviewed parents of children who had received a kidney transplant, were receiving maintenance dialysis, had chronic kidney disease, or had hypertension. We found that two-thirds of parents were hesitant to vaccinate their children. Their reasons varied, but the key issues included the need for information pertinent to their child and a consistent message from doctors and other health care providers. These findings may inform an effective vaccine campaign to protect children with kidney disease and hypertension.

Accuracy of diagnosing acute kidney injury by assessing urine output within the first week of life in extremely preterm infants.

Neonatal acute kidney injury (AKI) is associated with increased mortality and is often assessed with the neonatal modified Kidney Disease: improving Global Outcomes (KDIGO) classification, which uses changes in serum creatinine levels. However, because this classification has many drawbacks, a novel method, the neonatal Risk, Injury, Failure, Loss, and End-Stage Kidney Disease (nRIFLE) classification for diagnosing neonatal AKI according to urine output (UO), was recently proposed. To date, no data on the incidence of AKI according to nRIFLE are available for extremely preterm infants (born at gestational age less than 28weeks). This study was conducted to clarify the association between incidence of AKI and in-hospital mortality in extremely preterm infants. Of 171 extremely preterm infants hospitalized from 2006 to 2020, 84 in whom indwelling bladder catheters were placed for UO measurements within 24h of life were included. The incidence of AKI was assessed using the nRIFLE classification. In-hospital mortality was compared between patients with AKI and those without it. The incidence of AKI during the first week of life was 56% and that of in-hospital mortality was significantly higher in patients with AKI (25.5%) than in those without it (2.8%). The odds ratio was 12.3 with 95% confidence interval ranging from 1.5 to 100.0. The incidence of AKI according to nRIFLE was higher than reported in most previous studies using the neonatal modified KDIGO classification, suggesting that assessment by nRIFLE criteria using UO may improve diagnostic accuracy of AKI in extremely preterm infants.

Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification

Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that selected from each filter feature selection. The filter feature selection that implemented in this research are Information Gain (IG), Chi-Squares, ReliefF and Fisher Score. Genetic Algorithm (GA) is used to select the best subset from the ensemble result of the filter feature selection. In this research, Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes classification techniques were used to diagnose the CKD. The features subset that selected are different and specialised for each classifier. By implementing the proposed method irrelevant features through filter feature selection able to reduce the burden and computational cost for the genetic algorithm. Then, the genetic algorithm able to perform better and select the best subset that able to improve the performance of the classifier with less attributes. The proposed genetic algorithm union filter feature selections improve the performance of the classification algorithm. The accuracy of RF, XGBoost, KNN and SVM can achieve to 100% and NB can achieve to 99.17%. The proposed method successfully improves the performance of the classifier by using less features as compared to other previous work.

Commentary on Intraglumerular dysfunction predicts kidney failure in the type 2 diabetes.

The key physiological parameters that determine glomerular filtration rate levels are renal plasma flow, filtration fraction, intraglomerular pressure, and balance between afferent and efferent glomerular arteriolar resistance. The evaluation of the balance between afferent and efferent glomerular arteriolar resistance might be useful for the classification of diabetic kidney disease.

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

The world is now in the era of big data and processing, and exploring the data has become one of the significant challenges. Hence, researchers have done a lot to analyse these data in the health sector to enhance disease detection and classification using artificial intelligence and ML principles. Kidney disease is one of the terrible conditions in which its late detection has sent many people to untimely graves. ML classifiers have been employed in many dimensions to classify heart disease, but, existing works have not explored the variants of each method for selection of best model parameters. An attempt is being made in this research to study the behaviour of three (3) variants each from two(2) tree-based models in the classification of Kidney Disease. Three of the variants are Complex, Medium and Simple models of Decision tree classifier and the other one are Boosted, Bagged and RUSBoosted of Ensemble Classifiers. Using MATLAB for implementation, the model performance established that the accuracy of Ensemble Classifier (Bagged tree model) is the best, concerning the speed, Decision tree (Complex and Simple tree models have the same and highest value). Hence, the two are the best. In terms of training time, Decision tree(Simple tree) has the least time and therefore the best.

