Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment

Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research

Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.

Exploring the Potential of Chatbots in Critical Care Nephrology

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

Background and objectives

Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5.

Design

A retrospective cohort study.

Setting and participants

A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021.

Methods

This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM).

Main outcome measurement

Subsequent eGFR level after three consecutive laboratory data assessments.

Results

Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM.

Conclusion

The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.

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.

Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model

BACKGROUND

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of this study was to develop a deep learning-based artificial intelligence (AI) model to predict IDH using pre-dialysis features.

METHODS

Data from 2007 patients with 943 220 HD sessions at seven university hospitals were used. The performance of the deep learning model was compared with three machine learning models (logistic regression, random forest and XGBoost).

RESULTS

IDH occurred in 5.39% of all studied HD sessions. A lower pre-dialysis blood pressure (BP), and a higher ultrafiltration (UF) target rate and interdialytic weight gain in IDH sessions compared with non-IDH sessions, and the occurrence of IDH in previous sessions was more frequent among IDH sessions compared with non-IDH sessions. Matthews correlation coefficient and macro-averaged F1 score were used to evaluate both positive and negative prediction performances. Both values were similar in logistic regression, random forest, XGBoost and deep learning models, developed with data from a single session. When combining data from the previous three sessions, the prediction performance of the deep learning model improved and became superior to that of other models. The common top-ranked features for IDH prediction were mean systolic BP (SBP) during the previous session, UF target rate, pre-dialysis SBP, and IDH experience during the previous session.

CONCLUSIONS

Our AI model predicts IDH accurately, suggesting it as a reliable tool for HD treatment.

Development and validation of AI-based triage support algorithms for prevention of intradialytic hypotension

BACKGROUND

Intradialytic hypotension remains one of the most recurrent complications of dialysis sessions. Inadequate management can lead to adverse outcomes, highlighting the need to develop personalized approaches for the prevention of intradialytic hypotension. Here, we sought to develop and validate two AI-based risk models predicting the occurrence of symptomatic intradialytic hypotension at different time points.

METHODS

The models were built using the XGBoost algorithm and they predict the occurrence of intradialytic hypotension in the next dialysis session and in the next month. The initial dataset, obtained from routinely collected data in the EuCliD® Database, was split to perform model derivation, training and validation. Model performance was evaluated by concordance statistic and calibration charts; the importance of features was assessed with the Shapley Additive Explanation (SHAP) methodology.

RESULTS

The final dataset included 1,249,813 dialysis sessions, and the incidence rate of intradialytic hypotension was 10.07% (95% CI 10.02-10.13). Our models retained good discrimination (AUC around 0.8) and a suitable calibration yielding to the selection of three classification thresholds identifying four distinct risk groups. Variables providing the most significant impact on risk estimates were blood pressure dynamics and other metrics mirroring hemodynamic instability over time.

CONCLUSIONS

Recurrent symptomatic intradialytic hypotension could be reliably and accurately predicted using routinely collected data during dialysis treatment and standard clinical care. Clinical application of these prediction models would allow for personalized risk-based interventions for preventing and managing intradialytic hypotension.

Survey and Evaluation of Hypertension Machine Learning Research

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Improvement of Dialysis Dosing Using Big Data Analytics

OBJECTIVES

Large amounts of healthcare data are now generated via patient health records, records of diagnosis and treatment, smart devices, and wearables. Extracting insights from such data can transform healthcare from a traditional, symptom-driven practice into precisely personalized medicine. Dialysis treatments generate a vast amount of data, with more than 100 parameters that must be regulated for ideal treatment outcomes. When complications occur, understanding electrolyte parameters and predicting their outcomes to deliver the optimal dialysis dosing for each patient is a challenge. This study focused on refining dialysis dosing by utilizing emerging data from the growing number of dialysis patients to improve patients' quality of life and well-being.

METHODS

Exploratory data analysis and data prediction approaches were performed to gather insights from patients' vital electrolytes on how to improve the patients' dialysis dosing. Four predictive models were constructed to predict electrolyte levels through various dialysis parameters.

RESULTS

The decision tree model showed excellent performance and more accurate results than the support vector machine, linear regression, and neural network models.

