Artificial intelligence-guided precision treatment of chronic kidney disease-mineral bone disorder.

Chronic kidney disease (CKD)-mineral bone disorder (MBD) is a complex clinical syndrome that begins early during CKD and evolves into one of the deadliest complications of CKD through its effects on the cardiovascular and skeletal systems. Achievement of treatment goals to decrease the risk of accelerated cardiovascular events and fractures has been challenging. We hypothesized that application of quantitative systems pharmacology (QSP) modeling combined with artificial intelligence techniques could improve the management of CKD-MBD with the goal of improving outcomes for patients with CKD. We present the implementation of a reinforcement learning (RL) approach to achieve the prescribed goals for serum calcium, phosphorus, and parathyroid hormone through concurrent dosing of phosphate binders, vitamin D analogs, and calcimimetics by simulation in 80 subjects in Matlab. In silico simulation results demonstrate that the application of a QSP model coupled with RL more effectively and quickly achieves treatment goals even in the setting of inferior simulated subject compliance with medical therapy and identifies key decision variables for therapeutic recommendations.

Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease.

Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder.

Development of a quantitative systems pharmacology model of chronic kidney disease: metabolic bone disorder.

Chronic kidney disease mineral bone disorder (CKD-MBD) is a virtually universal complication of kidney diseases, starting early in the course of disease and resulting in devastating clinical consequences ranging from bone fragility to accelerated atherosclerosis and early cardiovascular death. Guidelines for therapeutic goals for CKD-MBD have been published, and achievement of these guidelines is associated with improved survival. However, the incomplete understanding of CKD-MBD and the individual variability in the manifestations of CKD-MBD have made it difficult to achieve these guidelines. We hypothesized that the progression of MBD through all stages of CKD, including end-stage kidney disease, could be represented by a quantitative systems pharmacology/systems biology (QSP) model. To address this hypothesis, we constructed a QSP model of CKD-MBD, building on an open-source model of calcium and phosphorus metabolism. Specifically, we estimated and validated the model using data from 5,496 patients with CKD enrolled in the Chronic Renal Insufficiency Cohort study. Our model accurately predicted changes in markers of mineral metabolism related to progressing CKD. We demonstrated that the incorporation of fibroblast growth factor 23 and the soft tissue compartment is essential for accurate modeling of the changes in calcium, phosphorus, intact parathyroid hormone, and calcitriol in CKD-MBD. We conclude that our systems biology model accurately represents CKD-MBD disease progression and can be used as a test bench for improving therapeutic interventions.

Redox States of Protein Cysteines in Pathways of Protein Turnover and Cytoskeleton Dynamics Are Changed with Aging and Reversed by Slc7a11 Restoration in Mouse Lung Fibroblasts.

Slc7a11 is the key component of system Xc -, an antiporter that imports cystine (CySS) and exports glutamate. It plays an important role in cellular defense against oxidative stress because cysteine (Cys), reduced from CySS, is used for and limits the synthesis of glutathione (GSH). We have shown that downregulation of Slc7a11 is responsible for oxidation of extracellular Cys/CySS redox potential in lung fibroblasts from old mice. However, how age-related change of Slc7a11 expression affects the intracellular redox environment of mouse lung fibroblasts remains unexplored. The purpose of this study is to evaluate the effects of aging on the redox states of intracellular proteins and to examine whether Slc7a11 contributes to the age-dependent effects. Iodoacetyl Tandem Mass Tags were used to differentially label reduced and oxidized forms of Cys residues in primary lung fibroblasts from young and old mice, as well as old fibroblasts transfected with Slc7a11. The ratio of oxidized/reduced forms (i.e., redox state) of a Cys residue was determined via multiplexed tandem mass spectrometry. Redox states of 151 proteins were different in old fibroblasts compared to young fibroblasts. Slc7a11 overexpression restored redox states of 104 (69%) of these proteins. Ingenuity Pathway Analysis (IPA) showed that age-dependent Slc7a11-responsive proteins were involved in pathways of protein translation initiation, ubiquitin-proteasome-mediated degradation, and integrin-cytoskeleton-associated signaling. Gene ontology analysis showed cell adhesion, protein translation, and organization of actin cytoskeleton were among the top enriched terms for biological process. Protein-protein interaction network demonstrated the interactions between components of the three enriched pathways predicted by IPA. Follow-up experiments confirmed that proteasome activity was lower in old cells than in young cells and that upregulation of Slc7a11 expression by sulforaphane restored this activity. This study finds that aging results in changes of redox states of proteins involved in protein turnover and cytoskeleton dynamics, and that upregulating Slc7a11 can partially restore the redox states of these proteins.

