Application of artificial intelligence to chronic kidney disease mineral bone disorder.

The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.

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.

Differential Functional Responses of Neutrophil Subsets in Severe COVID-19 Patients.

Neutrophils play a significant role in determining disease severity following SARS-CoV-2 infection. Gene and protein expression defines several neutrophil clusters in COVID-19, including the emergence of low density neutrophils (LDN) that are associated with severe disease. The functional capabilities of these neutrophil clusters and correlation with gene and protein expression are unknown. To define host defense and immunosuppressive functions of normal density neutrophils (NDN) and LDN from COVID-19 patients, we recruited 64 patients with severe COVID-19 and 26 healthy donors (HD). Phagocytosis, respiratory burst activity, degranulation, neutrophil extracellular trap (NET) formation, and T-cell suppression in those neutrophil subsets were measured. NDN from severe/critical COVID-19 patients showed evidence of priming with enhanced phagocytosis, respiratory burst activity, and degranulation of secretory vesicles and gelatinase and specific granules, while NET formation was similar to HD NDN. COVID LDN response was impaired except for enhanced NET formation. A subset of COVID LDN with intermediate CD16 expression (CD16Int LDN) promoted T cell proliferation to a level similar to HD NDN, while COVID NDN and the CD16Hi LDN failed to stimulate T-cell activation. All 3 COVID-19 neutrophil populations suppressed stimulation of IFN-γ production, compared to HD NDN. We conclude that NDN and LDN from COVID-19 patients possess complementary functional capabilities that may act cooperatively to determine disease severity. We predict that global neutrophil responses that induce COVID-19 ARDS will vary depending on the proportion of neutrophil subsets.

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.

Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management.

Administration of drugs requires sophisticated methods to determine the drug quantity for optimal results, and it has been a challenging task for the number of diseases. To solve these challenges, in this paper, we present the semi-blind robust model identification technique to find individualized patient models using the minimum number of clinically acquired patient-specific data to determine optimal drug dosage. To ensure the usability of these models for dosage predictability and controller design, the model (In)validation technique is also investigated. As a case study, the patients treated with warfarin are studied to demonstrate the semi-blind robust identification and model (In)validation techniques. The performance of models is assessed by calculating minimum means squared error (MMSE).Clinical Relevance- This work establishes a general framework for adaptive individualized drug-dose response models from a limited number of clinical patient-specific data. This work will help clinicians in decision-making for improved drug dosing, patient care, and limiting patient exposure to agents with a narrow therapeutic range.

Precise Warfarin Management through Personalized Modeling and Control with Limited Clinical Data.

Warfarin belongs to a medication class called anticoagulants or blood thinners. It is used for the treatment to prevent blood clots from forming or growing larger. Patients with venous thrombosis, pulmonary embolism, or who have suffered a heart attack, have an irregular heartbeat, or prosthetic heart valves are prescribed with warfarin. It is challenging to find optimal doses due to inter-patient and intra-patient variabilities and narrow therapeutic index. This work presents an individualized warfarin dosing method by utilizing the individual patient model generated using limited clinical data of the patients with chronic conditions under warfarin anticoagulation treatment. Then, the individual precise warfarin dosing is formalized as an optimal control problem, which is solved using the DORBF control approach. The efficiency of the proposed approach is compared with results obtained from practiced clinical protocol.

Na/H Exchange Regulatory Factor 1 Deficient Mice Show Evidence of Oxidative Stress and Altered Cisplatin Pharmacokinetics.

