Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study

Sex and the Risk of Atheromatous and Non-Atheromatous Cardiovascular Disease in CKD: Findings From the CKD-REIN Cohort Study

RATIONALE & OBJECTIVE

Sex differences in cardiovascular disease (CVD) are well-established, but whether chronic kidney disease (CKD) modifies these risk differences, and whether they differ between atheromatous (ACVD) and non-atheromatous (N-ACVD) CVD is unknown. Assessing this interaction was the principal goal of this study.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

Adults enrolled in the CKD-Renal Epidemiology and Information Network (CKD-REIN) cohort from from 2013 to 2020, a nationally representative sample of 40 nephrology clinics in France.

EXPOSURE

Sex.

OUTCOMES

Fatal and non-fatal composite ACVD events (ischaemic coronary, cerebral, and peripheral artery disease) and composite N-ACVD events (heart failure, haemorrhagic stroke, and arrhythmias).

ANALYTICAL APPROACH

Multivariable cause-specific Cox proportional hazards models.

RESULTS

1,044 women and 1,976 men with moderate to severe CKD (median age, 67 vs. 69; mean estimated glomerular filtration rate [eGFR], 32±12 vs. 33±12 mL/min/1.73m2) were studied. Over a median follow-up of 5.0 (interquartile range, 4.8;5.2) years, the ACVD rate (per 100 patient-years) was significantly lower in women than men: 2.1 (95% confidence interval: 1.6-2.5) vs 3.6 (3.2-4.0) (P<0.01), while the N-ACVD rate was not: 5.7 (5.0-6.5) vs 6.4 (5.8-7.0) (P=0.55). N-ACVD had a steeper relationship with eGFR than did ACVD. There was an interaction (P<0.01) between sex and baseline eGFR and the ACVD hazard: the adjusted hazard ratio for women compared to men was 0.42 (0.25;0.71) at 45 mL/min/1.73m2 and gradually attenuated at lower levels of eGFR, reaching 1.00 (0.62;1.63) at 16 mL/min/1.73m2. In contrast, the N-ACVD hazard did not differ between the sexes across the eGFR range studied.

LIMITATIONS

Cardiovascular biomarkers and sex hormones were not assessed.

CONCLUSION

This study shows how the lower risk of ACVD among women compared to men attenuates fully with kidney disease progression. The equal risk of N-ACVD between sexes across CKD stages and its steeper association with eGFR suggest an important contribution of CKD to the development of this CVD type.

A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

Introduction

This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD).

Methods

Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics.

Results

The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897-0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633-0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho.

Conclusion

We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease

Introduction

Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis.

Methods

Electronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records.

Results

This study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance.

Conclusion

This study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

New Insights on Cardiac Arrhythmias in Patients With Kidney Disease

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.

Dialysis Modality, Transplant Characteristics, and Incident Atrial Fibrillation After Kidney Transplant: An Observational Study Using USRDS Data

Rationale & Objective

Atrial fibrillation is the most common arrhythmia and is increasing in prevalence. The prevalence of atrial fibrillation is high among patients receiving dialysis, affecting ∼21.3% of the patients receiving hemodialysis and 15.5% of those receiving peritoneal dialysis. The association of previous dialysis modality with incident atrial fibrillation in patients after receiving their first kidney transplant has not been studied.

Study Design

We used the United States Renal Data System to retrospectively identify adult, Medicare-insured patients who received their first kidney transplant between January 1, 2005, and September 30, 2012 and who had not previously been diagnosed with atrial fibrillation.

Setting & Participants

The study included 43,621 patients who were aged 18 years older when receiving a first kidney transplant between January 1, 2005, and September 30, 2012 and whose primary payer was Medicare (parts A and B) at the time of transplantation and the 6 months preceding it.

Exposure

Dialysis modality used before transplant.

Outcome

Time to incidence of atrial fibrillation up to 3 years posttransplant.

Analytical Approach

Multivariable Cox regression was used to estimate HRs.

Results

Of 43,621 patients, 84.9% received hemodialysis and 15.1% received peritoneal dialysis before transplant. The mean ± SD age was 51 ± 13.6 years; 60.8% were male, 55.6% White, and 35.8% Black race. The mean dialysis vintage was 4.3 ± 2.8 years. Newly diagnosed atrial fibrillation after kidney transplant occurred in 286 patients (during 15,363 person-years) who had received peritoneal dialysis and in 2,315 patients (during 83,536 person-years) who had received hemodialysis. After multivariable adjustment, atrial fibrillation was 20% (95% CI, 4%-38%) more likely in those who had been receiving hemodialysis versus peritoneal dialysis, regardless of whether death was considered a competing risk or a censoring event. Each year of pretransplant dialysis vintage increased the risk of posttransplant atrial fibrillation by 6% (95% CI, 3%-9%).

Limitations

Residual confounding; data from billing claims does not specify the duration of atrial fibrillation or whether it is valvular.

Conclusions

Pretransplant hemodialysis, as compared with peritoneal dialysis, was associated with higher risk of newly diagnosed atrial fibrillation after a first kidney transplant.

