Narrative Review of Incremental Hemodialysis.

The prescription of hemodialysis (HD) in patients with incident end-stage kidney disease (ESKD) is fundamentally empirical. The abrupt transition from nondialysis chronic kidney disease (CKD) to thrice-weekly in-center HD of much the same dialysis intensity as in those with prevalent ESKD underappreciates the progressive nature of kidney disease whereby the decline in renal function has been gradual and ongoing-including at the time of HD initiation. Adjuvant pharmacologic treatment (i.e., diuretics, acid buffers, potassium binders), coupled with residual kidney function (RKF), can complement an initial HD regimen of lower intensity. Barriers to less intensive HD in incident ESKD include risk of inadequate clearance of uremic toxins due to variable and unexpected loss of RKF, lack of patient adherence to assessments of RKF or adjustment of HD intensity, increased burden for all stakeholders in the dialysis units, and negative financial repercussions. A stepped dialysis regimen with scheduled transition from time-delineated twice-weekly HD to thrice-weekly HD could represent an effective and safe strategy to standardize incremental HD in patients with CKD transitioning to early-stage ESKD. Patients' adherence and survival as well as other clinical outcomes should be rigorously evaluated in clinical trials before large-scale implementation of different incremental schedules of HD. This review discusses potential benefits of and barriers to alternative dialysis regimens in patients with incident ESKD, with emphasis on twice-weekly HD with pharmacologic therapy, and summarizes in-progress clinical trials of incremental HD schedules

The Effect of Diffusive and Convective Sodium Balance During Hemodialysis on Interdialytic Weight Gain

Patients with end stage renal disease (ESRD) often require hemodialysis treatments in which blood’s water and dissolved solutes undergo diffusion and convection by exposure to an extracorporeal membrane. The leading cause of death in this population is cardiovascular, and how hemodialysis is prescribed alters total sodium balance, a critical determinant of cardiovascular health. We performed retrospective and prospective analysis of data from patients in the Southwestern Ontario Regional Hemodialysis Program. An increased Dialysate sodium (Dial-Na+) to Pre-dialysis plasma sodium (Pre- Na+) concentration difference (DPNa+) leads to adverse clinical outcomes in hemodialysis patients. The post- to pre-dialysis plasma sodium difference (PPNa+) predicts clinical outcomes equally well as DPNa+ so long as Dial-Na+ is within 3 mmol/L of Pre-Na+. Calculation of DPNa+ requires determination of the Pre-Na+, historically thought to be stable in hemodialysis patients and thus termed “setpoint” (SP). However, we determined that SP is modifiable by hemodialysis prescription. Finally, an equation to predict interdialytic weight gain was created, confirming Dial-Na+, dialysis frequency and duration to be modifiable factors affecting IDWG. Further research is required to validate this equation prospectively, and to determine the impact of changes of SP on cardiovascular morbidity and mortality

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Prognostic and pathophysiological features of uraemic cardiomyopathy using cardiovascular magnetic resonance imaging