Acute Kidney Injury in Poisoned Patients Admitted to ICU

Background: Acute Kidney Injury (AKI) is an abrupt decrease in kidney function, leading to the retention of urea and other nitrogenous waste products. Poisoned patients admitted to the Intensive Care Unit (ICU) may develop AKI due to some reasons. This study was done to evaluate the AKI in poisoned patients admitted to ICU. Methods: 146 patients, admitted to the ICU of Imam Reza Hospital from March 2017 to March 2018 were studied. AKI status was assessed using Acute Kidney Injury Network (AKIN) and Risk, Injury, Failure, Loss of kidney function and End-stage kidney disease (RIFLE) classification. Data analysis was done through SPSS V. 22 software. Results: Opioids, organophosphates, aluminum phosphide, multiple drugs, and other types of poisoning were the main five poisoning classes. Opioid toxicity was had the highest frequency with 51 patients; cases in this group experienced longer length of hospitalization stay and higher serum creatinine level than others did. Among 146 patients, 19 patients (12.8%) died, and 97 patients (66%) were transferred to the ICU. Of all cases, 18 patients (12.3%) had renal dysfunction (six patients were at risk, five patient at injury, and seven patients were at failure phase based on the RIFLE criteria). Renal replacement therapy was required in 24 cases (16.4%). Conclusion: It is unlikely to detect a significant difference in the occurrence of AKI between the main poisoning classes. Being the largest group of intoxicated patients admitted to the ICU, the opioid poisoning had the highest rate of AKI

Impact of percutaneous coronary intervention on short and long-term prognosis of elderly patients with acute myocardial infarction from 2010 to 2017 in Japanese population

Abstract Background Advances in therapies have successfully decreased short-term mortality in patients with acute myocardial infarction (AMI). Although aging population is recently increasing in developed countries, there are few reports about the association between prevalence of percutaneous coronary intervention (PCI) and long-term prognosis in elderly patients with AMI in Japan. Purpose The aim of this study was to clarify the prevalence of PCI and the impact of PCI on short and long-term prognosis of elderly patients with AMI. Methods and results We investigated the prevalence of PCI and short-term mortality in 4,109 patients with AMI who were registered in Yamagata AMI Registry from 2010 to 2017. Long-term mortality was investigated using data from death certification in July 2019. We divided patients with AMI into three age groups (group 1, &amp;lt;65 years old; group 2, 65–79 years old; and group 3, ≥80 years old). Short-term mortality within 30 days was 6.5%, 12.1%, and 28.6%, respectively. Also, prevalence of PCI was 88.0%, 84.7%, and 62.7%, respectively. Multivariate analysis revealed that age, PCI, and severity of Killip classification were significantly associated with short-term mortality after adjustment for confound factors in group 3. Since the prevalence of PCI in group 3 was the lowest among three groups, the cause of PCI not being executed was investigated in 1,429 patients aged ≥80 years old. Elderly patients who didn't undergo PCI was older, more women, and had higher prevalence of chronic kidney disease, previous stroke, and severe Killip classification. Multivariate analysis revealed that age and Killip III/IV were significantly associated with non-executed PCI after adjustment for confound factors. Next, we investigated impact of PCI on long-term mortality in elderly patients who escaped acute death. Multivariate Cox hazard analysis revealed that PCI was associated with lower mortality after adjustment for confound factors (adjusted hazard ratio 2.47, 95% CI: 1.47–4.06; p=0.0008). Conclusion Lower prevalence of PCI and higher short-term mortality were observed in elderly patients with AMI aged ≥80 years old. PCI ameliorated long-term mortality as well as short-term mortality in elderly patients with AMI. Funding Acknowledgement Type of funding source: None

Pyelonephritis: A Historical Reappraisal.

There is no human knowledge which cannot lose its scientific character when we forget the conditions under which it originated, the questions it answered and the function it was created to serve.[1][1] As with most diseases of the kidney, pyelonephritis is an old ailment, which as a diagnosis, is a

Urinary proteins: up-to-date reference methods for urinary protein analysis.

