Individualized anemia management in a dialysis facility - long-term utility as a single-center quality improvement experience

The Use of Anemia Control Model Is Associated with Improved Hemoglobin Target Achievement, Lower Rates of Inappropriate Erythropoietin Stimulating Agents, and Severe Anemia among Dialysis Patients

INTRODUCTION

The Anemia Control Model (ACM) is a certified medical device suggesting the optimal ESA and iron dosage for patients on hemodialysis. We sought to assess the effectiveness and safety of ACM in a large cohort of hemodialysis patients.

METHODS

This is a retrospective study of dialysis patients treated in NephroCare centers between June 1, 2013 and December 31, 2019. We compared patients treated according to ACM suggestions and patients treated in clinics where ACM was not activated. We stratified patients belonging to the reference group by historical target achievement rates in their referral centers (tier 1: <70%; tier 2: 70-80%; tier 3: >80%). Groups were matched by propensity score.

RESULTS

After matching, we obtained four groups with 85,512 patient-months each. ACM had 18% higher target achievement rate, 63% smaller inappropriate ESA administration rate, and 59% smaller severe anemia risk compared to Tier 1 centers (all p < 0.01). The corresponding risk ratios for ACM compared to Tier 2 centers were 1.08 (95% CI: 1.08-1.09), 0.49 (95% CI: 0.47-0.51), and 0.64 (95% CI: 0.61-0.68); for ACM compared to Tier 3 centers, 1.01 (95% CI: 1.01-1.02), 0.66 (95% CI: 0.63-0.69), and 0.94 (95% CI: 0.88-1.00), respectively. ACM was associated with statistically significant reductions in ESA dose administration.

CONCLUSION

ACM was associated with increased hemoglobin target achievement rate, decreased inappropriate ESA usage and a decreased incidence of severe anemia among patients treated according to ACM suggestion.

Multi-head self-attention mechanism enabled individualized hemoglobin prediction and treatment recommendation systems in anemia management for hemodialysis patients

Anemia is a critical complication in hemodialysis patients, but the response to erythropoietin-stimulating agents (ESA) treatment varies from patient to patient and is not linear across different time points. The aim of this study was to develop deep learning algorithms for individualized anemia management. We retrospectively collected 36,677 data points from 623 hemodialysis patients, including clinical data, laboratory values, hemoglobin levels, and previous ESA doses. To reduce the computational complexity associated with recurrent neural networks (RNN) in processing time-series data, we developed neural networks based on multi-head self-attention mechanisms in an efficient and effective hemoglobin prediction model. Our proposed model achieved a more accurate hemoglobin prediction than the state-of-the-art RNN model, as shown by the smaller mean absolute error (MAE) of hemoglobin (0.451 vs. 0.593 g/dL, p = 0.014). In ESA (including darbepoetin and epoetin) dose recommendation, the simulation results by our model revealed a higher rate of achieved hemoglobin targets (physician prescription vs. model: 86.3 % vs. 92.7 %, p < 0.001), a lower rate of hemoglobin levels below 10 g/dL (13.7 % vs. 7.3 %, p < 0.001) and smaller change in hemoglobin levels (0.6 g/dL vs. 0.4 g/dL, p < 0.001) in all patients. Our model holds great potential for individualized anemia management as a computerized clinical decision support system for hemodialysis patients. Further external validation with other datasets and prospective clinical utility studies are warranted.

Physician Compliance with Computerized Clinical Decision Support System is a Complete Intermediate Factor in the Anemia Management of Patients with End-Stage Kidney Disease on Hemodialysis: A Retrospective Electronic Health Record Observational Study (Preprint)

BACKGROUND

Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined.

OBJECTIVE

We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia.

METHODS

We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions.

RESULTS

We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years; male n=430, 59.9%) with a total of 36,091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%; post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI 158-371) units per week, and 3.4-fold (95% CI 3.1-3.6) increased concordance rate. However, the on-target rate (29%; odds ratio 0.71, 95% CI 0.66-0.75) and failure rate (16%; odds ratio 0.84, 95% CI 0.76-0.92) were reduced. After additional adjustments for concordance in the full models, increased hemoglobin and decreased on-target rate tended toward attenuation (from 0.17 to 0.13 g/dL and 0.71 to 0.73 g/dL, respectively). Increased ESA and decreased failure rate were completely mediated by physician compliance (from 264 to 50 units and 0.84 to 0.97, respectively).

CONCLUSIONS

Our results confirmed that physician compliance was a complete intermediate factor accounting for the efficacy of the CDSS. The CDSS reduced failure rates of anemia management through physician compliance. Our study highlights the importance of optimizing physician compliance in the design and implementation of CDSSs to improve patient outcomes.

Routine laboratory testing in hemodialysis: how frequently is it needed?

Background:

Hemodialysis patients are followed by routine laboratory testing. There is uncertainty whether these tests always lead to a change in decision-making. This study aims to discover the number of yearly interventions/changes in prescription based on these tests and depict the group of patients who would benefit from reduced or increased laboratory blood tests.