CONCLUSIONS

The predictive models identified that pre-dialysis blood urea nitrogen, pre-weight, dry weight, anticoagulation, and sex had the most significant effects on electrolyte concentrations. Such models could fine-tune dialysis dosing levels for the growing number of dialysis patients to improve each patient's quality of life, life expectancy, and well-being, and to reduce costs, efforts, and time consumption for both patients and physicians. The study's results need to be validated on a larger scale.

Review of Artificial Intelligence-Based Signal Processing in Dialysis: Challenges for Machine-Embedded and Complementary Applications

Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and user experience during therapy. In dialysis, the application of artificial intelligence as a therapy-individualization tool is led more by start-ups than consolidated players, and innovation in dialysis seems comparably stagnant. Factors such as technical requirements or regulatory processes are important and necessary but can slow down the implementation of artificial intelligence due to missing data infrastructure and undefined approval processes. Current research focuses mainly on analyzing health records or wearable technology to add to existing health data. It barely uses signal data from treatment devices to apply artificial intelligence models. This article, therefore, discusses requirements for signal processing through artificial intelligence in health care and compares these with the status quo in dialysis therapy. It offers solutions for given barriers to speed up innovation with sensor data, opening access to existing and untapped sources, and shows the unique advantage of signal processing in dialysis compared to other health care domains. This research shows that even though the combination of different data is vital for improving patients' therapy, adding signal-based treatment data from dialysis devices to the picture can benefit the understanding of treatment dynamics, improving and individualizing therapy.

Artificial intelligence and digital health for volume maintenance in hemodialysis patients

Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.

Fluid and electrolyte management: increasing the knowledge of House Officers using an interactive eLearning tool

BACKGROUND

Interactive case-based tutorials represent a well-established method of improving House Officer learning. There has been little research on how tutorials of this kind can be improved, and whether their use changes practice.

AIM

Our study aims to assess whether our eLearning tutorial on IV fluid and electrolyte prescribing improves the underlying knowledge base and confidence of participating House Officers, with regards to fluid and electrolyte balance physiology and prescribing.

METHOD

An interactive eLearning module with core information on fluid and electrolyte prescribing and associated cases with questions and answers was created and distributed to participating House Officers in the 2019-2020 cohort nationwide. Participants were asked to complete pre-eLearning and post-eLearning questionnaires as well as a feedback survey to assess the efficacy of the module.

RESULTS

Forty-nine House Officers completed the eLearning module and associated questionnaires. A majority of participants (69.3%) reported their previous teaching on fluid and electrolyte management as "very poor", "poor" or "mediocre". The average score for the pre-eLearning knowledge test was 75%, compared to a score of 97% for the post-eLearning knowledge test, resulting in a 22% increase in correct answers (p < 0.001). We found an increase of 53% in feeling "confident" or "very confident" in assessing and managing fluid requirements, and an increase of 57.1% in feeling "confident" or "very confident" in managing electrolyte requirements after undertaking the eLearning module.

CONCLUSION

An interactive eLearning tutorial with real-world applications provides an effective, low-cost intervention that can improve confidence and skill in prescribing IV fluids.

Sodium First Approach, to Reset Our Mind for Improving Management of Sodium, Water, Volume and Pressure in Hemodialysis Patients, and to Reduce Cardiovascular Burden and Improve Outcomes

New physiologic findings related to sodium homeostasis and pathophysiologic associations require a new vision for sodium, fluid and blood pressure management in dialysis-dependent chronic kidney disease patients. The traditional dry weight probing approach that has prevailed for many years must be reviewed in light of these findings and enriched by availability of new tools for monitoring and handling sodium and water imbalances. A comprehensive and integrated approach is needed to improve further cardiac health in hemodialysis (HD) patients. Adequate management of sodium, water, volume and hemodynamic control of HD patients relies on a stepwise approach: the first entails assessment and monitoring of fluid status and relies on clinical judgement supported by specific tools that are online embedded in the HD machine or devices used offline; the second consists of acting on correcting fluid imbalance mainly through dialysis prescription (treatment time, active tools embedded on HD machine) but also on guidance related to diet and thirst management; the third consist of fine tuning treatment prescription to patient responses and tolerance with the support of innovative tools such as artificial intelligence and remote pervasive health trackers. It is time to come back to sodium and water imbalance as the root cause of the problem and not to act primarily on their consequences (fluid overload, hypertension) or organ damage (heart; atherosclerosis, brain). We know the problem and have the tools to assess and manage in a more precise way sodium and fluid in HD patients. We strongly call for a sodium first approach to reduce disease burden and improve cardiac health in dialysis-dependent chronic kidney disease patients.

