Daniel Schneditz

Intradialytic hypotension (IDH) is a frequent complication of hemodialysis that may occur with symptoms and often requires interventions to restore hemodynamic stability and end-organ perfusion. In the absence of large trials with IDH as the primary endpoint, guidelines recommending specific management protocols are not available, but hemodynamic instability is a daily clinical reality, nevertheless. First, we suggest defining and monitoring IDH, by adopting a uniform IDH definition such as the Nadir90 criteria and document symptoms, and regularly measuring intradialytic as well as ambulatory blood pressure. Next, we give recommendations on how to treat IDH, by developing a standard operating procedure for acute events, and increasing dialysate sodium, and decreasing ultrafiltration rates and dialysate temperature. Finally, the majority of our tips are dedicated to preventing IDH, by regularly assessing fluid status, reviewing antihypertensive drugs, considering more frequent, longer or nocturnal dialysis, potentially considering hemodiafiltration, avoiding food and considering exercise, and trying automatic feedback algorithms. No single measure consistently prevents IDH, and each tip should be viewed as one lever among many. In practice, IDH is best managed by a layered, individualized approach that combines rigorous documentation, structured follow-up and a willingness to revisit the prescription each time hemodynamic instability recurs.

Background: Ambulatory blood pressure (BP) and pulse wave characteristics (PWCs) are stronger predictors of mortality than conventional pre-/post-dialytic BP in hemodialysis. While BP is recommended to be managed primarily by reducing fluid overload (FO), associations of FO with interdialytic BP/PWCs remain unknown. Methods: Bioimpedance-derived FO and interdialytic 44-h BP/PWCs were analyzed at baseline and follow-up of a quality improvement project involving targeted fluid management. Trended cosinor linear mixed-effects models assessed intra-patient associations of post-dialysis FO with interdialytic BP/PWCs. Results: We analyzed 120 visits (77/43 at baseline/follow-up) from 80 patients (70% male; mean age 68 ± 14 years; median dialysis vintage 38 [18–64] months). Post-dialysis FO decreased in baseline hypervolemic patients from 2.7 ± 1.3 L to 0.9 ± 1.7 L. Every 1 L increase in post-dialysis FO at the patient level was associated with increased peripheral BP (systolic: 4.54 mmHg, P < 0.001; diastolic: 2.55 mmHg, P < 0.001), peripheral resistance (0.03 s*mmHg/mL, P = 0.011), augmentation index (1.38%, P = 0.007), wave amplitudes (forward: 0.56 mmHg, P = 0.025; backward: 0.61 mmHg, P = 0.027), reservoir pressure peak (3.83 mmHg, P < 0.001), and pulse wave velocity (0.12 m/s, P = 0.007). Higher FO was associated with decreased interdialytic slopes of all BPs, peripheral resistance, backward wave amplitude, reservoir peak pressure, and pulse wave velocity. FO interacted with circadian rhythms of stroke volume, peripheral resistance, forward wave amplitude, and excess pressure peak. Conclusions: We showed that FO in hemodialysis is associated with increased interdialytic BP and PWCs on a patient level, and that FO blunts interdialytic BP/PWC trends. We further establish that physiological BP/PWC patterns may be associated with euvolemic fluid status, hence making the case for tailoring target weight using bioimpedance spectroscopy.

