Left-sided heart disease and risk of death in patients with end-stage kidney disease receiving haemodialysis: an observational study

Hemodialysis and biomarkers of myocardial infarction - a cohort study

OBJECTIVES

End-stage renal disease is associated with a high risk of cardiovascular disease. We compared the concentration and prognostic ability of high sensitivity cardiac troponin T (hs-cTnT) and I (hs-cTnI) and cardiac myosin-binding protein C (cMyC) among stable hemodialysis patients.

METHODS

Patients were sampled before and after hemodialysis. We measured hs-cTnI, hs-cTnT and cMyC and used Cox regressions to assess the association between quartiles of concentrations and all-cause mortality and a combination of cardiovascular events and all-cause mortality during follow-up.

RESULTS

A total of 307 patients were included, 204 males, mean age 66 years (SD 14). Before dialysis, 299 (99 %) had a hs-cTnT concentration above the 99th percentile, compared to 188 (66 %) for cMyC and 35 (11 %) for hs-cTnI. Hs-cTnT (23 %, p<0.001) and hs-cTnI (15 %, p=0.049) but not cMyC (4 %, p=0.256) decreased during dialysis. Follow-up was a median of 924 days (492-957 days); patients in the 3rd and 4th quartiles of hs-cTnT (3rd:HR 3.0, 95 % CI 1.5-5.8, 4th:5.2, 2.7-9.8) and the 4th quartile of hs-cTnI (HR 3.8, 2.2-6.8) had an increased risk of mortality. Both were associated with an increased risk of the combined endpoint for patients in the 3rd and 4th quartiles. cMyC concentrations were not associated with risk of mortality or cardiovascular event.

CONCLUSIONS

Hs-cTnT was above the 99th percentile in almost all patients. This was less frequent for hs-cTnI and cMyC. High cTn levels were associated with a 3-5-fold higher mortality. This association was not present for cMyC. These findings are important for management of hemodialysis patients.

Non-A Blood Type Is a Risk Factor for Poor Cardio-Cerebrovascular Outcomes in Patients Undergoing Dialysis

The clinical impact of ABO blood type on cardio-cerebrovascular outcomes in patients undergoing dialysis has not been clarified. A total of 365 hemodialysis patients participated in the current study. The primary endpoint was defined as a composite including cardio-cerebrovascular events and cardio-cerebrovascular death. The primary endpoint was observed in 73 patients during a median follow-up period of 1182 days, including 16/149 (11%) with blood type A, 22/81 (27%) with blood type B, 26/99 (26%) with blood type O, and 9/36 (25%) with blood type AB. At baseline, no difference was found in the echocardiographic parameters. Multivariable Cox regression analyses revealed that blood type (type A vs. non-A type; hazard ratio (HR): 0.46, 95% confidence interval (95% CI): 0.26-0.81, p = 0.007), age (per 10-year increase; HR: 1.47, 95% CI: 1.18-1.84), antiplatelet or anticoagulation therapy (HR: 1.91, 95% CI: 1.07-3.41), LVEF (per 10% increase; HR: 0.78, 95% CI: 0.63-0.96), and LV mass index (per 10 g/m² increase; HR: 1.07, 95% CI: 1.01-1.13) were the independent determinants of the primary endpoint. Kaplan-Meier curves also showed a higher incidence of the primary endpoint in the non-A type than type A (Log-rank p = 0.001). Dialysis patients with blood type A developed cardio-cerebrovascular events more frequently than non-A type patients.

Cardiac Imaging and Management of Cardiac Disease in Asymptomatic Renal Transplant Candidates: A Current Update

Given the high cardiovascular risk accompanying end-stage kidney disease, it would be of paramount importance for the clinical nephrologist to know which screening method(s) identify high-risk patients and whether screening asymptomatic transplant candidates effectively reduces cardiovascular risk in the perioperative setting as well as in the longer term. Within this review, key studies concerning the above questions are reported and critically analyzed. The lack of unified screening criteria and of a prognostically sufficient screening cardiovascular effect for renal transplant candidates sets the foundation for a personalized patient approach in the near future and highlights the need for well-designed studies to produce robust evidence which will address the above questions.

