Effect of ultrafiltration profiling on outcomes among maintenance hemodialysis patients: a pilot randomized crossover trial

The effect of ascending- descending ultrafiltration and sodium profiles on blood pressure in hemodialysis patients: a randomized cross-over study

BACKGROUND

Considering no previous research into the utilization of ascending/descending ultrafiltration and linear sodium profiles in improving blood pressure among hemodialysis patients, the present study aimed to explore the effect of the A/D-UF along with linear sodium profiles on HD patients with hypotension.

METHODS

Applying a crossover design, this clinical trial was fulfilled between December 2022 and June 2023 on 20 patients undergoing HD, randomized into two groups, each one receiving two intervention protocols, viz., (a) an intervention protocol in which the liquid sodium in the dialysis solution was linear and the UF profiling was A/D, and (b) a routine protocol or HD, wherein both liquid sodium and UF in the dialysis solution remained constant. The HD patients' BP was then checked and recorded at six intervals, namely, before HD, one, two, three, and four hours after it, and following its completion, within each session. The data were further statistically analyzed using the IBM SPSS Statistics 20 and the related tests.

RESULTS

In total, 20 patients, including 12 men (60%) and 8 women (40%), with the mean age of 58.00 ± 14.54 on HD for an average of 54 months, were recruited in this study. No statistically significant difference was observed in the mean systolic and diastolic BP levels in the group receiving the A/D-UF profile all through the desired hours (p > 0.05), indicating that the patients did not face many changes in these two numbers during HD. Our cross-over clinical trial demonstrated a statistically significant reduction in symptomatic IDH episodes from 55 to 15% with the application of the A/D-UF profile (p < 0.05).

CONCLUSION

The study demonstrated that the A/D-UF profile could contribute to the stability of blood pressure levels among HD patients, with no significant fluctuations observed during treatment sessions.

TRIAL REGISTRATION

This study was registered in the Iranian Registry of Clinical Trials (no. IRCT20180429039463N5) on 07/01/2023.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning

Introduction

Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH.

Materials and Methods

We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model.

Results

A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively.

Conclusion

Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.

Sodium and ultrafiltration profiling in hemodialysis: A long-forgotten issue revisited

Sodium and ultrafiltration profiling are method of dialysis in which dialysate sodium concentration and ultrafiltration rate are altered during the course of the dialysis session. Sodium and ultrafiltration profiling have been used, commonly simultaneously, to improve hemodynamic stability during hemodialysis. Sodium profiling is particularly effective in decreasing the incidence of intradialytic hypotension, while ultrafiltration profiling is suggested to decrease subclinical repeated end organ ischemia during dialysis. However, complications such as increased interdialytic weight gain and thirst due to sodium excess have prevented widespread use of sodium profiling. Evidence suggest that different sodium profiling techniques may lead to different clinical results, and preferring sodium balance neutral sodium profiling may mitigate adverse effects related to sodium overload. However, evidence is lacking on the long-term clinical outcomes of different sodium profiling methods. Optimal method of sodium profiling as well as the utility of sodium/ultrafiltration profiling in routine practice await further clinical investigation.