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

INTRODUCTION: Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS. METHODS: We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance. RESULTS: Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P = 0.003; area under the receiver operating characteristic curve = 0.78). CONCLUSION: Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.

Personalized Anemia Management and Precision Medicine in ESA and Iron Pharmacology in End-Stage Kidney Disease.

Substantial progress has been made in the application of computer-driven methods to provide erythropoietic dosing information for patients with anemia resulting from chronic kidney disease. Initial solutions were simply computerized versions of traditional paper-based anemia management protocols. True personalization was achieved through the use of advanced modeling techniques such as artificial neural networks, physiologic models, and feedback control systems. The superiority of any one technique over another has not been determined, but all methods have shown an advantage in at least one area over the traditional paper expert system used by most dialysis facilities. Improvements in the percentage of hemoglobin measurements within target range, decreased within-subject hemoglobin variability, decreased erythropoiesis-stimulating agent dose, and decreased transfusion rates all have been shown.

Individualized anemia management in a dialysis facility - long-term utility as a single-center quality improvement experience
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BACKGROUND: Standard protocol-based approaches to erythropoiesis stimulating agent (ESA) dosing in anemia management of end-stage renal disease (ESRD) fail to address the inter-individual variability in patient's response to ESA. We conducted a single-center quality improvement project to investigate the long-term performance of a computer-designed dosing system. MATERIALS AND METHODS: The study was a retrospective case-control study with long-term follow-up. All hemodialysis patients who received treatment at University Kidney Center (Louisville, KY, USA) between September 1, 2009, and March 31, 2017, were included. We implemented an individualized ESA dosing algorithm into an electronic health records database software to provide patient-specific ESA dose recommendations to anemia managers at monthly intervals. The primary outcome was the percentage of hemoglobin (Hb) concentrations between 10 and 12 g/dL during the case-control study and 9 and 11 g/dL during follow-up. Secondary outcomes were intra- and inter-individual Hb variability. For the case-control study, we compared outcomes over 12 months before and after implementation of the algorithm. Subjects served as their own controls. We used the last Hb concentration of the month and ESA dose per week. Long-term follow-up examined trends in proportion within the target range, Hb, and ESA dose. RESULTS: Individualized ESA dosing in 56 subjects was associated with a moderate (6.6%) increase of mean Hb maintenance within target over the 12-month observation period (62.7% before vs. 69.3% after, p = 0.063). Intra-individual mean Hb variability decreased (1.1 g/dL before vs. 0.8 g/dL after, p < 0.001), so did inter-individual mean Hb variability (1.2 g/dL before vs. 1.0 g/dL after, p = 0.010). Long-term follow-up in 233 subjects for 42 months demonstrated stability of the achieved Hb despite an increasing ESA resistance in the patient population. CONCLUSION: Implementation of the individualized ESA dosing algorithm facilitates improvement in Hb maintenance within target, decreases Hb variability and reduces the dose of ESA required to achieve Hb target.
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Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease.

BACKGROUND AND OBJECTIVE: Anemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. METHODS: A Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. RESULTS: Performance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. CONCLUSIONS: The proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia.

Markers of iron status in chronic kidney disease.

Anemia is one of the main comorbidities related to chronic kidney disease (CKD). Until the advent of erythropoiesis stimulating agents (ESA), endogenous erythropoietin deficiency has been thought to be the main culprit of anemia in CKD patients. The use of ESAs has shed new light on the physiology of CKD anemia, where iron homeostasis plays an increasingly important role. Disorders of iron homeostasis occurring in CKD turn the anemia management in those patients into a complex multifactorial therapeutic task, where ESA and Iron dose must be properly balanced to achieve the desired outcome without exposing the patients to the risk of serious adverse events. This review covers diagnostic markers traditionally used for quantifying iron status in CKD patients, such as serum ferritin and transferrin saturation, new ones, such as reticulocyte hemoglobin content and percent hypochromic red cells (HRC), as well as experimental ones, such as hepcidin and soluble transferrin receptor (sTfR). Each marker is presented in terms of their diagnostic performance, followed by biological and analytical variability data. Advantages and disadvantages of each marker are briefly discussed. Although serum ferritin and transferrin saturation are easily available, they exhibit large biological variability and require caution when used for diagnosing iron status in CKD patients. Reticulocyte hemoglobin content and the percentage of HRC are more powerful, but their widespread use is hampered by the issue of sample stability in storage. sTfR and hepcidin show promise, but require further investigation as well as the development of standardized, low-cost assay platforms.