Machine learning to predict end stage kidney disease in chronic kidney disease

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Artificial intelligence in peritoneal dialysis: general overview

OBJECTIVE

This article is a general overview about artificial intelligence/machine learning (AI/ML) algorithms in the domain of peritoneal dialysis (PD).

METHODS

We searched studies that used AI/ML in PD, which were classified according to the type of algorithm and PD issue.

RESULTS

Studies were divided into (a) predialytic stratification, (b) peritoneal technique issues, (c) infections, and (d) complications prediction. Most of the studies were observational and majority of them were reported after 2010.

CONCLUSIONS

There is a number of studies proved that AI/ML algorithms can predict better than conventional statistical method and even nephrologists. However, the soundness of AI/ML algorithms in PD still requires large databases and interpretation by clinical experts. In the future, we hope that AI will facilitate the management of PD patients, thus increasing the quality of life and survival.

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach

Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems.

Hidden risks associated with conventional short intermittent hemodialysis: A call for action to mitigate cardiovascular risk and morbidity

The development of maintenance hemodialysis (HD) for end stage kidney disease patients is a success story that continues to save many lives. Nevertheless, intermittent renal replacement therapy is also a source of recurrent stress for patients. Conventional thrice weekly short HD is an imperfect treatment that only partially corrects uremic abnormalities, increases cardiovascular risk, and exacerbates disease burden. Altering cycles of fluid loading associated with cardiac stretching (interdialytic phase) and then fluid unloading (intradialytic phase) likely contribute to cardiac and vascular damage. This unphysiologic treatment profile combined with cyclic disturbances including osmotic and electrolytic shifts may contribute to morbidity in dialysis patients and augment the health burden of treatment. As such, HD patients are exposed to multiple stressors including cardiocirculatory, inflammatory, biologic, hypoxemic, and nutritional. This cascade of events can be termed the dialysis stress storm and sickness syndrome. Mitigating cardiovascular risk and morbidity associated with conventional intermittent HD appears to be a priority for improving patient experience and reducing disease burden. In this in-depth review, we summarize the hidden effects of intermittent HD therapy, and call for action to improve delivered HD and develop treatment schedules that are better tolerated and associated with fewer adverse effects.

Choices in hemodialysis therapies: variants, personalized therapy and application of evidence-based medicine

The extent of removal of the uremic toxins in hemodialysis (HD) therapies depends primarily on the dialysis membrane characteristics and the solute transport mechanisms involved. While designation of ‘flux’ of membranes as well toxicity of compounds that need to be targeted for removal remain unresolved issues, the relative role, efficiency and utilization of solute removal principles to optimize HD treatment are better delineated. Through the combination and intensity of diffusive and convective removal forces, levels of concentrations of a broad spectrum of uremic toxins can be lowered significantly and successfully. Extended clinical experience as well as data from several clinical trials attest to the benefits of convection-based HD treatment modalities. However, the mode of delivery of HD can further enhance the effectiveness of therapies. Other than treatment time, frequency and location that offer clinical benefits and increase patient well-being, treatment- and patient-specific criteria may be tailored for the therapy delivered: electrolytic composition, dialysate buffer and concentration and choice of anticoagulating agent are crucial for dialysis tolerance and efficacy. Evidence-based medicine (EBM) relies on three tenets, i.e. clinical expertise (i.e. doctor), patient-centered values (i.e. patient) and relevant scientific evidence (i.e. science), that have deviated from their initial aim and summarized to scientific evidence, leading to tyranny of randomized controlled trials. One must recognize that practice patterns as shown by Dialysis Outcomes and Practice Patterns Study and personalization of HD care are the main driving force for improving outcomes. Based on a combination of the three pillars of EBM, and particularly on bedside patient–clinician interaction, we summarize what we have learned over the last 6 decades in terms of best practices to improve outcomes in HD patients. Management of initiation of dialysis, vascular access, preservation of kidney function, selection of biocompatible dialysers and use of dialysis fluids of high microbiological purity to restrict inflammation are just some of the approaches where clinical experience is vital in the absence of definitive scientific evidence. Further, HD adequacy needs to be considered as a broad and multitarget approach covering not just the dose of dialysis provided, but meeting individual patient needs (e.g. fluid volume, acid–base, blood pressure, bone disease metabolism control) through regular assessment—and adjustment—of a series of indicators of treatment efficiency. Finally, in whichever way new technologies (i.e. artificial intelligence, connected health) are embraced in the future to improve the delivery of dialysis, the human dimension of the patient–doctor interaction is irreplaceable. Kidney medicine should remain ‘an art’ and will never be just ‘a science’.

