Risk factors for increased variability in dialysis delivery in haemodialysis patients

Brimble, K. Scott; Treleaven, Darin; Onge, Joye St.; Carlisle, Euan

doi:10.1093/ndt/gfg297

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…This monthly measurement is subsequently assumed to represent all dialysis treatments throughout the month; however, basing clinical decisions on one measurement may not be adequate. [14] There is a significant degree of variation between dialysis treatments, [15] with non-compliance to the prescribed dialysis the most cited culprit. [16][17][18] Furthermore, patients who are non-adherent to their dialysis prescription or skip sessions have higher mortality.…”

Section: Discussionmentioning

confidence: 99%

Estimating Dialysis Adequacy Using Ionic Dialysance

Carl

Feldman

2008

Renal Failure

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The aim of this study was to determine the efficacy and feasibility of estimating dialysis adequacy using ionic dialysance (ID). We retrospectively reviewed the medical records of patients receiving thrice weekly dialysis for an eight-month period at a single-center Veterans Affairs hospital. Dialysis adequacy was determined monthly using pre- and post-treatment BUN measurements to calculate the single pool Kt/V (spKt/V) with the formula set forth by Daugirdas. On the same treatment day, K(ID)t was determined by multiplying the average ID times the time (t) of the treatment. A surrogate volume, V(ID), was estimated by dividing K(ID)t by spKt/V using data from the first six months. During the subsequent two months, we compared dialysis adequacy estimated by urea-based spKt/V to ionic dialysance based K(ID)t/V(ID) utilizing V(ID). In the first month, K(ID)t/V(ID) estimations and the simultaneous spKt/V determinations averaged 1.55 +/- 0.36 and 1.59 +/- 0.42, respectively. In the second month, K(ID)t/V(ID) and spKt/V averaged 1.52 +/- 0.33 and 1.54 +/- 0.35, respectively. K(ID)t/V(ID) correlated well with spKt/V, as the slope was 0.85 (r = 0.95, p < 0.001). There was considerable intra-patient variability of K(ID), time, and K(ID)t/V(ID) with coefficient of variations (CV) of 8.4 +/- 4.3, 9.0 +/- 5.3, and 15.8 +/- 9.2, respectively. However, the CV for K(ID)t/V(ID) was similar to the CV for spKt/V (15.3 +/- 7.4). These results suggest that it is possible to estimate dialysis adequacy during every treatment using K(ID)t/V(ID). Furthermore, there is considerable variability in the delivered dialysis adequacy, suggesting that many sessions result in sub-optimal dialysis.

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Section: Discussionmentioning

confidence: 99%

Estimating Dialysis Adequacy Using Ionic Dialysance

Carl

Feldman

2008

Renal Failure

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“…The clustering of URR values to facilities are reported by some as reflective of facility‐specific URR sampling practices . Evidence suggest that facility URRs largely overestimate clearances provided; when blood volume processed is measured, it is higher on URR draw days than other days, and depends more on nursing factors than patient factors . Keeping blood flow rates above 350 mL/min, in one study, was effective in improving outcomes, especially in women and older individuals .…”

Section: Stakeholder‐level Risks To Hospitalizations In Patient On Hementioning

confidence: 99%

“…[130][131][132] Evidence suggest that facility URRs largely overestimate clearances provided; when blood volume processed is measured, it is higher on URR draw days than other days, and depends more on nursing factors than patient factors. [132][133][134] Keeping blood flow rates above 350 mL/min, in one study, was effective in improving outcomes, especially in women and older individuals. 62 Neither vascular access surveillance nor recirculation measurements (important to maintaining good clearances) are routinely carried out or are inaccurately performed.…”

Section: Psychosocialmentioning

confidence: 99%

Decreasing hospitalizations in patients on hemodialysis: Time for a paradigm shift

Golestaneh

2018

Seminars in Dialysis

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Hospitalizations drive up to 35% of the astronomical costs of care for patients on hemodialysis and are associated with poor outcomes. We describe outpatient care-sensitive categories of hospitalization risks in an effort to engage stakeholders and patients, as stakeholders, in mitigating hospitalizations. These categories include: (1) fluid (interdialytic weight gain (IDWG) and chronic volume status), (2) infection (vascular access and malnutrition/inflammation resilience), and c) psychosocial (poor social support, poor self-efficacy, and mood disorders) risks. Barriers to improving hospitalization outcomes, especially as they relate to above risk categories, exist at multiple stakeholder levels and include: (1) dialysis facilities (strict shift changes, personnel challenges), (2) nephrologists (static dialysis prescriptions and protocols based on dialysis facility metrics), and (3) patients (lack of engagement and self-efficacy). System-level elements, such as payment models, help to propagate these barriers. In this article, we seek to shift the care paradigm discussion to patient trajectories and long-term outcomes, and to active engagement of patients as self-managers, through which we hope to impact on high inpatient resource utilization. We will also focus attention on the complex interplay of practices that have become acceptable care structures, but that may be deleterious to outcomes. Only after thorough consideration of these topics can we hope to impact on this important problem.

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Risk factors for increased variability in dialysis delivery in haemodialysis patients

Cited by 2 publications

References 11 publications

Estimating Dialysis Adequacy Using Ionic Dialysance

Estimating Dialysis Adequacy Using Ionic Dialysance

Decreasing hospitalizations in patients on hemodialysis: Time for a paradigm shift

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