Artificial intelligence for optimal anemia management in end-stage renal disease.

Computational intelligence for the prediction of hemoglobin to guide the selection of erythropoiesis-stimulating agent dose results in improved anemia management. The models used for the prediction result from the use of individual patient data and help to increase the number of hemoglobin observations within the target range. The benefits of using these modeling techniques appear to be a decrease in erythropoiesis-stimulating agent use and a decrease in the number of transfusions. This study confirms the results of previous smaller studies and suggests that additional beneficial results may be achieved.

Role of Na+/H+ exchanger regulatory factor 1 in forward trafficking of the type IIa Na+-Pi cotransporter.

Na+/H+ exchanger regulatory factor (NHERF1) plays a critical role in the renal transport of phosphate by binding to Na+-Pi cotransporter (NpT2a) in the proximal tubule. While the association between NpT2a and NHERF1 in the apical membrane is known, the role of NHERF1 to regulate the trafficking of NpT2a has not been studied. To address this question, we performed cell fractionation by sucrose gradient centrifugation in opossum kidney (OK) cells placed in low-Pi medium to stimulate forward trafficking of NpT2a. Immunoblot analysis demonstrated expression of NpT2a and NHERF1 in the endoplasmic reticulum (ER)/Golgi. Coimmunoprecipitation demonstrated a NpT2a-NHERF1 interaction in the ER/Golgi. Low-Pi medium for 4 and 8 h triggered a decrease in NHERF1 in the plasma membrane with a corresponding increase in the ER/Golgi. Time-lapse total internal reflection fluorescence imaging of OK cells placed in low-Pi medium, paired with particle tracking and mean square displacement analysis, indicated active directed movement of NHERF1 at early and late time points, whereas NpT2a showed active movement only at later times. Silence of NHERF1 in OK cells expressing green fluorescent protein (GFP)-NpT2a resulted in an intracellular accumulation of GFP-NpT2a. Transfection with GFP-labeled COOH-terminal (TRL) PDZ-binding motif deleted or wild-type NpT2a in OK cells followed by cell fractionation and immunoprecipitation confirmed that the interaction between NpT2a and NHERF1 was dependent on the TRL motif of NpT2a. We conclude that appropriate trafficking of NpT2a to the plasma membrane is dependent on the initial association between NpT2a and NHERF1 through the COOH-terminal TRL motif of NpT2a in the ER/Golgi and requires redistribution of NHERF1 to the ER/Golgi.

Simplified Warfarin Dose-response Pharmacodynamic Models.

Warfarin is a frequently used oral anticoagulant for long-term prevention and treatment of thromboembolic events. Due to its narrow therapeutic range and large inter-individual dose-response variability, it is highly desirable to personalize warfarin dosing. However, the complexity of the conventional kinetic-pharmacodynamic (K-PD) models hampers the development of the personalized dose management. To avert this challenge, we propose simplified PD models for warfarin dose-response relationship, which is motivated by ideas from control theory. The simplified models were further applied to longitudinal data of 37 patients undergoing anticoagulation treatment using the standard two-stage approach and then compared with the conventional K-PD models. Data analysis shows that all models have a similar predictive ability, but the simplified models are most parsimonious.

Iron dosing in kidney disease: inconsistency of evidence and clinical practice.

The management of anemia in patients with chronic kidney disease (CKD) is difficult. The availability of erythropoiesis-stimulating agents (ESAs) has increased treatment options for previously transfusion-requiring patients, but the recent evidence of ESA side effects has prompted the search for complementary or alternative approaches. Next to ESA, parenteral iron supplementation is the second main form of anemia treatment. However, as of now, no systematic approach has been proposed to balance the concurrent administration of both agents according to individual patient's needs. Furthermore, the potential risks of excessive iron dosing remain a topic of controversy. How, when and whether to monitor CKD patients for potential iron overload remain to be elucidated. This review addresses the question of risk and benefit of iron administration in CKD, highlights the evidence supporting current practice, provides an overview of standard and potential new markers of iron status and outlines a new pharmacometric approach to physiologically compatible individualized dosing of ESA and iron in CKD patients.

Balancing ESA and iron therapy in a prospective payment environment.