Introduction: Increased blood pressure variability (BPV) is independently associated with cardiovascular mortality in hemodialysis (HD). Fluid overload (FO) is a major modifiable driver of blood pressure (BP), yet the relationship between BPV and FO is unknown. We assessed whether FO correlates with BPV, whether bioimpedance spectroscopy (BIS)-guided fluid management alters BPV, and whether pulse wave characteristics (PWC) correlate with BPV within individual HD patients. Methods We analysed 76 HD patients in a quality improvement project which consisted of targeted fluid management. Short-term BPV, primarily average real variability (ARV), was calculated from 44-hour interdialytic ambulatory recordings. FO strata (hypovolemia: <-1.1 L; euvolemia: -1.1 to + 1.1 L; hypervolemia: >1.1 L) were defined by post-HD BIS. Within-patient repeated-measures correlations (rmcorr) assessed correlations of FO and PWC with BP ARV. Results Peripheral systolic ARV was 12.3 ± 3.2 mmHg overall (baseline 12.5 ± 3.1; follow-up 11.8 ± 3.3). Among 37 patients with paired data, FO fell significantly from baseline to follow-up (ΔFO -0.59 ± 1.72 L, p = 0.043). ARV did not change (ΔARV − 0.25 ± 2.59 mmHg, p = 0.56), even in the 14 hypervolemic patients with more pronounced decrease in FO (ΔFO -2.06 ± 1.1 L; ΔARV − 0.04 mmHg, 95% CI [-1.37 to + 1.30]). FO did not correlate with ARV within-patient (r=-0.078, p > 0.99). In contrast, PWCs correlated strongly with ARV on an intra-patient basis, especially pulse wave velocity (r = + 0.631, p < 0.001). Conclusions FO did not correlate with BPV, whereas PWCs, particularly pulse wave velocity, were the dominant within-individual correlates. In HD, short-term BPV appears dissociated from volume status. BIS-guided fluid management addresses mean BP but should not be expected to reduce short-term BPV.

Background/Objectives: The main cause of death in patients with chronic kidney disease (CKD) is of cardiovascular origin. Entropy-based analysis of physiological signals reflects system irregularity, complexity, and adaptive capacity. Amplitude-aware permutation entropy (AAPE) is a signal analysis method suitable for assessing complex cardiovascular dynamics, and growing evidence suggests that measures of physiological signal variability and complexity may have prognostic value. This study aimed to evaluate whether AAPE can predict mortality in CKD patients undergoing hemodialysis (HD), with and without diabetes. The aim of this study was to assess whether AAPE analysis of cardiovascular signals following the administration of a glucose bolus directly into the extracorporeal circuit during hemodialysis (HD)—a method originally used to treat intradialytic hypotension and to study the kinetics of glucose, insulin, and C-peptide in patients with and without type 2 diabetes—can predict mortality in patients with chronic kidney disease (CKD) undergoing hemodialysis (HD), both with and without diabetes. Methods: After seven years of follow-up, mortality outcomes were analyzed in relation to AAPE-derived parameters. Results: Higher mortality was associated with smaller differences in AAPE of mean arterial pressure (MAP) and diastolic arterial pressure (DIA) before and after intravenous glucose administration (p = 0.009 and p = 0.016, respectively). Higher tonicity was associated with higher survival (p = 0.01). Additionally, greater reductions in AAPE of systolic arterial pressure (SYS) and larger differences in AAPE of ejection time (EJT) and total peripheral resistance (TPR) were associated with increased mortality. Conclusions: These findings suggest that entropy analysis reflects cardiovascular adaptability and may serve as a prognostic biomarker in HD patients.

Background: Bioimpedance and absolute blood volume measurement are objective assessments of volume status. However, uncertainties remain in their application at extreme body habitus. We aimed to compare blood volume and bioimpedance-based volume status at extreme body habitus and explore normalisation of blood volume in this context to support incorporation of these tools into fluid management decisions. Methods: We defined two common extreme volume-status phenotypes in haemodialysis: obese and bioimpedance-dry (n = 29) and lean and bioimpedance-wet (n = 29). Control subjects were haemodialysis patients who were haemodynamically stable with normal volume status (n = 20). We compared post-dialysis blood volume at clinical “target weight”, measured by dialysate dilution, in the obese/dry and lean/wet groups with controls. We explored normalisation of blood volume (n = 98) by body mass, lean tissue mass and an anthropometric-based estimate of blood volume determined by Lemmens' formula. Results: Post-dialysis blood volume normalized for body mass (specific blood volume) was lower in the obese/dry (57.9 mL/kg; P = 0.02) and higher in the lean/wet group (84.5 mL/kg; P = 0.001) than in controls (68.0 mL/kg). These differences were not present for lean tissue mass-normalised or Lemmens' formula-normalised blood volume. Normalising blood volume to body mass results in differences by sex and body mass, whereas normalising to Lemmens blood volume, which accounts for fat mass variation, does not. Conclusions: Setting target weights to achieve fixed reference bioimpedance or blood volume values may not be appropriate at extreme body habitus, reinforcing the need for individualised approaches. Normalising blood volume to body mass may exacerbate established measurement bias observed with bioimpedance.