Methods:

This is a multi-center retrospective study that included patients on hemodialysis for more than one year. Laboratory data collected included yearly average of hemoglobin, urea reduction ratio (URR), serum phosphate, calcium, potassium, parathormone (PTH), ferritin and transferrin saturation (TSAT); changes in prescription of erythropoietin-stimulating agents (ESAs), intravenous (IV) iron, alfacalcidol, phosphate binders and dialysis parameters were retrieved from medical records. A multivariate regression analysis assessed factors associated with high number of interventions.

Results:

A total of 210 hemodialysis patients were included: 62.4% males, 47.1% diabetics. Their median age was 72 (62,78.5) years. Their laboratory parameters were within KDIGO targets. The median number of yearly interventions was 5 (3,7) for ESAs, 4 (2,6) for IV iron, 1 (0,2.25) for phosphate binders, 0 (0,1) for alfacalcidol. Based on the multivariate analysis, patients with higher ferritin, frequent changes in ESA, more changes in alfacalcidol and higher PTH had higher number of prescription’s changes in ESA, IV iron, phosphate binders and alfacalcidol respectively.

Conclusion:

While maintaining KDIGO targets, therapeutic interventions following routine laboratory testing did not exceed six times yearly for all parameters. This suggests that a reduced testing frequency in hemodialysis patients is possible without any impact on quality of care. A personalized approach remains safe for hemodialysis patients while reducing the cost. This is very relevant in low-resource settings and during economic crises and needs to be evaluated in prospective studies.

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.

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

OBJECTIVE

Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures.

METHODS

We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts.

RESULTS

We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process.

DISCUSSION

We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.

Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy

In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 μg (ESAs dose), respectively. Compared to the results in the real-world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 μg/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%.

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.

Association between Satisfaction with Dialysis Treatment and Quality of Life: A Cross-Sectional Study

INTRODUCTION

CKD is associated with a reduction of patients' health-related quality of life. Considering the time spent in dialysis, satisfaction with care is essential for patients QOL.

OBJECTIVE

Since the possible association between satisfaction with the dialysis care and QOL has never been studied, in this study, we explore this plausible link.

METHODS

One hundred three patients on hemodialysis (HD) and peritoneal dialysis (PD) filled-in patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs). QOL was assessed by Kidney Disease Quality of Life-36 and satisfaction by Choices for Healthy Outcomes in Caring for End-Stage Renal Disease (CHOICE) questionnaire. The analysis was conducted on patient-level, considering for single patient sociodemographic characteristics and presence of depression/anxiety. One-way ANOVA was used to compare QOL mean scores for patients who answered "excellent" and for those who answered "other ratings" in CHOICE questionnaire and the Pearson χ2 test to compare the patients' characteristics between these 2 groups of patients.

RESULTS

The analysis showed a significant positive association between PREM and PROM scores for 8 out of 23 CHOICE items. Six of them were related to the figure of nephrologist, 1 to dialysis access site, and 1 to the social worker support. Significant association (p < 0.05) were between frequency of seeing nephrologist and physical component plus mental component, accuracy of information from nephrologist and burden of disease, accuracy of instructions from nephrologist and burden of disease, coordination between nephrologist and other physicians plus mental component, attention to cleanliness of access site and mental component, amount of dialysis information from staff and burden of disease, information from staff when choosing between HD or PD and physical component plus burden of disease, and ease of seeing social worker and burden of disease.

CONCLUSIONS

The study provides support for the relationship between the care satisfaction and QOL, highlighting the central role of the nephrologist-patient communication in the QOL of dialysis patients.

A pharmacist-managed dosing algorithm for darbepoetin alfa and iron sucrose in hemodialysis patients: A randomized, controlled trial

The attainment of target hemoglobin levels in hemodialysis patients is low. Several factors play a role, such as hyporesponsiveness to erythropoiesis-stimulating agents (ESA), but also suboptimal prescribing of ESA and iron. The goal of this study was to investigate if a pharmacist-managed dosing algorithm for darbepoetin alfa (DA) and iron sucrose improves the attainment of target hemoglobin levels. In this randomized controlled trial, 200 hemodialysis patients from a Dutch teaching hospital were included. In the intervention group (n = 100), a pharmacist monthly provided dose recommendations for DA and iron sucrose based on dosing algorithms. The control group (n = 100) received usual care. In the intervention group, the percentage per patient within the target range (PTR) for hemoglobin (target range 6.8-7.4 mmol/L) and iron status was higher than in the control group (for hemoglobin median 38.5% vs 23.1%, P = .001 and for iron status median 21.1% vs 8.3%, P = .003). The percentage of high hemoglobin levels (>8.1 mmol/L) was lower in the intervention group (median 0.0% vs 7.7%, P = .034). The weekly dose of DA was lower in the intervention group (median 34.0 vs 46.9 mcg, P = .020), whereas iron dose was higher (median 75 vs 0 mg). No difference was found for the percentage of hemoglobin levels below the target range. In conclusion, a pharmacist-managed dosing algorithm for DA and iron sucrose increased the attainment of target levels for hemoglobin and iron status, reduced the percentage of high hemoglobin levels, and was associated with a lower DA and a higher iron sucrose dose.