Multitargeted interventions to reduce dialysis-induced systemic stress

Hemodialysis (HD) is a life-sustaining therapy as well as an intermittent and repetitive stress condition for the patient. In ridding the blood of unwanted substances and excess fluid from the blood, the extracorporeal procedure simultaneously induces persistent physiological changes that adversely affect several organs. Dialysis patients experience this systemic stress condition usually thrice weekly and sometimes more frequently depending on the treatment schedule. Dialysis-induced systemic stress results from multifactorial components that include treatment schedule (i.e. modality, treatment time), hemodynamic management (i.e. ultrafiltration, weight loss), intensity of solute fluxes, osmotic and electrolytic shifts and interaction of blood with components of the extracorporeal circuit. Intradialytic morbidity (i.e. hypovolemia, intradialytic hypotension, hypoxia) is the clinical expression of this systemic stress that may act as a disease modifier, resulting in multiorgan injury and long-term morbidity. Thus, while lifesaving, HD exposes the patient to several systemic stressors, both hemodynamic and non-hemodynamic in origin. In addition, a combination of cardiocirculatory stress, greatly conditioned by the switch from hypervolemia to hypovolemia, hypoxemia and electrolyte changes may create pro-arrhythmogenic conditions. Moreover, contact of blood with components of the extracorporeal circuit directly activate circulating cells (i.e. macrophages-monocytes or platelets) and protein systems (i.e. coagulation, complement, contact phase kallikrein-kinin system), leading to induction of pro-inflammatory cytokines and resulting in chronic low-grade inflammation, further contributing to poor outcomes. The multifactorial, repetitive HD-induced stress that globally reduces tissue perfusion and oxygenation could have deleterious long-term consequences on the functionality of vital organs such as heart, brain, liver and kidney. In this article, we summarize the multisystemic pathophysiological consequences of the main circulatory stress factors. Strategies to mitigate their effects to provide more cardioprotective and personalized dialytic therapies are proposed to reduce the systemic burden of HD.

The future of the artificial kidney

End-stage renal disease (ESRD) is increasing worldwide. In India, diabetes mellitus and hypertension are the leading causes of chronic kidney disease and ESRD. Hemodialysis is the most prevalent renal replacement therapy (RRT) in India. The ideal RRT must mimic the complex structure of the human kidney while maintaining the patient's quality of life. The quest for finding the ideal RRT, the “artificial kidney”– that can be replicated in the clinical setting and scaled-up across barriers– continues to this date. This review aims to outline the developments, the current status of the artificial kidney and explore its future potential.

Noninvasive continuous intradialytic blood pressure monitoring: the key to improving haemodynamic stability

PURPOSE OF REVIEW

Intradialytic hypotension (IDH) occurs in 20% of haemodialysis treatments, leading to end-organ ischaemia, increased morbidity and mortality; and contributing to poor quality of life for patients. Treatment of IDH is reactive since brachial blood pressure (BP) is recorded only intermittently during haemodialysis, making early detection and prediction of hypotension impossible. Noninvasive continuous BP monitoring would allow earlier detection of IDH and thus support the development of methods for its prediction and consequently prevention.

RECENT FINDINGS

Noninvasive continuous BP monitoring is not yet part of routine practice in renal dialysis units, with a small number of devices (e.g. finger cuffs) having occasionally been used in research settings. In use, patients frequently report pain or discomfort at measurement sites. Additionally, these devices can be unreliable in patients with reduced blood flow to the digits, often manifest in dialysis patients. All existing methods are sensitive to patient movement.A new method for continuously estimating BP has been developed by monitoring arterial pressure near the arteriovenous fistula which can be achieved without any extraneous monitoring equipment attached to the patient. Additionally, artificial intelligence-based methods for real-time prediction of IDH are currently emerging.