(1) Background: One third of patients who receive cisplatin develop an acute kidney injury. We previously demonstrated the Na/H Exchange Regulatory Factor 1 (NHERF1) loss resulted in increased kidney enzyme activity of the pentose phosphate pathway and was associated with more severe cisplatin nephrotoxicity. We hypothesized that changes in proximal tubule biochemical pathways associated with NHERF1 loss alters renal metabolism of cisplatin or response to cisplatin, resulting in exacerbated nephrotoxicity. (2) Methods: 2-4 month-old male wild-type and NHERF1 knock out littermate mice were treated with either vehicle or cisplatin (20 mg/kg dose IP), with samples taken at either 4, 24, or 72 h. Kidney injury was determined by urinary neutrophil gelatinase-associated lipocalin and histology. Glutathione metabolites were measured by HPLC and genes involved in glutathione synthesis were measured by qPCR. Kidney handling of cisplatin was assessed by a kidney cortex measurement of γ-glutamyl transferase activity, Western blot for γ-glutamyl transferase and cysteine S-conjugate beta lyase, and ICP-MS for platinum content. (3) Results: At 24 h knock out kidneys show evidence of greater tubular injury after cisplatin and exhibit a decreased reduced/oxidized glutathione ratio under baseline conditions in comparison to wild-type. KO kidneys fail to show an increase in γ-glutamyl transferase activity and experience a more rapid decline in tissue platinum when compared to wild-type. (4) Conclusions: Knock out kidneys show evidence of greater oxidative stress than wild-type accompanied by a greater degree of early injury in response to cisplatin. NHERF1 loss has no effect on the initial accumulation of cisplatin in the kidney cortex but is associated with an altered redox status which may alter the activity of enzymes involved in cisplatin metabolism.

Plasma Cytokine Profiling to Predict Steroid Resistance in Pediatric Nephrotic Syndrome.

INTRODUCTION: Glucocorticoids (GCs) are the primary treatment for nephrotic syndrome (NS), although ∼10% to 20% of children develop steroid-resistant NS (SRNS). Unfortunately, there are no validated biomarkers able to predict SRNS at initial disease presentation. We hypothesized that a plasma cytokine panel could predict SRNS at disease presentation, and identify potential pathways regulating SRNS pathogenesis. METHODS: Paired plasma samples were collected from 26 children with steroid-sensitive NS (SSNS) and 14 with SRNS at NS presentation and after ∼7 weeks of GC therapy, when SSNS versus SRNS was clinically determined. Plasma cytokine profiling was performed with a panel of 27 cytokines. RESULTS: We identified 13 cytokines significantly different in Pretreatment SSNS versus SRNS samples. Statistical modeling identified a cytokine panel (interleukin [IL]-7, IL-9, monocyte chemoattractant protein-1 [MCP-1]) able to discriminate between SSNS and SRNS at disease presentation (receiver operating characteristic [ROC] value = 0.846; sensitivity = 0.643; specificity = 0.846). Furthermore, GC treatment resulted in significant decreases in plasma interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), IL-7, IL-13, and IL-5 in both SSNS and SRNS patients. CONCLUSIONS: These studies suggest that initial GC treatment of NS reduces the plasma cytokines secreted by both CD4+ TH1 cells and TH2 cells, as well as CD8+ T cells. Importantly, a panel of 3 cytokines (IL-7, IL-9, and MCP-1) was able to predict SRNS prior to GC treatment at disease presentation. Although these findings will benefit from validation in a larger cohort, the ability to identify SRNS at disease presentation could greatly benefit patients by enabling both avoidance of unnecessary GC-induced toxicity and earlier transition to more effective alternative treatments.

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.

Serum trace metal association with response to erythropoiesis stimulating agents in incident and prevalent hemodialysis patients.

Alterations in hemodialysis patients' serum trace metals have been documented. Early studies addressing associations levels of serum trace metals with erythropoietic responses and/or hematocrit generated mixed results. These studies were conducted prior to current approaches for erythropoiesis stimulating agent (ESA) drug dosing guidelines or without consideration of inflammation markers (e.g. hepcidin) important for regulation of iron availability. This study sought to determine if the serum trace metal concentrations of incident or chronic hemodialysis patients associated with the observed ESA response variability and with consideration to ESA dose response, hepcidin, and high sensitivity C-reactive protein levels. Inductively-coupled plasma-mass spectrometry was used to measure 14 serum trace metals in 29 incident and 79 prevalent dialysis patients recruited prospectively. We compared these data to three measures of ESA dose response, sex, and dialysis incidence versus dialysis prevalence. Hemoglobin was negatively associated with ESA dose and cadmium while positively associated with antimony, arsenic and lead. ESA dose was negatively associated with achieved hemoglobin and vanadium while positively associated with arsenic. ESA response was positively associated with arsenic. Vanadium, nickel, cadmium, and tin were increased in prevalent patients. Manganese was increased in incident patients. Vanadium, nickel, and arsenic increased with time on dialysis while manganese decreased. Changes in vanadium and manganese were largest and appeared to have some effect on anemia. Incident and prevalent patients' chromium and antimony levels exceeded established accepted upper limits of normal.

