Despite the high costs of treatment of people with kidney disease and associated comorbid conditions, the amount of reliable information available to guide the care of such patients is very limited. Some treatments have been assessed in randomized trials, but most such trials have been too small to detect treatment effects of a magnitude that would be realistic to achieve with a single intervention. Therefore, KDIGO convened an international, multidisciplinary controversies conference titled "Challenges in the Conduct of Clinical Trials in Nephrology" to identify the key barriers to conducting trials in patients with kidney disease. The conference began with plenary talks focusing on the key areas of discussion that included appropriate trial design (covering identification and evaluation of kidney and nonkidney disease outcomes) and sensible trial execution (with particular emphasis on streamlining both design and conduct). Break out group discussions followed in which the key areas of agreement and remaining controversy were identified. Here we summarize the main findings from the conference and set out a range of potential solutions. If followed, these solutions could ensure future trials among people with kidney disease are sufficiently robust to provide reliable answers and are not constrained by inappropriate complexities in design or conduct.

Background An integrated kidney disease healthcare company implemented a peritoneal dialysis (PD) remote treatment monitoring (RTM) application in 2016. We assessed if RTM utilization associates with hospitalization and technique failure rates.Methods We used data from adult (age $18 years) patients on PD treated from October 2016 through May 2019 who registered online for the RTM. Patients were classified by RTM use during a 30-day baseline after registration. Groups were: nonusers (never entered data), moderate users (entered one to 15 treatments), and frequent users (entered .15 treatments). We compared hospital admission/day and sustained technique failure (required .6 consecutive weeks of hemodialysis) rates over 3, 6, 9, and 12 months of follow-up using Poisson and Cox models adjusted for patient/clinical characteristics.Results Among 6343 patients, 65% were nonusers, 11% were moderate users, and 25% were frequent users. Incidence rate of hospital admission was 22% (incidence rate ratio [IRR]50.78; P50.002), 24% (IRR50.76; P,0.001), 23% (IRR50.77; P#0.001), and 26% (IRR50.74; P#0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Incidence rate of hospital days was 38% (IRR50.62; P50.013), 35% (IRR50.65; P50.001), 34% (IRR50.66; P#0.001), and 32% (IRR50.68; P,0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Sustained technique failure risk at 3, 6, 9, and 12 months was 33% (hazard ratio [HR]50.67; P50.020), 31% (HR50.69; P50.003), 31% (HR50.69; P50.001), and 27% (HR50.73; P50.001) lower, respectively, in frequent users versus nonusers. Among a subgroup of survivors of the 12-month follow-up, sustained technique failure risk was 26% (HR50.74; P50.023) and 21% (HR50.79; P50.054) lower after 9 and 12 months, respectively, in frequent users versus nonusers.Conclusions Our findings suggest frequent use of an RTM application associates with less hospital admissions, shorter hospital length of stay, and lower technique failure rates. Adoption of RTM applications may have the potential to improve timely identification/intervention of complications.

BackgroundHealth-related quality of life (HrQoL) varies among dialysis patients. However, little is known about the association of dialysis modality with HrQoL over time. We describe longitudinal patterns of HrQoL among chronic dialysis patients by treatment modality.MethodsNational retrospective cohort study of adult patients who initiated in-center dialysis or a home modality (peritoneal or home hemodialysis) between 1/2013 and 6/2015. Patients remained on the same modality for the first 120 days of the first two years. HrQoL was assessed by the Kidney Disease and Quality of Life-36 (KDQOL) survey in the first 120 days of the first two years after dialysis initiation. Home modality patients were matched to in-center patients in a 1:5 fashion.ResultsIn-center (n=4234) and home modality (n=880) patients had similar demographic and clinical characteristics. In-center dialysis patients had lower mean KDQOL scores across several domains compared to home modality patients. For patients who remained on the same modality, there was no change in HrQoL. However, there were trends towards clinically meaningful changes in several aspects of HrQoL for patients who switched modalities. Specifically, physical functioning decreased for patients who switched from home to in-center dialysis (p< 0.05).ConclusionsAmong a national cohort of chronic dialysis patients, there was a trend towards different patterns of HrQoL life that were only observed among patients who changed modality. Patients who switched from home to in-center modalities had significant lower physical functioning over time. Providers and patients should be mindful of HrQoL changes that may occur with dialysis modality change.Electronic supplementary materialThe online version of this article (10.1186/s12882-018-1198-5) contains supplementary material, which is available to authorized users.

Patients with end-stage renal disease (ESRD) experience unique patterns in their lifetime, such as the start of dialysis and renal transplantation. In addition, there is also an intricate link between ESRD and biological time patterns. In terms of cyclic patterns, the circadian blood pressure (BP) rhythm can be flattened, contributing to allostatic load, whereas the circadian temperature rhythm is related to the decline in BP during hemodialysis (HD). Seasonal variations in BP and interdialytic-weight gain have been observed in ESRD patients in addition to a profound relative increase in mortality during the winter period. Moreover, nonphysiological treatment patters are imposed in HD patients, leading to an excess mortality at the end of the long interdialytic interval. Recently, new evidence has emerged on the prognostic impact of trajectories of common clinical and laboratory parameters such as BP, body temperature, and serum albumin, in addition to single point in time measurements. Backward analysis of changes in cardiovascular, nutritional, and inflammatory parameters before the occurrence as hospitalization or death has shown that changes may already occur within months to even 1–2 years before the event, possibly providing a window of opportunity for earlier interventions. Disturbances in physiological variability, such as in heart rate, characterized by a loss of fractal patterns, are associated with increased mortality. In addition, an increase in random variability in different parameters such as BP and sodium is also associated with adverse outcomes. Novel techniques, based on time-dependent analysis of variability and trends and interactions of multiple physiological and laboratory parameters, for which machine-learning ­approaches may be necessary, are likely of help to the clinician in the future. However, upcoming research should also evaluate whether dynamic patterns observed in large epidemiological studies have relevance for the individual risk profile of the patient.

This study uniquely demonstrates the trajectories of key parameters though the transition from pre-dialysis to post-dialysis start. Significant differences are noted in the pre-dialysis period for patients who survive vs. those who do not survive the first year of dialysis. Early recognition of adverse trends in the pre-dialysis period may create opportunity to intervene to improve early dialysis outcomes.