Premature cardiovascular (CV) death is the commonest cause of death in patients with end stage renal disease (ESRD), which includes those receiving or close to requiring renal replacement therapy. In ESRD patients, CV deaths are most commonly caused by cardiac arrhythmia and sudden cardiac death compared to the general population where myocardial ischaemia and infarction predominate. Higher CV disease burden is due to accumulation of “conventional” risk factors (e.g. hypertension, diabetes mellitus, smoking) and “novel” risk factors (e.g. oxidative stress, proteinuria, anaemia, inflammation) in ESRD patients. In addition, risk factors specific to patients with renal disease have been identified including alteration in left ventricular (LV) structure, called uraemic cardiomyopathy. These structural abnormalities are common in patients with ESRD (between 60-80% of subjects upon initiation of dialysis) and include left ventricular hypertrophy (LVH), systolic dysfunction (LVSD) and dilatation. These changes in LV structure confer adverse CV outcome in ESRD patients and have proven difficult to reverse. Detection of these abnormalities is usually performed using echocardiography, however this technique is inaccurate in ESRD patients due to significant alterations in LV shape and geometric assumptions made during calculation of myocardial mass. Cardiovascular MRI (CMR) negates these assumptions and is the most accurate, reproducible and reliable method of assessing LV dimensions independent of intravascular volume, particularly in patients with altered myocardial architecture. Furthermore, maximal left atrial volume can be measured using CMR. The principle aims of the studies presented in this thesis were to elucidate prognostic and pathophysiological features of uraemic cardiomyopathy using CMR. In a large study (n=246) of haemodialysis patients, the determinants of each LV abnormality of uraemic cardiomyopathy were identified from past clinical history, haemodialysis and blood parameters and other LV measurements. For LV changes, major determinants were clinical features associated with advanced renal disease, namely expansion of intravascular/ extracellular fluid compartment, abnormal bone mineral biochemistry and hypertension. Furthermore, presence of one LV abnormality was one of the strongest predictors of presence of another, perhaps indicating differing stages of uraemic cardiomyopathy development. In a subsequent prognostic study including these patients (n=446), presence of LVSD and LV dilatation on CMR were significantly associated with poorer all cause and CV mortality. Presence of LVH, which is by far the most common structural change, was associated with poorer cardiovascular survival only. In addition, presence of two or three abnormalities (commonly LVH with another abnormality) had a significantly poorer prognosis and independently predicted CV and all cause mortality. This has implications for therapeutic strategies which should aim to slow or reverse cardiac changes of ESRD and prevent progression from one cardiac abnormality to 2 or more. In a further study (n=201) investigating additional prognostic features of ESRD patients with LVH, maximal left atrial volume (LAV) was measured using the bi-plane area length method at end LV systole. Elevated LAV and presence of LVSD were significantly associated with poorer all cause survival and were independent predictors of death. The most likely causes of elevated LAV in ESRD patients are LV diastolic dysfunction and expanded extracellular compartment and may provide a target for therapeutic intervention. The electrophysiological features of uraemic cardiomyopathy were assessed using microvolt T wave alternans (MTWA) which is a novel, non-invasive method of measuring small variations in surface electrocardiogram (ECG) T wave morphology and thus ventricular repolarisation. This technique has been used to stratify other cohorts at elevated risk of sudden cardiac death (such as ischaemic and non ischaemic cardiomyopathy, hypertensive LVH). A study presented in this thesis, compared MTWA results between ESRD (n=200) and hypertensive patients with LVH on echocardiography (n=30). Abnormal MTWA result was significantly more common in ESRD patients compared to hypertensive patients with LVH. Furthermore, abnormal MTWA result was significantly associated with myocardial abnormalities of uraemic cardiomyopathy and a history of macrovascular atheromatous disease in ESRD patients. Despite preservation of LV function on CMR, the frequency of abnormal MTWA result in ESRD patients was similar to previous studies in subjects with heart failure. 31Phosphorus magnetic resonance spectroscopy is a novel, non-invasive technique of estimating cardiac energetic status and high energy phosphate (HEP) metabolism in a myocardial area of interest and has previously been used to assess patients with global myocardial disease (dilated cardiomyopathy, hypertensive LVH). High energy phosphate metabolism was compared between patients with ESRD (n=53) and hypertensive LVH (n=30) and despite similar LV mass between both groups, PCr: ATP (an indicator of HEP metabolism) was significantly reduced in ESRD patients. These findings are most likely due to cardiac interstitial fibrosis and the alteration of tissue composition within the area of interest, and changes in metabolic function within cardiomyocytes of uraemic hearts. Finally, a small study (n=50) investigated the effect of successful renal transplantation on LV mass measured by CMR. On comparison of patients who remained on the renal transplant waiting list, there was no significant difference in LV mass in patients who received a renal transplant. It is likely that previous echocardiography studies that demonstrated significant regression of LVH, measured improvement in fluid control rather that actual reduction in myocardial mass. Future studies investigating benefit of therapeutic intervention may require identification of individuals at higher CV risk and the results of studies presented in this thesis aim to provide information for selecting such ESRD patients. With these results in mind, further prospective studies will be able to carefully select groups of ESRD patients with differing left ventricular, left atrial, electrophysiological and biochemical properties to demonstrate survival benefit with interventional agents. In this way, future therapies for ESRD patients can be tailored to improve cardiovascular survival

Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft