Background Few studies have examined the association between the early diastolic mitral inflow velocity/early diastolic mitral annulus velocity ratio (E/e’) and chronic kidney disease progression. Methods and Results We reviewed data from 2238 patients with nondialysis chronic kidney disease from the KNOW‐CKD (Korean Cohort Study for Outcome in Patients With Chronic Kidney Disease); data from 163 patients were excluded because of missing content. A >50% decrease in estimated glomerular filtration rate from baseline, doubling of serum creatinine, or dialysis initiation and/or kidney transplantation were considered renal events. At baseline, median (interquartile range) ejection fraction and E/e’ were 64.0% (60.0%–68.0%) and 9.1 (7.4–11.9), respectively. Proportions of ejection fraction <50% and E/e’ ≥15 were 1.3% and 9.6%, respectively. More than one quarter of patients (27.2%) had an estimated glomerular filtration rate <30 mL/min per 1.73 m 2 . During the mean 59.1‐month follow‐up period, 724 patients (34.9%) experienced renal events. In multivariable Cox proportional hazard regression analysis, the hazard ratio with 95% CI per 1‐unit increase in E/e’ was 1.027 (1.005–1.050; P =0.016). Penalized spline curve analysis yielded a suggested threshold of E/e’ for renal events of 12; in our data set, the proportion of E/e’ ≥12 was 4.1%. Conclusions Increased E/e’ was associated with an increased hazard of renal events, suggesting that diastolic heart dysfunction is a novel risk factor for chronic kidney disease progression.
Background: Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods: This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results: We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion: This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.
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