Michael E. Brier

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. 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. 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. 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. .

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.

Background and objectives: Variable hemoglobin (Hb) response to erythropoiesis stimulating agents may result in adverse outcomes. The utility of model predictive control for drug dosing was previously demonstrated. Design, setting, participants, & measurements: This was a double-blinded, randomized, controlled trial to test model predictive control for dosing erythropoietin in ESRD patients. The trial included 60 hemodialysis patients who were randomized into a treatment arm (30 subjects) that received erythropoietin doses on the basis of the computer recommendations or a control arm (30 subjects) that received erythropoietin doses on the basis of recommendations from a standard anemia management protocol (control). The subjects were followed for 8 months, and the proportions of measured Hb within the target of 11 to 12 g/dl and outside 9 to 13 g/dl were measured. Variability of the Hb level was measured by the absolute difference between the achieved Hb and the target Hb of 11.5 g/dl as well as the area under the Hb curve. Results: Model predictive control resulted in 15 observations >13 or P = 0.051), produced a mean absolute difference between achieved Hb and 11.5 g/dl of 1.18 ± 0.18 g/dl (P < 0.001 difference in variance), and an area under the Hb curve of 3.38 ± 2.69 (P = 0.025 difference in variance). Conclusions: Model predictive control of erythropoietin administration improves anemia management.

Delayed graft function (DGF) is one of the most important complications in the post-transplant period, having an adverse effect on both the immediate and long-term graft survival. In this study, an artificial neural network was used to predict the occurrence of DGF and compared with traditional logistical regression models for prediction of DGF. A total of 304 cadaveric renal transplants performed at the Jewish Hospital, Louisville were included in the study. Covariate analysis by artificial neural networks and traditional logistical regression were done to predict the occurrence of DGF. The incidence of DGF in this study was 38%. Logistic regression analysis was more sensitive to prediction of no DGF (91 vs 70%), while the neural network was more sensitive to prediction of yes for DGF (56 vs 37%). Overall prediction accuracy for both logistic regression and the neural network was 64 and 63%, respectively. Logistic regression was 36.5% sensitive and 90.7% specific. The neural network was 63.5% sensitive and 64.8% specific. The only covariate with a P < 0.001 was the transplant of a white donor kidney to a black recipient. Cox proportional hazard regression was used to test for the negative effect of DGF on long-term graft survival. One year graft survival in patients without DGF was 92 +/- 2% vs 81 +/- 3% in patients with DGF. The 5-year graft survival was not affected by DGF in this study. Artificial neural networks may be used for prediction of DGF in cadaveric renal transplants. This method is more sensitive but less specific than logistic regression methods.

Linezolid is a member of a new, unique class of synthetic antibacterial agents called oxazolidinones that are effective against gram-positive bacteria, including vancomycin-resistant organisms. We tested the hypothesis that the linezolid clearance would not be altered in subjects with renal dysfunction. Twenty-four subjects with renal function that ranged from normal to severe chronic impairment were enrolled, including patients with end-stage renal disease who were maintained on hemodialysis. Hemodialysis subjects were studied while they were both on and off dialysis. Linezolid was administered as a single oral 600-mg dose, and plasma and urine samples were assayed for linezolid and metabolites for 48 h for all subjects and for up to 96 h for those subjects with impaired renal function not on dialysis. The total apparent oral clearance of linezolid did not change with renal function and ranged from 92.5 to 109.6 ml/min for subjects not requiring dialysis. For subjects on dialysis, the total apparent oral clearance increased from 76.6 ml/min on their off-dialysis day to 130.0 ml/min on their on-dialysis day. Approximately one-third of the dose was removed by dialysis. However, those subjects with severe renal insufficiency (creatinine clearance, <40 ml/min) and those with end-stage renal disease maintained on hemodialysis had higher concentrations of both metabolites. We conclude that no adjustment of the linezolid dosage is needed in subjects with renal dysfunction or subjects on hemodialysis.