Digital Health Support: Current Status and Future Development for Enhancing Dialysis Patient Care and Empowering Patients

Chronic kidney disease poses a growing global health concern, as an increasing number of patients progress to end-stage kidney disease requiring kidney replacement therapy, presenting various challenges including shortage of care givers and cost-related issues. In this narrative essay, we explore innovative strategies based on in-depth literature analysis that may help healthcare systems face these challenges, with a focus on digital health technologies (DHTs), to enhance removal and ensure better control of broader spectrum of uremic toxins, to optimize resources, improve care and outcomes, and empower patients. Therefore, alternative strategies, such as self-care dialysis, home-based dialysis with the support of teledialysis, need to be developed. Managing ESKD requires an improvement in patient management, emphasizing patient education, caregiver knowledge, and robust digital support systems. The solution involves leveraging DHTs to automate HD, implement automated algorithm-driven controlled HD, remotely monitor patients, provide health education, and enable caregivers with data-driven decision-making. These technologies, including artificial intelligence, aim to enhance care quality, reduce practice variations, and improve treatment outcomes whilst supporting personalized kidney replacement therapy. This narrative essay offers an update on currently available digital health technologies used in the management of HD patients and envisions future technologies that, through digital solutions, potentially empower patients and will more effectively support their HD treatments.

Modifiable factors associated with achievement of high-volume post-dilution hemodiafiltration: results from an international study

BACKGROUND

The aim was to investigate factors associated with the successful achievement of ≥21 l/session of substitution fluid volume in patients on post-dilution hemodiafiltration.

METHODS

3315 patients treated in 6 European countries with the Fresenius 5008 CorDiax machine including the AutoSub Plus feature were considered. Variables that showed a relationship with convection volume were entered in a multivariable logistic regression model.

RESULTS

Mean blood flow was 379 ± 68 ml/min. Median substitution volume was 24.7 L (IQR 22.0-27.4 L). Mean filtration fraction was 28.3 ± 4.1%. 81.5% of sessions qualified as high-volume HDF (substitution volumes ≥21 L). Higher age, dialyzer surface area, blood flow and treatment time were positively associated with the achievement of ≥21 L substitution volume; higher body mass index, male gender, higher hematocrit, graft or catheter vs. fistula, and start of week vs. mid-week were negatively associated.

CONCLUSIONS

Dialysis center policy in terms of blood flow, treatment time, filter size, and perhaps even hemoglobin targets plays a key role in achieving high-volume HDF. All of these are modifiable factors that can help in prescribing an optimal combination of dialyzer size, achievable blood flows, and treatment times.

Noninvasive techniques for prevention of intradialytic hypotension

Episodes of hypotension during hemodialysis treatment constitutes an important clinical problem which has received considerable attention in recent years. Despite the fact that numerous approaches to reducing the frequency of intradialytic hypotension (IDH) have been proposed and evaluated, the problem has not yet found a definitive solution--an observation which, in particular, applies to episodes of acute, symptomatic hypotension. This overview covers recent advances in methodology for predicting and preventing IDH. Following a brief overview of well-established hypotension-related variables, including blood pressure, blood temperature, relative blood volume, and bioimpedance, special attention is given to electrocardiographic and photoplethysmographic (PPG) variables and their significance for IDH prediction. It is concluded that cardiovascular variables which reflect heart rate variability, heart rate turbulence, and baroreflex sensitivity are important to explore in feedback control hemodialysis systems so as to improve their performance. The analysis of hemodialysis-related changes in PPG pulse wave properties hold considerable promise for improving prediction.

Intra-dialytic hypotension and blood volume and blood temperature monitoring

Intra-dialytic hypotension (IDH) is a common problem affecting haemodialysis patients. Its aetiology is complex and influenced by multiple patient and dialysis factors. IDH occurs when the normal cardiovascular response cannot compensate for volume loss associated with ultrafiltration, and is exacerbated by a myriad of factors including intra-dialytic fluid gains, cardiovascular disease, antihypertensive medications and the physiological demands placed on patients by conventional haemodialysis. The use of blood volume monitoring and blood temperature monitoring technologies is advocated as a tool to predict and therefore prevent episodes of IDH. We review the clinical utility of these technologies and summarize the current evidence of their effect on reducing the incidence of IDH in haemodialysis population.

Artificial intelligence applications in the intensive care unit

OBJECTIVE

To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES

The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION

The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION

All literature relevant to the topic was reviewed.

DATA SYNTHESIS

Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS

The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.