Elevated resting heart rate is associated with mortality in patients with chronic kidney disease.

A higher heart rate is recognized as an independent risk factor for all-cause mortality and cardiovascular events in the general population. However, the association between elevated heart rate and clinical adverse outcomes in patients with non-dialysis-dependent chronic kidney disease (CKD) has not been sufficiently investigated. A total of 1353 participants enrolled in the Fukushima CKD Cohort Study were examined to investigate associations between resting heart rate and clinical adverse outcomes using Cox proportional hazards analysis. The primary outcome of the present study was all-cause mortality, with cardiovascular events as the secondary outcome. Participants were stratified into four groups based on resting heart rate levels at baseline (heart rate < 70/min, ≥ 70 and < 80/min, ≥ 80 and < 90/min, and ≥ 90/min). During the median observation period of 4.9 years, 123 participants died, and 163 cardiovascular events occurred. Compared with the reference level heart rate < 70/min group, the adjusted hazard ratios (HRs) for all-cause mortality were 1.74 (1.05-2.89) and 2.61 (1.59-4.29) for the heart rate ≥ 80 and < 90/min group and heart rate ≥ 90/min group, respectively. A significantly higher risk of cardiovascular events was observed in the heart rate ≥ 80/min and < 90/min group (adjusted HR 1.70, 1.10-2.62), but not in the heart rate ≥ 90/min group (adjusted HR 1.45, 0.90-2.34). In patients with non-dialysis-dependent CKD, a higher resting heart rate was associated with increased all-cause mortality.

Geriatric Nutritional Risk Index is associated with adverse outcomes in patients with hypertension: the Fukushima Cohort study.

Malnutrition is reportedly associated with adverse clinical outcomes in various populations. However, associations between nutritional status and adverse outcomes in patients with hypertension have not been sufficiently elucidated. We therefore aimed to investigate the impact of nutritional status as evaluated by the Geriatric Nutritional Risk Index (GNRI) on adverse outcomes in patients with hypertension. We conducted a retrospective cohort study of 1588 hypertensive patients enrolled in the Fukushima Cohort Study. Participants were categorized into tertiles (T1-T3) according to GNRI at baseline. The primary endpoint of the present study was a kidney event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease requiring kidney replacement therapy. Associations between GNRI and kidney events were assessed using Kaplan-Meier curves and multivariate Cox regression analyses. Median age was 64 years, 55% were men, median eGFR was 63.1 mL/min/1.73 m2, and median GNRI was 101.3. The lower GNRI group (T1) showed an increased incidence of kidney events in the Kaplan-Meier curve analysis. Compared to the highest GNRI group (T3), lower GNRI carried a higher risk of kidney events for both T2 (hazard ratio [HR] 1.38, 95% confidence interval [CI] 0.71-2.68) and T1 (HR 3.59, 95%CI 1.96-6.63). Similar relationships were observed for risks of all-cause death and cardiovascular events. Lower GNRI was associated with kidney events, all-cause death, and cardiovascular events in patients with hypertension. Nutritional status as evaluated by GNRI could offer a simple and useful predictor of adverse outcomes in this population.

Predicting CKD progression using time-series clustering and light gradient boosting machines.

Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m2. Based on 5-year changes in estimated GFR, participants were stratified into groups with similar trajectories by cluster analysis. Next, we applied the light gradient boosting machine algorithm and Shapley addictive explanation to develop a prediction model for clusters and identify important parameters for prediction. Data from 780 participants were available for analysis. Participants were classified into five classes (Class 1: n = 78, mean [± standard deviation] estimated GFR 100 ± 19.3 mL/min/1.73 m2; Class 2: n = 176, 76.0 ± 9.3 mL/min/1.73 m2; Class 3: n = 191, 59.8 ± 5.9 mL/min/1.73 m2; Class 4: n = 261, 52.7 ± 4.6 mL/min/1.73 m2; and Class 5: n = 74, 53.5 ± 12.0 mL/min/1.73 m2). Declines in estimated GFR were 8.9% in Class 1, 12.2% in Class 2, 4.9% in Class 3, 12.0% in Class 4, and 45.1% in Class 5 during the 5-year period. The accuracy of prediction was 0.675, and the top three most important Shapley addictive explanation values were 1.61 for baseline estimated GFR, 0.12 for hemoglobin, and 0.11 for body mass index. The estimated GFR transition of patients with preserved chronic kidney disease mostly depended on baseline estimated GFR, and the borderline for estimated GFR trajectory was nearly 50 mL/min/1.73 m2.