Ever since the introduction of EPO, ESAs and iron dosing have been driven by financial incentives. When ESAs were a profit center for providers, large doses were used. With ESAs becoming a cost center, a new trend has appeared, gradually replacing their use with iron to achieve the same therapeutic effect at lower cost. This financially driven approach, treating ESAs and iron as alternatives, is not consistent with human physiology where these agents act in a complementary manner. It is likely that we are still giving unnecessarily large doses of ESAs and iron, relative to what our patients' true needs are. Although we have highlighted the economic drivers of this outcome, many other factors play a role. These include our lack of understanding of the complex interplay of the anemia of chronic disease, inflammation, poor nutrition, blood loss through dialysis, ESAs and iron deficiency. We propose that physiology-driven modeling may provide some insight into the interactions between erythropoiesis and ferrokinetics. This insight can then be used to derive new, physiologically compatible dosing guidelines for ESAs and iron.

Individualized anemia management reduces hemoglobin variability in hemodialysis patients.

One-size-fits-all protocol-based approaches to anemia management with erythropoiesis-stimulating agents (ESAs) may result in undesired patterns of hemoglobin variability. In this single-center, double-blind, randomized controlled trial, we tested the hypothesis that individualized dosing of ESA improves hemoglobin variability over a standard population-based approach. We enrolled 62 hemodialysis patients and followed them over a 12-month period. Patients were randomly assigned to receive ESA doses guided by the Smart Anemia Manager algorithm (treatment) or by a standard protocol (control). Dose recommendations, performed on a monthly basis, were validated by an expert physician anemia manager. The primary outcome was the percentage of hemoglobin concentrations between 10 and 12 g/dl over the follow-up period. A total of 258 of 356 (72.5%) hemoglobin concentrations were between 10 and 12 g/dl in the treatment group, compared with 208 of 336 (61.9%) in the control group; 42 (11.8%) hemoglobin concentrations were <10 g/dl in the treatment group compared with 88 (24.7%) in the control group; and 56 (15.7%) hemoglobin concentrations were >12 g/dl in the treatment group compared with 46 (13.4%) in the control group. The median ESA dosage per patient was 2000 IU/wk in both groups. Five participants received 6 transfusions (21 U) in the treatment group, compared with 8 participants and 13 transfusions (31 U) in the control group. These results suggest that individualized ESA dosing decreases total hemoglobin variability compared with a population protocol-based approach. As hemoglobin levels are declining in hemodialysis patients, decreasing hemoglobin variability may help reduce the risk of transfusions in this population.

TSAT is a better predictor than ferritin of hemoglobin response to Epoetin alfa in US dialysis patients.

Clinical guidelines recommend concurrent treatment of anemia in end-stage renal disease with erythropoiesis-stimulating agents (ESAs) and iron. However, there are mixed data about optimal iron supplementation. To help address this gap, the relationship between iron markers and hemoglobin (Hb) response to ESA (Epoetin alfa) dose was examined. Electronic medical records of 1902 US chronic hemodialysis patients were analyzed over a 12-month period between June 2009 and June 2010. The analysis included patients who had at least one Hb value during each 4-week interval for four consecutive intervals (k - 2, k - 1, k, and k + 1; k is the index interval), received at least one ESA dose during intervals k - 1 or k, had at least one transferrin saturation (TSAT) value at interval k, and at least one ferritin value during intervals k - 2, k - 1, or k. Effect modification by TSAT and ferritin on Hb response was evaluated using the generalized estimating equations approach. Patients had a mean (standard deviation) age of 62 (15) years; 41% were Caucasian, 34% African American, 65% had hypertension, and 39% diabetes. Transferrin saturation, but not ferritin, had a statistically significant (P < 0.05) modifying effect on Hb response. Maximum Hb response was achieved when TSAT was 34%, with minimal incremental effect beyond these levels. Of the two standard clinical iron markers, TSAT should be used as the primary marker of the modifying effect of iron on Hb response to ESA. Long-term safety of iron use to improve Hb response to ESA warrants further study.

Reticulocyte-based estimation of red blood cell lifespan.

