Faculty Bibliography

BACKGROUND:Cardiovascular disease is the most common cause of morbidity and mortality in kidney transplant recipients. Screening for coronary disease is frequently required prior to kidney transplantation, but coronary intervention has not been shown to be beneficial except in complex coronary artery disease. The likelihood of finding significant coronary artery disease and the benefits of routine pre-transplant screening are uncertain. METHODS:We performed a systematic review and meta-analysis. Medline & Embase were searched to identify manuscripts published between 1998 and 2024 reporting the results of pre-transplant screening. The primary endpoints were the frequency of detecting significant coronary lesions for which there are AHA class 1 indications for revascularization: a) >50% left main stenosis; or b) multi-vessel disease with ejection fraction < 35% during pre-kidney transplant screening. Secondary endpoints included frequency of detecting multivessel disease, proximal left anterior descending artery (LAD) disease, and number of patients who underwent invasive coronary angiography. Meta-regression was used to explore outcome heterogeneity according to the presence of hypertension, diabetes, and age. RESULTS:We identified 1273 studies out of which 44 met eligibility criteria. The mean prevalence of class 1 indications was 2%, although the heterogeneity was high with estimates ranging from 0% to 17%. Estimated prevalence of proximal LAD disease was 2% and left main stenosis was 1%, whereas 10% of patients had multi-vessel coronary artery disease, and 35% were referred for invasive angiography. There was no evidence of significant heterogeneity according to sex of the population or prevalence of diabetes or hypertension. CONCLUSIONS:Identification of class I indications for revascularization during pre-transplant coronary screening was rare.

The risk of arrhythmia and its management become increasingly complex as kidney disease progresses. This presents a multifaceted clinical challenge. Our discussion addresses these specific challenges relevant to patients as their kidney disease advances. We highlight numerous opportunities for enhancing the current standard of care within this realm. Additionally, this review delves into research concerning early detection, prevention, diagnosis, and treatment of various arrhythmias spanning the spectrum of kidney disease.

RATIONALE & OBJECTIVE/UNASSIGNED:The incidence of arrhythmia varies by time of day. How this affects individuals on maintenance dialysis is uncertain. Our objective was to quantify the relationship of arrhythmia with the time of day and timing of dialysis. STUDY DESIGN/UNASSIGNED:Secondary analysis of the Monitoring in Dialysis study, a multicenter prospective cohort study. SETTINGS & PARTICIPANTS/UNASSIGNED:Loop recorders were implanted for continuous cardiac monitoring in 66 participants on maintenance dialysis with a follow up of 6 months. EXPOSURE/UNASSIGNED:Time of day based on 6-hour intervals. OUTCOMES/UNASSIGNED:Event rates of clinically significant arrhythmia. ANALYTICAL APPROACH/UNASSIGNED:Negative binomial mixed effects regression models for repeated measures were used to evaluate data from the Monitoring in Dialysis study for differences in diurnal patterns of clinically significant arrhythmia among those with end-stage kidney disease with heart failure and end-stage kidney disease alone. We additionally analyzed rates according to presence of heart failure, time of dialysis shift, and dialysis versus nondialysis day. RESULTS/UNASSIGNED: = 0.43), their periodicity differed with a peak between 12:00 AM and 5:59 AM in those with AM dialysis and a later peak between 6:00 AM and 11:59 AM in those with PM shifts. LIMITATIONS/UNASSIGNED:Post hoc analysis, unable to account for unmeasured confounders. CONCLUSION/UNASSIGNED:Clinically significant arrhythmias showed strong diurnal patterns with a maximal peak between 12:00 AM and 5:59 AM and noon. Although overall arrhythmia rates were similar, the peak rate occurred overnight in individuals without heart failure and during the morning in individuals with heart failure. Further exploration of the influence of circadian rhythm on arrhythmia in the setting of hemodialysis is needed.

KEY POINTS:Women are under-represented in high-impact nephrology trials. Trends remain consistent over the past 20 years and on the basis of target condition. Addressing the imbalanced enrollment of women in trials could improve disparities in care and outcomes of kidney disease. BACKGROUND:Gender disparities in the incidence and complications of kidney diseases are well described. However, analysis to elucidate gender disparities in enrollment in nephrology randomized clinical trials (RCTs) has not been performed. METHODS:) kidney transplantation. We summarized trial characteristics according to reporting and enrollment of participants, enrollment site, publication year, trial category, and intervention type. Outcomes of interest include the proportion of enrolled male and female participants overall and according to trial category. In addition, we compared enrollment trends in the United States and globally to estimates of kidney disease prevalence. RESULTS:=133,082). Male participants formed most of trial cohorts in AKI (65%), CKD (62%), dialysis (55%), and transplant trials (65%), whereas women were majority enrollees in GN trials (61%). CKD trials under-represented women in both US trials and worldwide. CONCLUSIONS:Women are under-represented in high-impact nephrology trials with the exception of GN trials. This imbalance may contribute to disparities in outcomes and gaps in the care of women with kidney disease.

SIGNIFICANCE STATEMENT:Racial and ethnic disparities in clinical trial enrollment are well described. However, whether these disparities are present in nephrology randomized clinical trials has not been previously reported. We performed a systematic review and meta-analysis of 380 randomized clinical trials involving different aspects of kidney disease published between 2000 and 2021. Our results indicate that worldwide reporting of race and ethnicity is poor and that White individuals account for most of the randomized participants with decreased enrollment of Black participants in more recent trials. However, trials conducted in the United States have representation of Black and Hispanic participants consistent with the population prevalence of disease and under-representation of Asian participants. BACKGROUND:Under-representation of racial and ethnic minorities in clinical trials could worsen disparities, but reporting and enrollment practices in nephrology randomized clinical trials have not been described. METHODS:PubMed was searched to capture randomized clinical trials for five kidney disease-related conditions published between 2000 and 2021 in ten high-impact journals. We excluded trials with <50 participants and pilot trials. Outcomes of interest were the proportion of trials reporting race and ethnicity and the proportions of enrolled participants in each race and ethnicity category. RESULTS:Among 380 trials worldwide, race was reported in just over half and ethnicity in 12%. Most enrolled participants were White, and Black individuals accounted for ≤10% of participants except in dialysis trials where they accounted for 26% of participants. However, Black participants were enrolled at high proportions relative to disease and population prevalence in US CKD, dialysis, and transplant trials representing 19% of participants in AKI, 26% in CKD, 44% in GN, 40% in dialysis, and 26% in transplant trials. Enrollment of Asian participants was low worldwide except in GN trials with marked under-representation in US CKD, dialysis, and transplant trials. Hispanic individuals represented only 13% of participants in US dialysis trials compared with 29% of US dialysis population. CONCLUSION:More complete reporting of race and ethnicity in nephrology trials is needed. Black and Hispanic patients are well-represented in kidney disease trials in the United States. Asian patients are poorly represented in kidney trials both globally and in the United States.

Cardiovascular morbidity and mortality occur with an extraordinarily high incidence in the hemodialysis-dependent end-stage kidney disease population. There is a clear need to improve identification of those individuals at the highest risk of cardiovascular complications in order to better target them for preventative therapies. Twelve-lead electrocardiograms are ubiquitous and use inexpensive technology that can be administered with minimal inconvenience to patients and at a minimal burden to care providers. The embedded waveforms encode significant information on the cardiovascular structure and function that might be unlocked and used to identify at-risk individuals with the use of artificial intelligence techniques like deep learning. In this review, we discuss the experience with deep learning-based analysis of electrocardiograms to identify cardiovascular abnormalities or risk and the potential to extend this to the setting of dialysis-dependent end-stage kidney disease.