SUMMARY

Key monitoring technologies and computational methods are emerging to support the development of real-time IDH prediction.

Effects of Natural Convalescent Factors Combined with Motor Intelligence Management on Patients' Blood Pressure

To explore the application of natural convalescent factors combined with exercise intelligence management in blood pressure control of patients with hypertension, 102 patients with hypertension who were admitted from January 2017 to August 2019 were selected as the research subjects. According to the odd-even number method, they were divided into two groups with 51 cases in each group. The control group was treated with natural convalescent factor therapy alone, and the observation group was treated with natural convalescent factor combined with motor intelligence management. The application effects of the two groups were compared. Before sports intelligence management, the levels of systolic blood pressure (SBP) in control group and observation group were (145.45 ± 8.44) mmHg (1 mmHg = 0.133 kPa) and (146.55 ± 8.37) mmHg, respectively; the diastolic blood pressure (DBP) levels of the control group and the observation group were (98.47 ± 3.48) mmHg and (98.94 ± 3.48) mmHg, respectively, with no statistical significance (P > 0.05). After the exercise intelligence management, the SBP levels of the control group and the observation group were (132.76 ± 4.48) mmHg and (130.06 ± 2.48) mmHg, respectively. The DBP levels of the control group and the observation group were (85.48 ± 5.38) mmHg and (83.47 ± 3.35) mmHg, respectively. The difference was statistically significant (P < 0.05). The scores of each index of quality of life in the observation group were higher than those in the control group, and the differences of physical function and psychological/mental scores were significant. The scores of physical function in the two groups before administration were (48.36 ± 1.69) and (48.74 ± 1.62), and the differences were not statistically significant (P > 0.05). After management, the physiological function scores of the two groups were (40.32 ± 1.33) and (32.15 ± 1.54) and the difference was statistically significant (P < 0.05). There were no significant differences in the psychological (30.75 ± 1.26)/mental scores (30.26 ± 1.48) between the two groups before management (P > 0.05), but there were significant differences in the psychological (25.30 ± 1.02)/mental scores (18.76 ± 1.36) between the two groups after management (P < 0.05). The combination of natural convalescent factors and intelligent exercise management can effectively control the blood pressure level and improve the quality of life of patients with hypertension, and the clinical application effect is good.

Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy

In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 μg (ESAs dose), respectively. Compared to the results in the real-world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 μg/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure

BACKGROUND

Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals.

METHOD

We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records.

RESULTS

Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data.

CONCLUSIONS

The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

Dialysis adequacy predictions using a machine learning method

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman's rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

BACKGROUND AND OBJECTIVES

Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.

RESULTS

The recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87-0.87) and 0.79 (interquartile range, 0.79-0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.

CONCLUSIONS

Our deep learning model can be used to predict the real-time risk of intradialytic hypotension.

Opportunities in the cloud or pie in the sky? Current status and future perspectives of telemedicine in nephrology

The use of telehealth to support, enhance or substitute traditional methods of delivering healthcare is becoming increasingly common in many specialties, such as stroke care, radiology and oncology. There is reason to believe that this approach remains underutilized within nephrology, which is somewhat surprising given the fact that nephrologists have always driven technological change in developing dialysis technology. Despite the obvious benefits that telehealth may provide, robust evidence remains lacking and many of the studies are anecdotal, limited to small numbers or without conclusive proof of benefit. More worryingly, quite a few studies report unexpected obstacles, pitfalls or patient dissatisfaction. However, with increasing global threats such as climate change and infectious disease, a change in approach to delivery of healthcare is needed. The current pandemic with coronavirus disease 2019 (COVID-19) has prompted the renal community to embrace telehealth to an unprecedented extent and at speed. In that sense the pandemic has already served as a disruptor, changed clinical practice and shown immense transformative potential. Here, we provide an update on current evidence and use of telehealth within various areas of nephrology globally, including the fields of dialysis, inpatient care, virtual consultation and patient empowerment. We also provide a brief primer on the use of artificial intelligence in this context and speculate about future implications. We also highlight legal aspects and pitfalls and discuss the 'digital divide' as a key concept that healthcare providers need to be mindful of when providing telemedicine-based approaches. Finally, we briefly discuss the immediate use of telenephrology at the onset of the COVID-19 pandemic. We hope to provide clinical nephrologists with an overview of what is currently available, as well as a glimpse into what may be expected in the future.

Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic

Background and aims

Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.

Methods

We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.

Results

AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.

Conclusions

Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.

Volume Balance in Chronic Kidney Disease: Evaluation Methodologies and Innovation Opportunities

BACKGROUND

Patients affected by chronic kidney disease are at a risk of cardiovascular morbidity and mortality. Body fluids unbalance is one of the main characteristics of this condition, as fluid overload is highly prevalent in patients affected by the cardiorenal syndrome.

SUMMARY

We describe the state of the art and new insights into body volume evaluation. The mechanisms behind fluid balance are often complex, mainly because of the interplay of multiple regulatory systems. Consequently, its management may be challenging in clinical practice and even more so out-of-hospital. Availability of novel technologies offer new opportunities to improve the quality of care and patients' outcome. Development and validation of new technologies could provide new tools to reduce costs for the healthcare system, promote personalized medicine, and boost home care. Due to the current COVID-19 pandemic, a proper monitoring of chronic patients suffering from fluid unbalances is extremely relevant. Key Message: We discuss the main mechanisms responsible for fluid overload in different clinical contexts, including hemodialysis, peritoneal dialysis, and heart failure, emphasizing the potential impact provided by the implementation of the new technologies.

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

Hemodialysis has saved many lives, albeit with significant residual mortality. Although poor outcomes may reflect advanced age and comorbid conditions, hemodialysis per se may harm patients, contributing to morbidity and perhaps mortality. Systemic circulatory "stress" resulting from hemodialysis treatment schedule may act as a disease modifier, resulting in a multiorgan injury superimposed on preexistent comorbidities. New functional intradialytic imaging (i.e., echocardiography, cardiac magnetic resonance imaging [MRI]) and kinetic of specific cardiac biomarkers (i.e., Troponin I) have clearly documented this additional source of end-organ damage. In this context, several factors resulting from patient-hemodialysis interaction and/or patient management have been identified. Intradialytic hypovolemia, hypotensive episodes, hypoxemia, solutes, and electrolyte fluxes as well as cardiac arrhythmias are among the contributing factors to systemic circulatory stress that are induced by hemodialysis. Additionally, these factors contribute to patients' symptom burden, impair cognitive function, and finally have a negative impact on patients' perception and quality of life. In this review, we summarize the adverse systemic effects of current intermittent hemodialysis therapy, their pathophysiologic consequences, review the evidence for interventions that are cardioprotective, and explore new approaches that may further reduce the systemic burden of hemodialysis. These include improved biocompatible materials, smart dialysis machines that automatically may control the fluxes of solutes and electrolytes, volume and hemodynamic control, health trackers, and potentially disruptive technologies facilitating a more personalized medicine approach.

Sodium, volume and pressure control in haemodialysis patients for improved cardiovascular outcomes

Chronic volume overload is pervasive in patients on chronic haemodialysis and substantially increases the risk of cardiovascular death. The rediscovery of the three-compartment model in sodium metabolism revolutionizes our understanding of sodium (patho-)physiology and is an effect modifier that still needs to be understood in the context of hypertension and end-stage kidney disease. Assessment of fluid overload in haemodialysis patients is central yet difficult to achieve, because traditional clinical signs of volume overload lack sensitivity and specificity. The highest all-cause mortality risk may be found in haemodialysis patients presenting with high fluid overload but low blood pressure before haemodialysis treatment. The second highest risk may be found in patients with both high blood pressure and fluid overload, while high blood pressure but normal fluid overload may only relate to moderate risk. Optimization of fluid overload in haemodialysis patients should be guided by combining the traditional clinical evaluation with objective measurements such as bioimpedance spectroscopy in assessing the risk of fluid overload. To overcome the tide of extracellular fluid, the concept of time-averaged fluid overload during the interdialytic period has been established and requires possible readjustment of a negative target post-dialysis weight. ²³Na-magnetic resonance imaging studies will help to quantitate sodium accumulation and keep prescribed haemodialytic sodium mass balance on the radar. Cluster-randomization trials (e.g. on sodium removal) are underway to improve our therapeutic approach to cardioprotective haemodialysis management.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

BACKGROUND

The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used?

