Browsing by Author "Baart, Sara (56592995900)"

Renal dysfunction is an important component of chronic heart failure (CHF), but its single assessment does not sufficiently reflect clinically silent progression of CHF prior to adverse clinical outcome. Therefore, we aimed to investigate temporal evolutions of glomerular and tubular markers in 263 stable patients with CHF, and to determine if their patient-specific evolutions during this clinically silent period can dynamically predict clinical outcome. We determined the risk of clinical outcome (composite endpoint of Heart Failure hospitalization, cardiac death, Left Ventricular Assist Device placement, and heart transplantation) in relation to marker levels, slopes and areas under their trajectories. In each patient, the trajectories were estimated using repeatedly measured glomerular markers: creatinine/estimated glomerular filtration rate (eGFR), cystatin C (CysC), and tubular markers: urinary N-acetyl-beta-D-glucosaminidase (NAG) and kidney injury molecule (KIM)-1, plasma and urinary neutrophil gelatinase-associated lipocalin (NGAL). During 2.2 years of follow-up, we collected on average 8 urine and 9 plasma samples per patient. All glomerular markers predicted the endpoint (univariable hazard ratio [95% confidence interval] per 20% increase: creatinine: 1.18[1.07–1.31], CysC: 2.41[1.81–3.41], and per 20% eGFR decrease: 1.13[1.05–1.23]). Tubular markers, NAG, and KIM-1 also predicted the endpoint (NAG: 1.06[1.01–1.11] and KIM-1: 1.08[1.04–1.11]). Larger slopes were the strongest predictors (creatinine: 1.57[1.39–1.84], CysC: 1.76[1.52–2.09], eGFR: 1.59[1.37–1.90], NAG: 1.26[1.11–1.44], and KIM-1: 1.64[1.38–2.05]). Associations persisted after multivariable adjustment for clinical characteristics. Thus, during clinically silent progression of CHF, glomerular and tubular functions deteriorate, but not simultaneously. Hence, patient-specific evolutions of these renal markers dynamically predict clinical outcome in patients with CHF. © 2017 International Society of Nephrology

In nephrology, repeated measures are frequently available (glomerular filtration rate or proteinuria) and linked to adverse outcomes. However, several features of these longitudinal data should be considered before making such inferences. These considerations are discussed, and we describe how joint modeling of repeatedly measured and time-to-event data may help to assess disease dynamics and to derive personalized prognosis. Joint modeling combines linear mixed-effects models and Cox regression model to relate patient-specific trajectory to their prognosis. We describe several aspects of the relationship between time-varying markers and the endpoint of interest that are assessed with real examples to illustrate the aforementioned aspects of the longitudinal data provided. Thus, joint models are valuable statistical tools for study purposes but also may help health care providers in making well-informed dynamic medical decisions. © 2018 International Society of Nephrology