The anaemia control model: Does it help nephrologists in therapeutic decision-making in the management of anaemia?

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.

Predictors of iron versus erythropoietin responsiveness in anemic hemodialysis patients

Anemia protocols for hemodialysis patients usually titrate erythropoietin (ESA) according to hemoglobin and iron according to a threshold of ferritin, with variable response seen. A universally optimum threshold for ferritin may be incorrect, and another view is that ESA and iron are alternative anemia treatments, which should be selected based on the likely response to each. Hemodialysis patients developing moderate anemia were randomised to treatment with either an increase in ESA or a course of intravenous iron. Over 2423 patient-months in 197 patients, there were 133 anemia episodes with randomized treatment. Treatment failure was seen in 20/66 patients treated with ESA and 20/67 patients treated with iron (30.3 vs. 29.9%, p = 1.0). Successful ESA treatment was associated with lower C-reactive protein (13.5 vs. 28.6 mg/L, p = 0.038) and lower previous ESA dose (6621 vs. 9273 μg/week, p = 0.097). Successful iron treatment was associated with lower reticulocyte hemoglobin (33.8 vs. 35.5 pg, p = 0.047), lower hepcidin (91.4 vs. 131.0 μg/ml, p = 0.021), and higher C-reactive protein (29.5 vs. 12.6 mg/L, p = 0.085). A four-variable iron preference score was developed to indicate the more favorable treatment, which in a retrospective analysis reduced treatment failure to 17%. Increased ESA and iron are equally effective, though treatment failure occurs in almost 30%. Baseline variables including hepcidin can predict treatment response, and a four-variable score shows promise in allowing directed treatment with improved response rates.

Hidden risks associated with conventional short intermittent hemodialysis: A call for action to mitigate cardiovascular risk and morbidity

The development of maintenance hemodialysis (HD) for end stage kidney disease patients is a success story that continues to save many lives. Nevertheless, intermittent renal replacement therapy is also a source of recurrent stress for patients. Conventional thrice weekly short HD is an imperfect treatment that only partially corrects uremic abnormalities, increases cardiovascular risk, and exacerbates disease burden. Altering cycles of fluid loading associated with cardiac stretching (interdialytic phase) and then fluid unloading (intradialytic phase) likely contribute to cardiac and vascular damage. This unphysiologic treatment profile combined with cyclic disturbances including osmotic and electrolytic shifts may contribute to morbidity in dialysis patients and augment the health burden of treatment. As such, HD patients are exposed to multiple stressors including cardiocirculatory, inflammatory, biologic, hypoxemic, and nutritional. This cascade of events can be termed the dialysis stress storm and sickness syndrome. Mitigating cardiovascular risk and morbidity associated with conventional intermittent HD appears to be a priority for improving patient experience and reducing disease burden. In this in-depth review, we summarize the hidden effects of intermittent HD therapy, and call for action to improve delivered HD and develop treatment schedules that are better tolerated and associated with fewer adverse effects.

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%.

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

Hemodialysis has saved many lives, albeit with significant residual mortality. Although poor outcomes may reflect advanced age and comorbid conditions, hemodialysis per se may harm patients, contributing to morbidity and perhaps mortality. Systemic circulatory "stress" resulting from hemodialysis treatment schedule may act as a disease modifier, resulting in a multiorgan injury superimposed on preexistent comorbidities. New functional intradialytic imaging (i.e., echocardiography, cardiac magnetic resonance imaging [MRI]) and kinetic of specific cardiac biomarkers (i.e., Troponin I) have clearly documented this additional source of end-organ damage. In this context, several factors resulting from patient-hemodialysis interaction and/or patient management have been identified. Intradialytic hypovolemia, hypotensive episodes, hypoxemia, solutes, and electrolyte fluxes as well as cardiac arrhythmias are among the contributing factors to systemic circulatory stress that are induced by hemodialysis. Additionally, these factors contribute to patients' symptom burden, impair cognitive function, and finally have a negative impact on patients' perception and quality of life. In this review, we summarize the adverse systemic effects of current intermittent hemodialysis therapy, their pathophysiologic consequences, review the evidence for interventions that are cardioprotective, and explore new approaches that may further reduce the systemic burden of hemodialysis. These include improved biocompatible materials, smart dialysis machines that automatically may control the fluxes of solutes and electrolytes, volume and hemodynamic control, health trackers, and potentially disruptive technologies facilitating a more personalized medicine approach.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

BACKGROUND

The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used?

METHODS

We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm.

RESULTS

AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues.

CONCLUSIONS

Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

Role of Artificial Intelligence in Kidney Disease

Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.