Background: Fluid monitoring is critical for patients on maintenance hemodialysis. Bioimpedance enables estimation of fluid volumes from measures of electrical tissue properties. However, empirical equations are needed to approximate key variables, especially in wrist-to-ankle bioimpedance measurements, introducing potential errors. Summary: Here, we provide a technical overview of electrical impedance, derivation of fluid volumes from different bioimpedance methods and electrode setups, as well as sources of error including the assumption of constant resistivity, constant body temperature, and vendor-specific equations to derive fluid overload. We summarize the validity of bioimpedance methods in hemodialysis and conclude that irrespective of error sources reported above, segmental bioimpedance, where limbs and the trunk are measured separately, may be more accurate compared to the convenient wrist-to-ankle measurement. We argue that insufficient correction for variable body shape in wrist-to-ankle measurements jeopardizes this methodology, reporting here our analyses by means of theory and data simulation, where we found that conventional wrist-to-ankle bioimpedance underestimated extracellular fluid volume with increasing body fat percentage. The error could be reduced by using subject specific body shape correction based on high-resolution 3D models. Finally, we attempt to provide guidance for identifying and mitigating common issues of wrist-to-ankle bioimpedance. Key Messages: While more convenient than segmental measurements, wrist-to-ankle bioimpedance may underestimate fluid volumes in obesity when body shape is not properly accounted for. Novel techniques, including smartphone-based 3D scans of the body, could potentially facilitate individualizing body shape correction to improve fluid volume estimates.

Introduction: Patients on maintenance hemodialysis accumulate excess fluid between treatments. Intradialytic removal of fluid via ultrafiltration is partly compensated by vascular refilling from the interstitial space. Associations between whole-body fluid status and blood volume were previously investigated on the population level. The aim of this observational cohort study was to assess longitudinal changes in fluid compartment volumes on an intra-patient level. Methodology: Pre-dialysis bioimpedance spectroscopy measurements and absolute blood volume estimations were conducted in maintenance hemodialysis patients during 14 consecutive dialysis treatments over 5 weeks. Blood volume was determined using the dialysate bolus method. Longitudinal changes were evaluated using linear mixed models. Correlations were analyzed with repeated measures correlation coefficients (${\rho _{rm}}$). Results: Twenty-five patients were included in the final analysis [88% male, median (quartile 1, quartile 3) age and dialysis vintage of 66.0 years (48.0, 74.0) and 23.5 months (13.5, 34.5), respectively]. Pre-dialysis fluid overload significantly decreased from the first to the third treatment within the week (β = −0.38, P < .01) with no significant within-week changes in euvolemic body mass (β= −0.04, P = .78) or absolute blood volume at treatment start (β = −0.06, P = .65). Fluid overload did not correlate with absolute (${\rho _{rm}}$ = 0.10, P = .65) or specific blood volume (${\rho _{rm}}$=0.06, P = .78) at treatment start on an intra-patient level, but correlated moderately with refilling volume (${\rho _{rm}}$ = 0.46, P < .01). Conclusions: The observed lack of intra-patient correlations between pre-dialysis fluid overload and blood volume suggests that excess fluid may not necessarily accumulate proportionally in the interstitial and intravascular space, thus challenging previous assumptions regarding within-week changes in fluid compartments.