Proteomic Analysis Identifies Distinct Glomerular Extracellular Matrix in Collapsing Focal Segmental Glomerulosclerosis.

BACKGROUND: The mechanisms leading to extracellular matrix (ECM) replacement of areas of glomerular capillaries in histologic variants of FSGS are unknown. This study used proteomics to test the hypothesis that glomerular ECM composition in collapsing FSGS (cFSGS) differs from that of other variants. METHODS: ECM proteins in glomeruli from biopsy specimens of patients with FSGS not otherwise specified (FSGS-NOS) or cFSGS and from normal controls were distinguished and quantified using mass spectrometry, verified and localized using immunohistochemistry (IHC) and confocal microscopy, and assessed for gene expression. The analysis also quantified urinary excretion of ECM proteins and peptides. RESULTS: Of 58 ECM proteins that differed in abundance between cFSGS and FSGS-NOS, 41 were more abundant in cFSGS and 17 in FSGS-NOS. IHC showed that glomerular tuft staining for cathepsin B, cathepsin C, and annexin A3 in cFSGS was significantly greater than in other FSGS variants, in minimal change disease, or in membranous nephropathy. Annexin A3 colocalized with cathepsin B and C, claudin-1, phosphorylated ERK1/2, and CD44, but not with synaptopodin, in parietal epithelial cells (PECs) infiltrating cFSGS glomeruli. Transcripts for cathepsins B and C were increased in FSGS glomeruli compared with normal controls, and urinary excretion of both cathepsins was significantly greater in cFSGS compared with FSGS-NOS. Urinary excretion of ECM-derived peptides was enhanced in cFSGS, although in silico analysis did not identify enhanced excretion of peptides derived from cathepsin B or C. CONCLUSIONS: ECM differences suggest that glomerular sclerosis in cFSGS differs from that in other FSGS variants. Infiltration of activated PECs may disrupt ECM remodeling in cFSGS. These cells and their cathepsins may be therapeutic targets.

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.

Effects of body protection vests and experience levels in prevention of equestrian injuries.

OBJECTIVES: To investigate the risk reduction and benefit of wearing body protection/safety vests in equestrian sports. METHODS: A comparison of equestrians wearing body protective vests and those not wearing vests was performed using incident report data of 718 participants in the United States Pony Clubs during 2011-2017. Data obtained included age, gender, certification level of member, type of activity, description of incident, description of injuries, what protective equipment was worn and other possible contributing factors. RESULTS: While wearing body protective vests when riding on the flat or for show jumping was not correlated with a decrease in injuries, wearing vests for cross country was correlated with decrease in reported injuries (p=0.036) and showed a trend towards a lower incident severity level (p=0.062). Wearing body protection during cross country reduced the relative risk of injury by 56%. While the volume of incidents varied with a rider's experience level, the number of serious injuries did not appear to correlate with lesser equestrian experience. CONCLUSIONS: While equestrian sports are considered to have a certain degree of risk associated with them, there are ways to make them safer. Wearing safety equipment, such as helmets and body protection, and obtaining education and experience can lessen the chance of incurring serious injuries.

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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Biomarker enhanced risk prediction for development of AKI after cardiac surgery.