Measurement of urine albumin has been introduced in the new classification of kidney disease (KD) as a marker for detecting, monitoring and predicting KD. Currently, the measure is not standardized. The variability of results obtained with commercially available procedures is important and can lead to misclassification of patients. Analytical standardization, started in 2007, is in progress. SRM 2925 primary reference material, SRM 3666 secondary reference material and liquid chromatography isotope dilution mass spectroscopy (LC-IDMS) reference measurement procedure are being validated by the National Institute of Standards and Technology (NIST). This report presents strategies and difficulties for developing this reference system.

My Patient with Monoclonal Gammopathy of Undetermined Significance has a Kidney Problem

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant condition signifying the presence of a B-cell lymphoproliferative disorder. By connotation, it should not meet the definition of multiple myeloma, Waldenström macroglobulinemia, or lymphoma. In addition, it cannot be responsible for any end-organ damage. Similar to polyclonal immunoglobulins (Ig), monoclonal gammopathy has been increasingly recognized as an important cause of kidney disease. The recent introduction of the term “monoclonal gammopathy of renal significance” (MGRS) highlights this importance. MGRS is similar to MGUS in which the B-cell lymphoproliferative disorder has not reached a state considered to be malignant, but differentiates itself by the presence of a monoclonal gammopathy related kidney disease. This distinction is important since it separates MGRS, which is not benign, from the MGUS condition, which is benign. It also allows for a better classification of kidney diseases caused by monoclonal gammopathies. There are many renal diseases and lesions that have been identified to be secondary to MGRS. In addition, MGRS-associated renal diseases can mimic polyclonal Ig mediated kidney diseases. Kidney biopsy with immunofluorescence is the key for diagnosing MGRS-related kidney diseases. Once the diagnosis is made, a specific evaluation is needed for the diagnosis and treatment of MGRS-related kidney diseases that differs from the polyclonal Ig counterparts.

Classification of Ultrasound Kidney Images using PCA and Neural Networks

In this paper, a computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases. Images of five classes: Normal, Cyst, Stone, Tumor and Failure were considered. A set of statistical features and another set of multi-scale wavelet-based features were extracted from the region of interest (ROI) of each image and the principal component analysis was performed to reduce the number of features. The selected features were utilized in the design and training of a neural network classifier. A correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.

Canadian Society of Nephrology Commentary on the KDIGO Clinical Practice Guideline for CKD Evaluation and Management

We congratulate the KDIGO (Kidney Disease: Improving Global Outcomes) work group on their comprehensive work in a broad subject area and agreed with many of the recommendations in their clinical practice guideline on the evaluation and management of chronic kidney disease. We concur with the KDIGO definitions and classification of kidney disease and welcome the addition of albuminuria categories at all levels of glomerular filtration rate (GFR), the terminology of G categories rather than stages to describe level of GFR, the division of former stage 3 into new G categories 3a and 3b, and the addition of the underlying diagnosis. We agree with the use of the heat map to illustrate the relative contributions of low GFR and albuminuria to cardiovascular and renal risk, though we thought that the highest risk category was too broad, including as it does people at disparate levels of risk. We add an albuminuria category A4 for nephrotic-range proteinuria and D and T categories for patients on dialysis or with a functioning renal transplant. We recommend target blood pressure of 140/90mm Hg regardless of diabetes or proteinuria, and against the combination of angiotensin receptor blockers with angiotensin-converting enzyme inhibitors. We recommend against routine protein restriction. We concur on individualization of hemoglobin A1c targets. We do not agree with routine restriction of sodium intake to <2g/d, instead suggesting reduction of sodium intake in those with high intake (>3.3g/d). We suggest screening for anemia only when GFR is <30mL/min/1.73m(2). We recognize the absence of evidence on appropriate phosphate targets and methods of achieving them and do not agree with suggestions in this area. In drug dosing, we agree with the recommendation of using absolute clearance (ie, milliliters per minute), calculated from the patient's estimated GFR (which is normalized to 1.73m(2)) and the patient's actual anthropomorphic body surface area. We agree with referral to a nephrologist when GFR is <30mL/min/1.73m(2) (and for many other scenarios), but suggest urine albumin-creatinine ratio > 60mg/mmol or proteinuria with protein excretion > 1g/d as the referral threshold for proteinuria.

