Sandys Vicki, Edwards Colin, McAleese Paul, O'Hare Emer, O'Seaghdha Conall
Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Ireland.
patientMpower Ltd., 21 Denzille Lane, Dublin, D02 EY19, Ireland.
Pilot Feasibility Stud. 2022 Jan 24;8(1):17. doi: 10.1186/s40814-022-00976-7.
Fluid overload has a high prevalence in haemodialysis patients and is an important risk factor for excess mortality and hospitalisations. Despite the risks associated with chronic fluid overload, it is clinically difficult to assess and maintain fluid status adequately. Current methods of fluid status assessment are either imprecise or time intensive. In particular, to date, no method exists to accurately assess fluid status during the interdialytic interval.
This pilot study aimed to evaluate whether a prototype wearable hydration monitor can accurately and reproducibly detect fluid overload in the haemodialysis population when compared to haemodialysis and bioimpedance data.
A prospective, open-label, single-arm observational trial of 20 patients commenced in January 2021 in a single haemodialysis centre in Ireland, with a wearable hydration monitor, the Sixty device. The Sixty device uses diffuse reflectance spectroscopy to measure fluid levels at the level of the subdermis and uses machine learning to develop an algorithm that can determine fluid status. The Sixty device was worn at every dialysis session and nocturnally over a three-week observational period. Haemodialysis parameters including interdialytic weight gain, ultrafiltration volume, blood pressure, and relative blood volume were collected from each session, and bioimpedance measurements using the Fresenius body composition monitor were performed on 4 occasions as a comparator. The primary objective of this trial was to determine the accuracy and reproducibility of the Sixty device compared to bioimpedance measurements.
If the accuracy of the wearable hydration monitor is validated, further studies will be conducted to integrate the device output into a multi-parameter machine learning algorithm that can provide patients with actionable insights to manage fluid overload in the interdialytic period.
www.clinicaltrials.gov NCT04623281 . Registered November 10th, 2020.
液体过载在血液透析患者中普遍存在,是导致额外死亡和住院的重要风险因素。尽管存在与慢性液体过载相关的风险,但临床上难以充分评估和维持液体状态。目前的液体状态评估方法要么不精确,要么耗时较长。特别是,迄今为止,尚无方法能够准确评估透析间期的液体状态。
这项前瞻性研究旨在评估一款原型可穿戴式水合监测仪与血液透析及生物电阻抗数据相比,能否准确且可重复地检测血液透析人群中的液体过载情况。
2021年1月,在爱尔兰的一个血液透析中心,对20名患者开展了一项前瞻性、开放标签、单臂观察性试验,使用一款可穿戴式水合监测仪Sixty设备。Sixty设备利用漫反射光谱法测量皮下组织的液体水平,并使用机器学习开发一种可确定液体状态的算法。在为期三周的观察期内,每次透析时以及夜间均佩戴Sixty设备。每次收集血液透析参数,包括透析间期体重增加、超滤量、血压和相对血容量,并使用费森尤斯人体成分监测仪进行4次生物电阻抗测量作为对照。该试验的主要目的是确定Sixty设备与生物电阻抗测量相比的准确性和可重复性。
如果可穿戴式水合监测仪的准确性得到验证,将开展进一步研究,将该设备的输出整合到一个多参数机器学习算法中,为患者提供可操作的见解,以管理透析间期的液体过载情况。
www.clinicaltrials.gov NCT编号04623281。于2020年11月10日注册。