Left Ventricular Diastolic Dysfunction and Progression of Chronic Kidney Disease: Analysis of KNOW-CKD Data.
Eunjeong KangSung Woo LeeHyunjin RyuMinjung KangSeonmi KimSue-Kyung ParkJi Yong JungKyu-Beck LeeSeung-Hyeok HanCurie AhnKook-Hwan OhPublished in: Journal of the American Heart Association (2022)
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.
Keyphrases
- ejection fraction
- chronic kidney disease
- end stage renal disease
- aortic stenosis
- left ventricular
- peritoneal dialysis
- newly diagnosed
- mitral valve
- heart failure
- kidney transplantation
- blood pressure
- big data
- aortic valve
- prognostic factors
- machine learning
- patient reported outcomes
- blood flow
- deep learning
- coronary artery disease
- case control
- patient reported
- data analysis