BACKGROUND: Acute kidney injury (AKI) is a common post-cardiac surgery complication and influences patient morbidity and prognosis. This study was designed to identify preoperative candidate urine biomarkers in patients undergoing cardiac surgery. METHODS: A prospective cohort study of adults undergoing cardiac surgery at increased risk for AKI at a single hospital between July 2010 and September 2012 was performed. The primary outcome was the development of AKI, defined as an absolute serum creatinine (SCr) level increase ≥ 0.5 mg/dL or a ≥ 50% relative increase within 72 h of surgery. A secondary outcome was development of AKI defined by Kidney Disease Improving Global Outcomes (KDIGO). Urine collected by voiding within 4 h prior to surgery was used for proteomic analysis and confirmatory enzyme linked immunosorbent assays (ELISAs) studies. Biomarkers were tested for AKI-prediction using Cox and Snell R2, area under the receiver operating curve (AUROC), and percent of corrected classifications. To evaluate the added effect of each candidate biomarker on AKI discrimination, receiver operator characteristic (ROC) curves, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated. RESULTS: Forty-seven of 755 patients met screening criteria including 15 with AKI. Proteomic analysis identified 29 proteins with a significant ≥2-fold change. Confirmatory ELISA measurements of five candidate markers showed urinary complement factor B (CFB) and histidine rich glycoprotein (HRG) concentrations were significantly increased in patients with AKI. By multivariate analysis, NRI, and IDI the addition of CFB and HRG to the standard clinical assessment significantly improved risk prediction for the primary outcome. Only HRG was a significant predictor in the 21 patients with AKI defined by KDIGO criteria. CONCLUSIONS: Pre-operative urine measurement of CFB or HRG significantly enhanced the current post-surgery AKI risk stratification for more restrictive definition of AKI. HRG, but not CFB or clinical risk stratification, predicted AKI defined by KDIGO. The ability of these biomarkers to predict risk for dialysis-requiring AKI or death could not be reliably assessed in our study due to a small number of patients with either outcome.

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.

Rate of hepatitis C viral clearance by human livers in human patients: Liver transplantation modeling primary infection and implications for studying entry inhibition.

To better understand the dynamics of early hepatitis C virus (HCV) infection, we determined how rapidly non-cirrhotic HCV-uninfected liver allografts clear HCV from the circulation of cirrhotic HCV-infected patients at the time of transplantation but before administration of immunosuppression. Specifically, we characterized serum HCV kinetics during the first 90 min of reperfusion for 19 chronically HCV-infected patients transplanted with an HCV-uninfected liver by measuring serum viral load immediately prior to reperfusion (t = 0) and then every 15 min for a total of 90 min (t = 90). Immunosuppression was withheld until all samples were taken to better model primary infection. During this period, rates of viral clearance varied more than 20-fold with a median rate constant of 0.0357 1/min, range 0.0089-0.2169; half-life (minutes) median 19.4, range 3.2-77.8. The majority of viral clearance occurred within the first 60 min. The amount of blood transfused during this 90-min period (a potential confounding variable of this human liver transplant model of primary infection) accounted for 53% and 59% of k (r = 0.53, p = 0.05) and half-life (r = 0.59, p = 0.03) variability, respectively. No other clinical variables tested (age, allograft weight, and degree of reperfusion injury as assessed by peak postoperative ALT or AST) accounted for the remaining variability (p>0.05). CONCLUSION: In a human liver transplant model of primary infection, HCV rapidly clears the bloodstream. With approximately 90% of clearance occurring in the first 90 minutes of reperfusion, studies of HCV entry inhibition could utilize rate of clearance during this early period as an outcome measure.

Pediatric Hypertension: Are Pediatricians Following Guidelines?

The National Heart, Lung, and Blood Institute recommends that children older than 3 years seen in the medical setting have their blood pressure (BP) measured. The authors aimed to determine whether BPs are measured at well-child visits and whether elevated readings are recognized. A retrospective chart review of 3- to 18-year-old children seen for well-child visits was performed. Age, sex, weight, height, BP, extremity measured, and type of intervention were collected. BP was measured in 777 of 805 patients (97%). BP was elevated in 158 patients (20%). A total of 95 patients (60%) did not receive any intervention. Not recognizing elevated BP was associated with increased daily patient load (17.9±6.5 vs 12.6±5.5, P=.001). Higher body mass index was associated with elevated BP (P=.0008) but was not associated with improved recognition. Findings show that BP is almost always measured at well-child visits but is not being measured appropriately, and general pediatric clinics are not consistently following BP management recommendations.