We introduce a new, minimally invasive laboratory technique called reticulocyte-based estimation of lifespan (REBEL) of erythrocytes in humans. Its major advantage over existing techniques is its applicability to patients with both changing and steady-state erythropoiesis status. The feasibility of REBEL was tested in five patients with hemodialysis-dependent end-stage renal disease. The RNA degradation half-life was first determined for each subject on day 1 by flow cytometry measurement of the decay rate of thiazole orange stain. Reticulocyte age distribution was then measured from residual RNA content weekly for 2 months to estimate the RBC production rate time course. Mean RBC lifespan per subject was estimated by fitting the integrated RBC production rate over time to the measured RBC count and optimizing the integration limits. The mean reticulocyte RNA half-life was 0.71 ± 0.11 days. The small coefficient of variation (15.6%) indicated that the degradation rate of RNA did not vary substantially between subjects. The mean RBC lifespan (TRBC = 76.6 ± 23.8 days) was comparable to the reported values for this patient population.

Predictive modeling for improved anemia management in dialysis patients.

PURPOSE OF REVIEW: This review will explore the basic assumptions needed to perform predictive modeling of hemoglobin response to erythropoiesis stimulating agents (ESAs) and summarize the current literature in the area so that the practitioner can incorporate these tools as part of an improved anemia management process. RECENT FINDINGS: During the last year, several publications have demonstrated some advances in the field that may improve anemia management. The first of these was the publication of a randomized, controlled clinical trial of model predictive control in the dosing of erythropoietin. This work showed that hemoglobin variability can be decreased using predictive models of hemoglobin response. The second publication is potentially more interesting in the long run, as new markers of erythropoietin response were identified in a well-defined population of patients. SUMMARY: Predictive models of hemoglobin response improve anemia management by decreasing hemoglobin variability. This will result in more patients within the target range. Coupling these tools with new biomarkers of hemoglobin response has the potential to dramatically improve anemia management.

Oncostatin M receptor ß and cysteine/histidine-rich 1 are biomarkers of the response to erythropoietin in hemodialysis patients.

Biomarkers that evaluate the response to erythropoietic-stimulating agents largely measure inflammation and iron availability. While these are important factors in modifying an individual's response to these agents, they do not address all aspects of a poor response. To clarify this, we isolated peptides in the serum of good and poor responders to erythropoietin in order to identify biomarkers of stimulating agent response. Ninety-one candidate biomarker targets were identified and characterized using mass spectrometry, of which tandem mass spectroscopy provided partial amino-acid sequence information of 17 different peptides for 16 peptide masses whose abundance significantly differed between poor and good responders. The analysis concluded that three peptides associated with a poor response were derived from oncostatin M receptor ß (OSMRß). The 13 serum peptides associated with a good response were derived from fibrinogen α and ß, coagulation factor XIII, complement C3, and cysteine/histidine rich 1(CYHR1). Poor response was most strongly associated with the OSMRß fragment with the largest molecular weight, while a good response was most strongly associated with CYHR1. Immunoblots found the abundance of intact OSMRß and CYHR1 significantly differed between good and poor responders. Thus, two measurable biomarkers of the response to erythropoietic-stimulating agents are present in the serum of treated patients.

Determining optimum hemoglobin sampling for anemia management from every-treatment data.

BACKGROUND AND OBJECTIVES: Anemia management protocols in ESRD call for hemoglobin (Hb) monitoring every 2 to 4 weeks. Short-term Hb variability affects the reliability of Hb measurement and may lead to incorrect dosing of erythropoiesis stimulating agents. We prospectively analyzed short-term Hb variability and quantified the relationship between frequency of Hb monitoring and error in Hb estimation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using the Crit-Line III TQA device, we prospectively observed Hb during each dialysis treatment in 49 ESRD patients and quantified long- and short-term Hb variability. We estimated Hb from data sampled at regular intervals; 8×, 4×, 2×, or 1× per month to establish how well we account for short-term variability at different monitoring intervals. We calculated the Hb estimation error (Hb(err)) as a root mean-squared difference between the observed and estimated Hb and compared it with the measurement error. RESULTS: The most accurate Hb estimation is achieved when monitoring 8× per month (Hb(err) = 0.23 ± 0.05 g/dl), but it exceeds the accuracy of the measurement device. The estimation error increases to 0.34 ± 0.07 g/dl when monitoring 4× per month, 0.39 ± 0.08 g/dl when monitoring 2× a month, and 0.45 ± 0.09 g/dl when monitoring 1× per month. Estimation error comparable to instrument error information is as follows: 8× per month, 15 patients; 4× per month, 22 patients; 2× per month, 6 patients; 1× per a month, 6 patients. CONCLUSIONS: Four times a month is the clinically optimal Hb monitoring frequency for anemia management.