血液透析
医学
重症监护医学
透析
血容量
药方
前瞻性队列研究
临床终点
体质指数
超声波
心脏病学
内科学
临床试验
放射科
药理学
作者
Manfred Hecking,Magdalena Madero,Friedrich K. Port,Daniel Schneditz,Peter Wabel,Charles Chazot
标识
DOI:10.1016/j.kint.2022.09.021
摘要
Every hemodialysis session starts with the question of how much fluid should be removed, which can currently not be answered precisely. Herein, we first revisit the "probing-dry-weight" concept, using the historical example of Tassin/France (practicing also "long, slow dialysis"): Mortality outcomes were, in the 1980s, better than registry data, but are nowadays similar to European average. In view of the negative primary end point in a recent trial on dry weight assessment, based on lung ultrasound-guided evaluation of fluid excess in the lungs, and a meta-analysis of prospective studies failing to show that bioimpedance-based interventions for correction of volume overload had a direct effect on all-cause mortality, we ask how to ever move forward. Clinical reasoning demands that as much information as possible should be gathered on the fluid status of patients undergoing dialysis. Besides body weight and blood pressure, measurements of bioimpedance and dialysate bolus-derived absolute blood volume can in principle be automatized, whereas lung ultrasound can be obtained routinely. In the era of machine learning, fluid management could consist of flexible target weight prescriptions, adjusted on a daily basis and accounting even for fluctuations in fluid-free body mass. In view of all the negative prospective results surrounding fluid management in hemodialysis, we propose this as a "never-give-up" approach.
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