Electron Microscopy in Kidney Research: Seeing is Believing

Electron microscopy (EM) has been an indispensable tool for kidney research since its inception more than half a century ago. Much of the substantial advances were propelled by the need to find methods to best visualize and analyze the kidney's structure deduced from the fundamental principle that has structure and function intimately related. The result of 3 decades of experimental kidney work between 1950 and 1980 coincided with remarkable advances in nephrology that marked a renaissance era for renal pathology and resulted in the morphologic classification of medical kidney diseases. In the era of genetics and molecular medicine TEM continues to contribute significant clinical and pathogenetic insights in kidney disease. The basic principles as applied to kidney disease experimental models are discussed with emphasis on crescent formation in Col4A3-deficient mice and a mouse model of experimental oxalosis (CaOx).

Weighing the Evidence: The Challenge of Developing Evidence-Based Renal Dosing Strategies

Evaluating drug dosing to account for patient renal function is one of the primary review functions of a hospital pharmacist. Available estimates of renal function have known limitations, but laboratory and medical advances have improved the detection and classification of kidney disease. How do new, more accurate methods of classifying renal disease apply to the interpretation of drug dose recommendations derived by other, less accurate methods? The application of these new strategies to medication dose evaluation lacks clarity. The Cockcroft-Gault estimation of creatinine clearance (CrCl) was introduced in 1976 and has been a standard consideration for drug dose evaluation in adult patients with renal impairment. Although it has been routinely used, this method has recognized limitations. For example, the standard modification to account for female gender in the Cockcroft-Gault equation is theoretical; the original study population did not include female patients. The Modification of Diet in Renal Disease (MDRD) estimation of glomerular filtration rate (GFR) was introduced in 1999 and generally provides a more accurate assessment of renal function than traditional estimates in adult patients. Because the MDRD equation was not available or generally accepted as a method for classifying kidney function during the development of most marketed drugs, clinicians may be unsure about how to apply estimated GFR to drug dose evaluation. Both Cockcroft-Gault and MDRD estimates are based on studies in patients with stable renal function. In contrast, hospitalized patients often demonstrate dynamic renal function due to changing clinical status and comorbidities. National Kidney Disease Education Program (NKDEP) guidance on drug dosing recognizes this limitation and acknowledges that available estimates do not account for all factors that affect serum creatinine concentrations. Available estimates lack accuracy for patients with body size or muscle mass extremes. Some clinicians using the Cockcroft-Gault equation round the low serum creatinine values in elderly patients to another value, such as 1 mg/dL, or utilize ideal body weight to overcome these limitations. However, these approaches are not evidence-based, and researchers have demonstrated that these modifications are less accurate. 2-5 A standardized technique to calibrate serum creatinine assays introduced several years ago is now expected to be fully implemented nationally. Standardization minimizes variation of serum creatinine assay results between clinical laboratories and should lead to more consistent dosing for drugs eliminated renally. Changes to serum creatinine reference ranges would have been expected at most facilities upon implementation. Standardization has generally resulted in higher CrCl values as estimated by the CockcroftGault equation. According to the NKDEP, because the original study samples are no longer available and the creatinine assay method used to derive the CockcroftGault equation is no longer utilized, the equation cannot be re-expressed to the new standardized creatinine values. In contrast, standardized calibration of serum creatinine assays increases the accuracy of estimating GFR by the MDRD method. Current US Food and Drug Administration (FDA) guidance for the industry on pharmacokinetics published in 1998 acknowledges measured serum creatinine concentrations or estimated CrCl as recognized methods to classify kidney function for drug dosing. Draft guidance posted for review in 2010 reflects the significant changes in the body of knowledge in classifying renal disease and incorporates MDRD as well as Cockcroft-Gault estimates of kidney function. According to the NKDEP, clinicians should use either the MDRD equation or the Cockcroft-Gault equation to estimate kidney function for drug dosing.