Kidney Medicine最新文献

Background

In the United States, diabetic kidney disease (DKD) affects about one-third of individuals with type 2 diabetes, causing significant economic burdens on the health care system and affecting patients’ quality of life.

Objective

The aim of the study was to quantify the burden of care in patients at different stages of DKD and to monitor shifts in healthcare costs throughout these stages.

Methods

This study used data from the Veterans Affairs National database, focusing on US veterans diagnosed with DKD between January 2016 and March 2022. Aggregated all-cause health care costs per month were summarized using descriptive statistics. We used a generalized linear model to calculate the cost of DKD patent care based on the stages, dialysis phase, and kidney replacement therapy.

Results

The cohort of 685,288 patients with DKD was predominantly male (96.51%), White (74.42%), and non-Hispanic (93.54%). The mean (SD) per-patient per-month costs were $1,597 ($3,178), $1,772 ($4,269), $2,857 ($13,072), $3,722 ($12,134), $5,505 ($14,639), and $6,999 ($16,901) for stages 1, 2, 3a, 3b, 4 and 5 respectively. The average monthly expenditure for patients receiving long-term dialysis was $12,299. Costs peaked sharply during the first month of kidney replacement therapy at $38,359 but subsequently decreased to $6,636 after 1 year.

Conclusions

The economic implications of DKD are profound, emphasizing the need for efficient early detection and disease management strategies. Preventing patients from progressing to advanced DKD stage will minimize the economic repercussions of DKD and will assist health care systems in optimizing resource allocation.

Plain-Language Summary

Diabetic kidney disease (DKD) places a substantial burden on health care systems in the United States. In part of our effort to close the knowledge gap around the disease burden, care cost analysis for the patients with DKD was performed for US veterans. Along with stage progression, overall care costs per-patient per-month drastically increases from $1,597 (stage 1) to $6,999 (stage 5). Monthly costs exceeded $10,000 once veterans started to receive long-term dialysis. The quantitative summary will help health care systems efficiently allocate resources across various disease sectors.

This review describes the history of vascular access for hemodialysis (HD) over the past 8 decades. Reliable, repeatable vascular access for outpatient HD began in the 1960s with the Quinton-Scribner shunt. This was followed by the autologous Brecia-Cimino radial-cephalic arteriovenous fistula (AVF), which dominated HD vascular access for the next 20 years. Delayed referral and the requirement of 1.5-3 months for AVF maturation led to the development of and increasing dependence on synthetic arteriovenous grafts (AVGs) and tunneled central venous catheters, both of which have higher thrombosis and infection risks than AVFs. The use of AVGs and tunneled central venous catheters increased progressively to the point that, in 1997, the first evidence-based clinical practice guidelines for HD vascular access recommended that they only be used if a functioning AVF could not be established. Efforts to promote AVF use in the United States during the past 2 decades doubled their prevalence; however, recent practice guidelines acknowledge that not all patients receiving HD are ideally suited for an AVF. Nonetheless, improved referral for AVF placement before dialysis initiation and improved conversion of failing AVGs to AVFs may increase AVF use among patients in whom they are appropriate.

The long-term mortality of patients with kidney failure remains unacceptably high. There are a multitude of reasons for the unfavorable status quo of dialysis care, such as the inadequate and suboptimal pattern of uremic toxin removal resulting in a metabolic and hemodynamic “roller coaster” induced by thrice-weekly in-center hemodialysis. Innovation in dialysis delivery systems is needed to build an adaptive and self-improving process to change the status quo of dialysis care with the aim of transforming it from being reactive to being proactive. The introduction of more physiologic and smart dialysis systems using artificial intelligence (AI) incorporating real-time data into the process of dialysis delivery is a realistic target. This would enable machine learning from both individual and collective patient treatment data. This has the potential to shift the paradigm from the practice of population-driven, evidence-based data to precision medicine. In this review, we describe the different components of an AI system, discuss the studied applications of AI in the field of dialysis, and outline parameters that can be used for future smart, adaptive dialysis delivery systems. The desired output is precision dialysis; a self-improving process that has the ability to prognosticate and develop instant and individualized predictive models.