METHODS

We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm.

RESULTS

AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues.

CONCLUSIONS

Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

Leveraging Data Science for a Personalized Haemodialysis

Background

The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed.

Summary

Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis.

Key messages

Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

Fluid and hemodynamic management in hemodialysis patients: challenges and opportunities

Fluid volume and hemodynamic management in hemodialysis patients is an essential component of dialysis adequacy. Restoring salt and water homeostasis in hemodialysis patients has been a permanent quest by nephrologists summarized by the ‘dry weight’ probing approach. Although this clinical approach has been associated with benefits on cardiovascular outcome, it is now challenged by recent studies showing that intensity or aggressiveness to remove fluid during intermittent dialysis is associated with cardiovascular stress and potential organ damage. A more precise approach is required to improve cardiovascular outcome in this high-risk population. Fluid status assessment and monitoring rely on four components: clinical assessment, non-invasive instrumental tools (e.g., US, bioimpedance, blood volume monitoring), cardiac biomarkers (e.g. natriuretic peptides), and algorithm and sodium modeling to estimate mass transfer. Optimal management of fluid and sodium imbalance in dialysis patients consist in adjusting salt and fluid removal by dialysis (ultrafiltration, dialysate sodium) and by restricting salt intake and fluid gain between dialysis sessions. Modern technology using biosensors and feedback control tools embarked on dialysis machine, with sophisticated analytics will provide direct handling of sodium and water in a more precise and personalized way. It is envisaged in the near future that these tools will support physician decision making with high potential of improving cardiovascular outcome.

Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea

Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications

This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.

The renal replacement therapy landscape in 2030: reducing the global cardiovascular burden in dialysis patients

Despite the significant progress made in understanding chronic kidney disease and uraemic pathophysiology, use of advanced technology and implementation of new strategies in renal replacement therapy, the clinical outcomes of chronic kidney disease 5 dialysis patients remain suboptimal. Considering residual suboptimal medical needs of short intermittent dialysis, it is our medical duty to revisit standards of dialysis practice and propose new therapeutic options for improving the overall effectiveness of dialysis sessions and reduce the burden of stress induced by the therapy. Several themes arise to address the modifiable components of the therapy that are aimed at mitigating some of the cardiovascular risks in patients with end-stage kidney disease. Among them, five are of utmost importance and include: (i) enhancement of treatment efficiency and continuous monitoring of dialysis performances; (ii) prevention of dialysis-induced stress; (iii) precise handling of sodium and fluid balance; (iv) moving towards heparin-free dialysis; and (v) customizing electrolyte prescriptions. In summary, haemodialysis treatment in 2030 will be substantially more personalized to the patient, with a clear focus on cardioprotection, volume management, arrhythmia surveillance, avoidance of anticoagulation and the development of more dynamic systems to align the fluid and electrolyte needs of the patient on the day of the treatment to their particular circumstances.

Role of Artificial Intelligence in Kidney Disease

Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

Intradialytic Hypotension: Mechanisms and Outcome

Intradialytic hypotension (IDH) occurs in approximately 10-12% of treatments. Whereas several definitions for IDH are available, a nadir systolic blood pressure carries the strongest relation with outcome. Whereas the relation between IDH may partly be based on patient characteristics, it is likely that also impaired organ perfusion leading to permanent damage, plays a role in this relationship. The pathogenesis of IDH is multifactorial and is based on a combination of a decline in blood volume (BV) and impaired vascular resistance at a background of a reduced cardiovascular reserve. Measurements of absolute BV based on an on-line dilution method appear more promising than relative BV measurements in the prediction of IDH. Also, feedback treatments in which ultrafiltration rate is automatically adjusted based on changes in relative BV have not yet resulted in improvement. Frequent assessment of dry weight, attempting to reduce interdialytic weight gain and prescribing more frequent or longer dialysis treatments may aid in preventing IDH. The impaired vascular response can be improved using isothermic or cool dialysis treatment which has also been associated with a reduction in end organ damage, although their effect on mortality has not yet been assessed. For the future, identification of vulnerable patients based on artificial intelligence and on-line assessment of markers of organ perfusion may aid in individualizing treatment prescription, which will always remain dependent on the clinical context of the patient.