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis

BACKGROUND AND OBJECTIVES

Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve.

RESULTS

We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59).

CONCLUSION

A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.

Hypoalbuminaemia and One-Year Mortality in Haemodialysis Patients with Heart Failure: A Cohort Analysis

INTRODUCTION

The prevalence of chronic kidney disease (CKD) and heart failure (HF) has been rising over the past decade, with a prevalence close to 40%. Cardiovascular disease and malnutrition are common comorbidities and known risk factors for mortality in haemodialysis (HD) patients. We aimed to evaluate the one-year mortality rate after dialysis induction, and the impact of serum albumin levels on survival outcomes, in patients with CKD and HF.

METHODS

This was a retrospective analysis of patients with CKD and HF who underwent chronic HD between January 2016 and December 2019 in a tertiary-care Portuguese hospital. Variables were submitted to univariate and multivariate analysis to determine factors predictive of one-mortality after HD start.

RESULTS

In total, 204 patients were analysed (mean age 75.1 ± 10.3 years). Within the first year of HD start, 28.7% of patients died. These patients were significantly older [79.8 ± 7.2 versus 72.9 ± 10.9 years, p < 0.001; OR 1.08 (1.04-1.13), p < 0.001] and had a higher mean Charlson Index [9.0 ± 1.8 versus 8.3 ± 2.0, p = 0.015; OR 1.22 (1.04-1.44), p = 0.017], lower serum creatinine [5.1 ± 1.6 mg/dL versus 5.8 ± 2.0 mg/dL; p = 0.021; OR 0.80 (0.65-0.97), p = 0.022], lower albumin levels [3.1 ± 0.6 g/dL versus 3.4 ± 0.6 g/dL, p < 0.001; OR 0.38 (0.22-0.66), p = 0.001] and started haemodialysis with a central venous catheter more frequently [80.4% versus 66.2%, p = 0.050]. Multivariate analysis identified older age [aOR 1.07 (1.03-1.12), p = 0.002], lower serum creatinine [aOR 0.80 (0.64-0.99), p = 0.049] and lower serum albumin [aOR 0.41 (0.22-0.75), p = 0.004] as predictors of one-year mortality.

CONCLUSION

In our cohort, older age, lower serum creatinine and lower serum albumin were independent risk factors for one-year mortality, highlighting the prognostic importance of malnutrition in patients starting chronic HD.

Serum uric acid is associated with coronary artery calcification in early chronic kidney disease: a cross-sectional study

Background

Although uric acid (UA) is regarded as a risk factor for cardiovascular disease, whether UA is an independent risk factor contributing to coronary artery calcification in chronic kidney disease (CKD) is not well known. We evaluated whether UA level is associated with coronary artery calcium (CAC) score in a predialysis CKD cohort.

Methods

A total of 1,350 subjects who underwent coronary computed tomography as part of the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease were analysed. We conducted a logistic regression analysis to evaluate the association between UA and the presence of CAC.

Results

CAC was detected in 705 (52.2 %) patients, and the level of UA was significantly higher in CAC > 0 patients. UA showed a positive relationship with CAC > 0 in age- and sex-adjusted logistic regression analysis (Odds ratio (OR) 1.11, 95 % confidence interval (CI) 1.04–1.19, P = 0.003). However, UA showed no association with CAC > 0 in multivariate analysis. Further analysis showed that UA showed a positive association with CAC > 0 only in estimated glomerual filtration rate (eGFR) > 60 ml/min/1.73 m² (OR 1.23, 95 % CI 1.02–1.49, P = 0.036) but not in eGFR 30–59 ml/min/1.73 m² (OR 0.92, 95 % CI 0.78–1.08, P = 0.309) or < 30 ml/min/1.73 m² (OR 0.92, 95 % CI 0.79–1.08, P = 0.426).

Conclusions

UA level was significantly associated with CAC in early CKD, but not in advanced CKD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02463-2.