Plain-Language Summary

New-onset atrial fibrillation (AF) occurs in 7% of kidney transplant recipients in the first 3 years posttransplantation. We conducted this study to determine whether pretransplant dialysis modality was associated with posttransplant AF. We identified 43,621 patients; 84.9% used hemodialysis and 15.1% used peritoneal dialysis pretransplant. Multivariable Cox regression was used to estimate hazard ratios. We found that patients receiving hemodialysis pretransplant were at 20% increased risk of developing posttransplant AF as compared with patients receiving peritoneal dialysis. As our understanding of transplant-specific risk factors for AF increases, we may be able to better risk-stratify transplant patients and develop monitoring and management strategies that can improve outcomes.

One Heartbeat Away from a Prediction Model for Cardiovascular Diseases in Patients with Chronic Kidney Disease: A Systematic Review

INTRODUCTION

Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate. In this review, we aim to give an overview of CVD prognostic studies available, and their methodological quality, specifically for patients with CKD.

METHODS

MEDLINE was searched for papers reporting CVD prognostic studies in patients with CKD published between 2012 and 2021. Characteristics regarding patients, study design, outcome measurement, and prediction models were compared between included studies. The risk of bias of studies reporting on prognostic factors or the development/validation of a prediction model was assessed with, respectively, the QUIPS and PROBAST tool.

RESULTS

In total, 134 studies were included, of which 123 studies tested the incremental value of one or more predictors to existing models or common risk factors, while only 11 studies reported on the development or validation of a prediction model. Substantial heterogeneity in cohort and study characteristics, such as sample size, event rate, and definition of outcome measurements, was observed across studies. The most common predictors were age (87%), sex (75%), diabetes (70%), and estimated glomerular filtration rate (69%). Most of the studies on prognostic factors have methodological shortcomings, mostly due to a lack of reporting on clinical and methodological information. Of the 11 studies on prediction models, six developed and internally validated a model and four externally validated existing or developed models. Only one study on prognostic models showed a low risk of bias and high applicability.

CONCLUSION

A large quantity of prognostic studies has been published, yet their usefulness remains unclear due to incomplete presentation, and lack of external validation of prognostic models. Our review can be used to select the most appropriate prognostic model depending on the patient population, outcome, and risk of bias. Future collaborative efforts should aim at improving existing models by externally validating them, evaluating the addition of new predictors, and assessment of the clinical impact.

REGISTRATION

We have registered the protocol of our systematic review on PROSPERO (CRD42021228043).

Finerenone

In developed countries, diabetes mellitus (DM) is one of the main causes of end stage renal disease (ESRD). In addition, the development of chronic kidney disease (CKD) further increases the already significantly increased cardiovascular (CV) risk in patients with diabetes. Both albuminuria and impaired renal function predict CV disease-related morbidity. The multifactorial pathogenesis of DM-related CKD involves structural, physiological, hemodynamic, and inflammatory processes. Instead of a so-called glucocentric approach, current evidence suggests that a multimodal, interdisciplinary treatment approach is needed to also prevent further progression of CKD and reduce the risk of cardiovascular events. Combined antihypertensive, antihyperglycemic and hypolipidemic therapy is the basis of a comprehensive approach to prevent the progression of diabetic kidney disease. According to recent evidence, adjunctive therapy with the non-steroidal mineralocorticoid receptor antagonist (MRA) finerenone - in addition to the use of an ACE (angiotensin converting enzyme) or AT1 (angiotensin II receptor subtype 1) blocker and an SGLT2 (sodium-glucose cotransporter-2) inhibitor - represents an effective therapeutic tool to improve nephroprotection in CKD. The aim of this review is to provide brief information on this promising pharmacotherapeutic approach to the treatment of diabetic kidney disease.

Comparison of Diagnostic Value for Chronic Kidney Disease between 640-Slice Computed Tomography Kidney Scan and Conventional Computed Tomography Scan

Objective

To explore the diagnostic value for chronic kidney disease (CKD) between 640-slice computed tomography (CT) kidney scan and conventional CT scan.

Methods

A total of 120 CKD patients who received kidney plain scan plus enhanced examination in the CT room of the Medical Imaging Department of our hospital from June 2019 to September 2019 were selected and randomly divided into the experimental group (n = 60) and the control group (n = 60). Patients in the control group received the conventional CT plain scan and enhanced scan, and for patients in the experimental group, CT plain scan was performed first, the range of 640-slice CT dynamic volume scan was determined, and after bolus injection of contrast agent, dynamic volume scan was performed for scanning in the cortical phase, myeloid phase, and secretory phase. The imaging quality and effective scanning dose were compared between the two modalities, and the relationship between CT values obtained from 640-slice CT scan and conventional CT scan and the renal impairment was analyzed.

Results

Compared with the control group, the image quality of 640-slice CT scan conducted in the experimental group was significantly better (P < 0.05); the effective radiation doses of the experimental group and the control group were, respectively, (1.89 ± 0.32) mSv and (3.26 ± 0.47) mSv, indicating that the dose was significantly lower in the experimental group than in the control group (t = 18.664, P < 0.001), and the correlation analysis showed that the relationship between the sum of CT values in the cortical phase of both kidneys and kidney injury in the experimental group was r = 0.835, P < 0.001.

Conclusion

Both 640-slice CT kidney scan and conventional CT scan can be used in the diagnosis of CKD. 640-slice CT has a lower radiation dose, better image quality, and higher application value.

Machine learning in the detection and management of atrial fibrillation

Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls.